This final chapter contains some key observations and insights that I wanted to share to conclude my thoughts on the wider responsibility you are undertaking in developing a data strategy and then executing it in your organisation, and some summary points that are here as reminders of some of the key points in earlier chapters.

12.1 CULTURE – IS YOUR STRATEGY HEADING FOR THE BREAKFAST PLATE?

I referred to culture at some length in Chapter 7. This is one of the most important factors to be aware of when embarking on any strategy effort, as it is probably the biggest challenge you will face and the hardest to articulate or embrace.

With the quote at the start of this chapter in mind, every organisation, in my experience, will have evolved a culture at its core that may not align with the corporate perspective of what the culture is thought to be. There may also be an external layer for wider consumption, particularly if there is an effective marketing operation that is building a brand and establishing what the brand stands for with its customers. Yet, this is different from an organisation’s culture, which pervades every part of the organisation and has taken time to evolve and determine the type of belief system which exists.

Culture is not something that can be read in a corporate document (though many organisations will claim to have values, beliefs and other concepts that articulate the culture as the corporate centre wants it to be seen). It is intangible and can be challenging to comprehend to those on the outside looking in. Much of it is unspoken, a series of behavioural norms which are engrained in the fabric of the organisation and drive attitudes of employees to one another, management, change programmes and any external (to the group, as well as the organisation) effort to drive change that may be resisted simply because it ‘isn’t the way we do things around here’.

Efforts have been made to characterise culture, with varying degrees of success. Charles Handy identified four principal types of culture,² based on power, role, task and person, but these are categories in their own right. They are a route into understanding culture, rather than snugly fitting descriptors. Johnson, Whittington and Scholes³ proposed a cultural web consisting of six interrelated elements in the work environment that feature in the organisational culture: stories; rituals and routines; symbols; organisational structure; control systems; and power structures. Together, they form the cultural paradigm and enable an assessment to be undertaken as to the existing culture in an organisation versus the one you would want to enable strategic goals to be achieved.

It is one thing to be able to assess culture – and even then this is subjective, based on trying to articulate and quantify the intangible – but quite another to change it. This requires the commitment of all parties to be willing to change, adopt different norms and become an organisation with a different belief set, and different values and behaviours. Many change programmes claim to have changed culture but are superficial, failing to supplant what was there before, and simply putting an additional veneer of corporate standards on top of it such that it drives the existing culture to be even more subversive. Do not be surprised to discover in some more traditional, long-standing organisations that some aspects of the culture date back to decisions taken 20 or more years ago, long before many of the existing workforce were there, based upon some form of perceived grievance that eroded trust and has been overlaid with a large dose of cynicism.

I would also add that any hint that you are seeking to change the culture will be unlikely to be well received. People like to have certainty, predictability and assurance, and if the culture is not to their liking, they will either leave or keep their head down to avoid rocking the boat. Of course, there are exceptions, particularly where strong characters can resist the pressures, challenges and tension that being at odds with other leaders brings, and they are comfortable being part of a small minority of like-minded individuals within the organisation.

Therefore, whilst there may be some who are willing to get behind your data strategy and see it through, many more will be in the shadows, either passively supportive or negative but not declaring themselves either way, to see how it copes with the onslaught of butting up against the culture. Inevitably a number will be vocal, critical, ready to explain why it will never work or that the change is not needed, or indeed the change is already under way so best to leave things alone (of course, there probably isn’t any tangible change; it may simply be said to drive you away). This is why picking the right sponsor, getting the appropriate level of ‘local’ buy-in, and developing a resilient and well-informed team around you is so essential.

Returning to the opening quote, those who say that there is no reason to change do so as part of the knowing culture, certain that the decisions they made are the best available and resisting anyone from outside their team telling them otherwise. The transition from knowing to learning requires a level of humility to accept being told there is a better way; moreover, professional pride, concern at being undermined and potentially surplus to requirements, and reluctance to be seen to back something new which could easily fail are all reasons why the default can so often be resistance.

Yet learning in different environments is something which we have made a fundamental part of our own development. From being a small child, through school and the transition to the workplace, we are constantly learning, absorbing as much as we can, developing our own individual culture of norms, beliefs, values and expectations shaped by those around us. The transition to the workplace is a novel experience for many, working in an environment totally alien to us where we learn our craft and take on many of the cues from those we work with closest and the leadership we experience.

All of these things define us, our vision of the future, aspirations, interests, even our trust settings to know what to expect from colleagues, managers and those corporate messages that resonate. For most of us who make decisions, from the immediate transactional to the career-orientated strategic, it is a case of weighing up what we feel is right at the time and trusting instinct – supported by largely what we choose to hear from those we trust around us – to make a decision. Yet, as Buluswar states, we are seeking to unshackle the conscious choice (some might go so far as to say expectation) from knowing to continuous learning, to open up the mind to evidence which states there is a better option out there, regardless of what your intuition built over many years through countless experiences and numerous discussions with trusted others has instilled in your mind as the right course of action.

The final point to make is that inertia in the face of change is often through a lack of understanding, which triggers a defensive response that materialises as resistance. Often, change can be uncomfortable, at least initially, due to changing practices or ways of approaching things, and the immediate impact may be more work, or things taking longer than was previously the case. All of these things build resistance. It needs the context – why make the change and what improves as a consequence – to give meaning to something that all can rally around. You need to be able to communicate this for the data strategy, providing context to win people over to achieve a common goal.

12.2 ARE YOU REALLY READY TO SAIL?

The quote at the start of this chapter highlights what, for many of you, will be the biggest challenge of all as you embark on defining and executing a data strategy in your organisation. I have navigated you through the challenges you will face, and aimed to prepare you for the breadth of experiences you will encounter as you plot a course and then set sail for the oceans with the crew on board the yacht. As with any analogy, it has limitations, but let me stick with the sailing theme a little longer.

Invariably, you would set sail with an end in mind, a distant port, perhaps, or a series of stopping-off points you are planning to make and have defined clearly in your mind. In preparation, you would plan a route, estimate time and make provisions accordingly, whilst recognising a need for contingency due to unforeseen things along the way and a need to be dynamic at all times. The data strategy is based on the conditions you observe, a distant goal to be achieved which aligns to the corporate strategy, and a series of waymarkers you have identified to get you there. You need a team around you to help define a data strategy with the quality of inputs from diverse parts of the organisation, as you do a crew with specific skillsets to ensure the yacht is shipshape and ready for the voyage, and an executive-level sponsor to help guide you, who in a major yachting race may well be the owner.

However, with the best planning in the world, once you set sail you are at the mercy of the weather, the yacht itself and the crew, not to mention the support staff on dry land who need to be in place to support as and when called upon. Your data strategy will follow similar vagaries in execution, being buffeted by the prevailing winds of other demands upon the organisation which could throw you off course or damage the sail itself. It will be exposed to scrutiny and challenge, in the same way the sun tests your crew, and it will face rough and choppy seas in deep water, which will prevail over your implementation at times where you find the organisation less than supportive in endorsing or enabling your programme to succeed.

Finally, the crew itself will show signs of weakness at times when under pressure, and at other times perform beyond your wildest expectations. Your implementation team will be no different, and you should prepare for emotional rollercoasters as these arise and anticipate that, at any time, a key person in your team could leave at short notice.

The reason I draw these parallels is that embarking on strategy definition and execution is one of the most uncertain things, in terms of an end-to-end activity, that you might ever embark upon. The window is long, much like a round-the-world yacht race, and the risks are great of failing to succeed.

I highlight this not to deter you from putting yourself into this position, but to ensure you are fully aware of the scale of the task you are undertaking. It is not for perfectionists, as you will have to make sacrifices along the way, face hard decisions regularly, feel isolated periodically and be operating in an environment in which the end product – the successes arising from the data strategy implementation – will be delivered by others. It is not for those who want to use this as a fast-track to bigger things; once you set sail you don’t get to finish till the race is over, unless you walk away or are replaced in the role.

So why on earth would anyone put themselves into this position? Personally, I find the challenge of turning an organisation that has not really ‘got’ data fully into one which has data first and foremost in its thinking one of the most appealing and challenging things anyone with a data and/or analytics skillset could be charged with doing.

Consider those organisations that haven’t ‘got’ it yet – to stick with the shipping theme – akin to the big cargo ships plying their trade around the world, stuck on a course from which they rarely deviate. It may surprise you that around 100 large ships are lost in an average year.⁴ The giants can find themselves in a position unable to deal with challenging circumstances, failing to look ahead or use evidence available to them in a strategic sense; they can often be sunk, despite being established operators in their markets. Take Blockbuster, Polaroid, Pan Am, General Motors and Kodak, for example, all synonymous with being market leaders at a point in time that found themselves focused on the wrong things and overtaken by those more alert to the shifting market and customer expectations.

Being at the helm of the ship that is ready for the weather conditions ahead, able to resource and commit to whatever comes its way to make it through to the end goal: that is your opportunity, steering a course which will shape the future of the organisation.

Can you be the opportunist in the market, stealing a march on much larger, more established players as Netflix did, having been rebuffed by Blockbuster when it offered itself for sale just a decade before Blockbuster’s collapse? Can you be the strategic leader who enables the organisation to reinvent itself and thus keep relevant, as IBM has done through a huge acquisition strategy to shift its focus just at a time when it was ailing and at risk of corporate failure? Are you able to make your business one which continuously evolves through innovation, and not only keeps pace with the changing market but is able to influence it, such as Apple, spotting the business opportunities and designing products which reinvent the standard customers expect?

The one thing these organisations had in common was a forward outlook to where the market and customer demand was heading, focusing on where the organisation needed to be rather than resting on its laurels and unable to respond dynamically enough when the weather changed and agility was needed. That is not to say that these organisations did so without taking risks, and there are plenty of organisations that have either a great idea but an inability to execute it or simply the right idea at the wrong time. Having evidence – data – to hand is essential to being one of these organisations. Without it you are gambling on gut feel (though there is an informative book on the laws of chance I can recommend if you so desire⁵), with a likelihood that luck will run out at some point. Even the largest organisations can fail if you do not listen, learn and act upon what the data is telling you today, leading you towards tomorrow and predicting for the months ahead.

12.3 REVOLUTION VERSUS EVOLUTION – THE IMPLEMENTATION CHALLENGE

The nature of the change that the data strategy is to drive will be determined by the appetite and commitment of the organisation to change. It will also be shaped by the maturity of the organisation, with the maturity assessment process having identified and demonstrated where the gaps lie, and the resolve of the organisation to set its own pace and objectives to be achieved by the time of the next assessment.

There are two distinct approaches to strategy implementation which have fundamentally different ways of being managed – revolution versus evolution. I do not mean these terms in the sense of ripping everything apart versus retaining the status quo, more in reflection of the pace of implementation and therefore its approach.

The revolutionary approach is based on making the data strategy implementation the top priority of the organisation, and putting resource and commitment behind it to drive change at a faster pace than would otherwise be the case. It is an approach which seeks to get to the end result in as short a time as feasible, whilst also recognising it has to pull the strings of other related activities for it to be successful.

By contrast, the evolutionary approach is at a slower pace, with the data strategy implementation being one of a number of programmes within the organisation or having to be delivered at a pace which is less disruptive to the business-as-usual activities. The implementation will therefore take longer, be managed in conjunction with other programmes and activities, and need to track dependencies, risks and issues as it goes and be cognisant that there may be other priority calls from time to time that could cause delay. By contrast, it does enable those involved with the implementation to learn and contribute more effectively over time, developing the skills needed to ensure the results of the data strategy implementation are likely to endure.

There is a lot to be said for evolution in most cases, simply because it recognises that the data strategy in itself is not likely to be at the very top of the agenda for most organisations, even if it is recognised that it is important, and therefore the pace of change will need to grow over time but start off with a less ambitious cadence to it.

If there is an opportunity to embark on a more revolutionary approach, it will demand more resource, commitment and focus by some magnitude, but will, in the long run, almost certainly consume far less resource and investment. Working around the organisation, its priorities, potentially a shifting focus over time and other communications that may actually hinder progress (for instance, expediency may necessitate making a problem worse in the short term, or at least halt progress, and so actively setting a direction later for it to be upturned will be harder) will absorb a lot more energy, resource and hence investment, and take far longer to achieve.

This is not to indicate that one approach is right: it is simply highlighting that the reality is most organisations are committed to evolution but at heart want progress to be more akin to revolution, which makes the task of implementation that much harder to achieve. It necessitates strong leadership in the implementation phase, keeping focus where it matters and ensuring those parts of the implementation programme which need to be maintained are not sacrificed to other priorities if at all possible.

The challenges of revolution and evolution are quite different, and so your task as the implementation lead will need to reflect the nature of the situation you are working to in the way you construct the programme and the tone of leadership you bring. It will also determine the approach you adopt in constructing your team, as an evolutionary approach involves working with and around other in-flight programmes and business-as-usual activities, whereas the revolutionary model is more driven and targeted, and hence has a greater focus on programme discipline to make other activities fit the programme.

If you have the opportunity to determine which approach to adopt – possibly a more likely scenario the smaller the organisation and the greater control you have over the implementation – then it would be wise to consider the benefits and potential pitfalls of each approach and assess which might be right for you. I have provided below a little more context on the scenarios in which revolution may be a more likely direction and the case for the evolutionary approach. Don’t forget, whichever you choose, the importance of retaining alignment to the corporate strategy.

12.3.1 The case for revolution

There are situations in which a revolutionary approach to the data strategy and its implementation will be the right one to adopt. Whilst either approach can work, the revolutionary approach really comes into its own in the following situations.

• Data-centric environments, where a lot of the activity is constructed around data and so there is a greater awareness of, and integration with, the ways of working within the organisation – such cases will gain more value from a more decisive and direct approach to change in this space, and there is likely to be a greater level of commitment to cooperating with the changes needed to enhance current operations. These types of organisation will typically operate in an online or at least web-enabled model where the challenges with data and its utilisation are at the forefront of driving improvements. The impact is therefore more readily visible, which in turn will lead to a greater expectation arising from the data strategy implementation.
• Acquisitions – often the acquisition of one organisation by another will have a significant dependency on systems and data integration. Any delay in addressing these just continues to carry inefficiencies, resulting in a higher cost base and eradicating value through management time devoted to reconciling two organisations trying to operate as one. The process of identifying the work required to make systems and data align would typically start before the acquisition is completed; hence the data strategy will almost certainly need redrafting at pace to enable the implementation to adapt seamlessly to the strategic priority of handling the acquisition. This can also be an opportunity to accelerate existing plans or goals in the data strategy within the acquiring organisation, as it may unlock a lot of value to move on this more swiftly, applying such plans or goals to both organisations rather than just one.
• Pace needed to address a compliance crisis – the situation discussed in the opening chapters, in which compliance has highlighted gaps in data being controlled and managed in a satisfactory way and needs an urgent response, is a good example of an opportunity to adopt a more revolutionary approach to address more than simply the compliance issue. I have known organisations make a case to invest to be several steps ahead in the regulatory space to ensure that the resolution presented demonstrates more than a ‘just enough’ response to a compliance issue. This presents the opportunity to adopt a more revolutionary approach to the data strategy implementation, with the teeth of compliance as a helpful means of enforcement if needed.
• Organisation in distress – the worst situation, in some respects, in which to manage anything strategic. However, even if it requires a tactically focused approach to keep an organisation afloat, the opportunity to get traction and pace into an implementation is clear in an operationally challenging situation. Of course, the benefits of a revolutionary approach need to deliver in the short term, but the focus such a situation brings helps to prioritise effort and remove blockers very effectively.
• Small organisations with a big appetite – often, the pace can be driven harder in a smaller organisation which has fewer lines of decision making and can therefore see the merit of investing to progress as swiftly as possible. The more the data strategy implementation is linked to customer outcomes and/or financial returns, the greater the likelihood of success. In such an environment, there is likely to be a drive to ‘get on with it’, and whilst that brings its own challenges it can be rewarding and refreshing to have that backing.

12.3.2 The evolutionary approach

The evolutionary approach is typical of most data strategy implementations. This is just as acceptable a way of proceeding with the data strategy implementation, as it can be executed alongside other changes which are managed through a dependency on other activity, thus reducing the scale of the programme to be managed directly by the implementation team.

The evolutionary approach also gives greater flexibility to the programme, enabling it to work around those areas which are not ready or need further investigation, as well as providing more scope to test and learn through the implementation.

On the other hand, the challenge of the evolutionary approach is that there is more effort to coordinate activity. Dependencies will need to be managed across activities and there may be shifts in priorities or rates of progress elsewhere that may delay the pace of the data strategy implementation programme. The evolutionary approach will also have a lot of moving parts to consider, and this in turn presents a significant communications effort to keep these aligned and the messages consistent. In delivering through an evolutionary approach, it is critical to keep a clear focus on the direction you are heading by remaining aligned with the implementation plan, which is key to mitigate any risk of tangents or other diversions.

Of course, the evolutionary approach should become easier, if it is implemented well. The understanding of the organisation can grow and embrace what is required, the impact of change is visible as you go and the plan can flex to recognise pressures on resource to deliver the right result for the organisation. Indeed, if the evolutionary approach is successful, you may even find that it builds momentum to increase the pace, such that it begins to look more revolutionary in its pace and impact.

It is important to adopt a test and learn philosophy through the evolutionary approach, being able to conduct assessments and reviews within the implementation programme of what has worked well, what has been learnt and how to feed this in to the planning for what comes next. This is another benefit of the evolutionary approach, as it builds confidence in both stakeholders and programme team members, demonstrating that each activity is conducted in a way that learnt from the last and showing an appreciation for the feedback provided by those involved.

12.4 THE TRICKY TRIUMVIRATE – PRIORITISATION, DEPENDENCIES AND CAPABILITIES

Three aspects of the data strategy implementation will be ever-present in the way you manage the programme – prioritisation, dependencies and capabilities. Whilst these are not entirely in your control in the delivery of the implementation programme, they are a high priority for you to manage closely as they will all play a part in how successful your implementation will be.

In addition, there is an interplay between them. Depending on the priorities, it will focus which dependencies are a priority to track closely, and the capabilities you need to deliver the priorities. Should the dependencies slip, or capabilities be unavailable, a reprioritisation of the deliverables might be needed to accommodate what can be achieved as opposed to what it is desirable to deliver. The sponsor will also play a key role, as any shift from the priorities will need to be communicated so that there is a discussion as to whether this can be avoided. This could be through putting further focus on the availability of the critical capabilities (in other words, can these be released to your programme to make it achievable?), or the area responsible for the dependency being instructed to complete in the original timescales.

12.4.1 Prioritisation

In Chapter 9, the importance of prioritisation was discussed and highlighted as an inevitable challenge for anyone embarking on a data strategy programme, regardless of whether in the definition or implementation stage. This is clearly something which needs to be planned in to the approach you will take, as you do not want to be caught on the back foot, finding resources stretched or pulled in different directions, or blockers halting your progress with no alternative in mind. You need a clear understanding as to what constitutes your priorities within the implementation phase in particular, otherwise any traction gained can be just as easily lost.

The prioritisation may need to be fluid – as discussed previously, the organisation does not stand still, nor does it usually flex to suit your programme – so there will be a need to reprioritise and reassess the benefits to be accrued as these may change, depending on the circumstances driving the change. At the very least, changes to the running order or delays in implementation may affect the impact and/or the benefits to be realised and undermine the expected value to be delivered from the data strategy (unlikely if your prioritisation has been done effectively and it is not an unforeseen opportunity).

The key to prioritisation is balancing the focus on what I have termed here ‘the tricky triumvirate’– dependencies and capabilities alongside prioritisation – due to the relationship of these factors with one another. For instance, you may need to delay due to the shift in availability of skilled resources that are scarce within the organisation, which, whilst a blow to the programme in the short term, is worth the wait as progressing without those resources involved would be suboptimal and lack credibility within the organisation. A change in a related programme that is due to release benefits for your own programme (for instance, a new system implementation, which you are dependent on to realise your master data management approach, may be delayed, leading to that part of the data strategy implementation work having to be mothballed or postponed) could realign your plan, shift deliverables and lead to releasing resources at one point but needing them back at another, which was not part of the original agreement with a particular part of your organisation.

More fundamentally, if the corporate strategy shifts, then you need to assess whether the data strategy needs to shift with it. This is a balance: there is clearly alignment between the two, but the importance of a change in corporate direction (maybe an unexpected opportunity to acquire a competitor has arisen, or to divest part of the organisation at an attractive price) means it should be considered in depth to assess whether you need to revise your own direction. A data strategy which no longer reflects the priorities of the organisation as a whole is doomed to fail, and likely to struggle to keep any momentum beyond the immediate term.

Prioritisation therefore is a fact of life in delivering any major programme and needs to be reviewed regularly – weekly as a routine check-in is not too much; monthly as a programmatic effort as a minimum, I would suggest, and should certainly involve ratification from your sponsor (the more engagement the better, as it ensures there is nothing on their radar which could catch you unawares). Remember to dock in with other programmes, tracking your own in conjunction with their priorities, asserting your interest in those things which represent dependencies and seeking confidence in timelines being met to build your own stakeholder confidence with your programme.

12.4.2 Dependencies

You should manage your dependencies as if they are as close to you as your key team members on the implementation team. A good programme manager will be able to recite the key dependencies, have a good feel for the current status, and be able to rest easy at night knowing these are being tracked and marshalled as if they were precious cargo. Anything less and you will be taken by surprise. In addition, you should manage them with a healthy degree of scepticism, anticipating shifts in delivery dates and being able to adapt your own activity almost instantly. Those responsible for delivering the dependencies should know you are on top of them as if you were personally responsible for their delivery, to ensure they attach the same importance to them as you do.

When you are building your implementation plan, bear in mind that you will most likely need to validate those dependencies which are made clear to you at the outset but almost certainly have to investigate how many more lurk in dark recesses of programmes and business-as-usual activities. The challenge will be to capture the information in anything like enough detail, but you have to play the role of the demanding customer in capturing this information, otherwise you will end up having to operate as the programme manager for more than just the data strategy implementation.

Keep attuned for new programmes, changes to ways of working and other activities that could cut across your implementation plan. I recommend sharing your plan widely with anyone receptive to listen: it may trigger thoughts or connections with those in other parts of your organisation you would not have thought to contact. It may also forge connections through its visibility, especially if your implementation team has subject matter expertise deployed into the implementation team, whether formally or informally, and create a local port of call to talk these things through in a more proactive way than being a centralised activity would.

12.4.3 Capabilities

Finally, in terms of the triumvirate, capabilities. The skills to move the data strategy forward in either definition or execution will depend entirely on the maturity of your organisation in the data arena. If you are embarking on this afresh, possibly the first person to define a data strategy in your organisation, then you may find willing ‘amateurs’ keen to join in, many of whom through sheer enthusiasm and a willingness to be part of something new bring a level of intensity to the programme. Just bear in mind that their capability will be low compared to the scale of the challenge you are undertaking, and no matter how much ‘learning on the job’ is a way to learn that suits some, the magnitude of what you are seeking to achieve – to transform the organisation into a different way of thinking and operating – is a bold task for anyone, let alone those with little experience.

You will find that the blend of experience and enthusiasm can be powerful in driving your programme forward, so consider whether you need to supplement your enthusiasts for a time with external resources that can bring a level of direction, focus and expertise to overcome the barriers you will face. If you are prepared to blend these within your programme, see how fast you can transition the knowledge and experience of others into your own team – do not become dependent on those outside your organisation, as this is a sure-fire way to end up with a data strategy that is unsustainable; you need that knowledge to reside and remain within your organisation.

If you have SMEs in your organisation who are willing to get behind the data strategy and drive it within their areas of expertise, then make the most of their networks, expertise and personal credibility. There is no substitute for having someone in your tent who is willing to be an early advocate: it will make the process of getting buy-in from their senior stakeholders all the easier.

I recommend you take a holistic yet pragmatic approach to corralling the tricky triumvirate together. It is worth reminding yourself, as you embark on data strategy implementation, that if you have a high degree of control over prioritisation within the programme, dependencies outside of it and locking capabilities around them both, you are in a strong position to be able to move forwards with a greater degree of confidence. It does not guarantee you success – nothing can – but it will probably help you sleep more easily at night.

12.5 EVALUATION AND MEASUREMENT

The preceding chapter highlighted the importance of evaluation and measurement as key disciplines along with benefits realisation in being able to manage the effectiveness of any programme. It is challenging to do so with a data strategy: the accounting practices in many organisations do not attribute benefits to those enablers which provide the means for another part of the organisation to declare success and claim those benefits as their own. However, there is a recognition that to deliver benefits there is often an upstream change programme required that either creates the right environment or delivers a capability for the benefits to be realised downstream.

Having an agreed approach to articulating benefits, evaluating the implementation programme and measuring activity is paramount to keeping stakeholders engaged and retaining confidence in the data strategy. To do this needs a clearly defined baseline, to confirm where you are starting from, and alignment as to what success looks like for your key senior stakeholders that your sponsor can sign up to and be held accountable for delivering. Without this, it is all too easy to lose sight of the progress that is being made and for funding or support to be drained away from the programme to those activities which have a higher profile.

The pre-implementation review (Chapter 8) is the opportunity to take a look at the way the programme will be structured to navigate into mobilising the programme. This would be an opportune time to assess your baseline and whether the starting point has shifted since the drafting of the data strategy, such that it leads to a reappraisal of the priorities or direction to be taken.

I would also recommend being as transparent and accessible as possible in publishing your progress via performance measurement, milestones achieved and benefits realised, to keep a high profile with your implementation programme. Assuming progress is being made, there is no substitute for making the news widely available in your organisation and supplementing this with positive endorsements from stakeholders and influencers across the organisation.

12.6 SPONSORSHIP, EXECUTIVE BUY-IN AND STAKEHOLDER MANAGEMENT

Throughout the book, reference has been made to stakeholder engagement. A data strategy will touch every part of the organisation – we are all data users in some form – and so it is impossible to develop a data strategy without engaging on the widest basis possible to get coherence and gain traction for the effort involved to turn strategy into implementation. Key to this is also the process of selecting the right sponsor.

The importance of ensuring a data strategy is understood from the outset cannot be overstated. Whilst in many organisations data is seen to be something that has always been done, the sad reality is that it is an asset that has been undervalued, underutilised and lacked investment in most organisations. The tide is definitely changing, but data has been overlooked as an asset class in its own right for far too long, and most organisations are not in a good place, whether legally, operationally or a combination of the two. The differentiator, in which data is clearly being taken seriously, can be seen when organisations in a competitive market start to operate in a markedly different way to their rivals due to the insight gained, which can lead to market dominance, increased profitability, acquisition of poorer performing rivals or a combination of the three. This can drive a major realignment of thinking in such a market, such that others need to catch up quickly to survive.

It is, therefore, vital that you get the right level of sponsorship and commitment from the outset. If you are hindered due to the sponsor not fully understanding, you will find yourself unable to get your message across in a compelling way. Similarly, if the sponsor is not fully committed to the programme, you will not have the executive influence you need should you encounter difficulties that need their engagement to address.

If the sponsor hasn’t been selected – there is the possibility that the data strategy is driven from the executive group and has already assigned a sponsor – then make it your business to find the right sponsor, someone who understands the importance of data and is prepared to pitch to their peers on a subject which many of them might fail to find compelling at first, but which can be brought to life by the right person in the sponsor role.

You need to be really switched on to managing a stakeholder network. It is not enough to know who is in what post: you need to know what their data issues are and what could be done that would differentiate performance or compliance for them to make them sit up and take notice. It is a network, not simply a recognition of who sits on the executive board, as there will be influencers at all levels in the organisation. It is your business to know this and to flush these people out and get them engaged. The more effort you put into this from the outset, the easier the implementation activity will be. Build a wider network of interested parties in the data strategy and it makes your communication activity all the easier to land. Identify quick wins, major change opportunities and programmes already under way in their functional area and you are on course to build advocacy for your programme.

Many will say stakeholder engagement is distracting, is time-consuming and absorbs lots of effort. Each of these may apply, but all three will multiply in effect if you do not make the effort to drive it yourself, as you will be either herding cats to keep things aligned or completely out of the loop and last to know something which may be fundamental to the success of your programme. Invest the time, build the network and keep stakeholders engaged. It will be one of your wisest investments in the pre-implementation phase of your programme.

12.7 COMMUNICATIONS

Just as strategy execution requires an implementation plan, that plan will need a parallel stream of activity to communicate throughout the delivery of the data strategy. Prior to implementation, it will have been essential to keep stakeholders aligned and engaged to ensure you have their support when it comes to gaining sign-off of the data strategy, so this should be formalising the relationships and trust you have built up throughout that phase.

The key to successful communications is to understand your audience. In the same way any marketer will tell you that you need to tailor your message to meet client expectations and generate interest, you have the same challenge with the data strategy. It is essential that you understand your stakeholder network well enough to be able to tailor your message to be relevant and engaging, otherwise it will not register and your programme will lose visibility. Remember, a data strategy is for everyone within an organisation, so you have to find the key to make it appeal and generate continued interest.

There is a real risk, especially if you are defining or executing a data strategy within the role you currently undertake in the organisation, that the data strategy is seen as a business-as-usual activity. You might suggest that it is; any strategy is simply an evolution of an organisation and therefore the natural progression over a number of years mapped out for all to understand so they can deliver it. In some ways, this is true of a data strategy, but there is one notable difference. In most organisations there has not been such a focus on data to define a strategy around it, or previous attempts may have failed along the way. It is still a minority of organisations that have embraced a data strategy in a proactive, committed way to see it through and deliver on its impact. Therefore, the data strategy is much more akin to a major change programme, as it will likely seek to change the organisational culture, shift attitudes, realign priorities and break down silos, all of which are significant battles in their own right.

Therefore, it is essential that the data strategy communications effort is focused on driving forward, demonstrating the impact of change being delivered by being able to articulate what has been achieved, the vision of where the organisation is going, and the part each and every member of the organisation has to play in it – the message that data strategy implementation is not a spectator sport is one of the most powerful you will need to make if you are to really succeed in rallying change on any significant level within your organisation.

Communication is your lever to try to influence resistance to change on any organisation-wide level. Without effective communications you are simply setting yourself up to fail, as you need to project the voice of the implementation programme much further and wider than you and the team can do between you. Communicate too much and the programme loses credibility and is an irritant seeking to generate noise but little impact. Too little, and the programme is forgotten about or assumed to be failing. Getting it just right is an art, and the most effective way to succeed in this is to understand the communication routes open to you.

There are many layers to a communications approach to underpin the data strategy implementation. Indeed, the obvious are often the least effective, so balance the direct-from-the-programme messaging with what is delivered through other routes. By that, I mean that you will discover that within functional areas in your organisation there are communications plans and opportunities you may not have been aware of. For instance, that team awayday that the operations directorate is planning, when a significant proportion of the team come together, may be the ideal opportunity to do a 20 minute pitch to the group on data strategy and what it means for them. Make it engaging; if you are suitably prepared, then including time for a question and answer session may be a good way to get interaction and generate interest, especially if you have some supportive colleagues in that part of the organisation who could ask a few questions to get the ball rolling.

Try to get senior leaders and a few others involved; don’t think you have to convey the message alone. As I have said a number of times in this book, recognise the importance of influencers within the group, the people who are recognised for talking sense and project credibility amongst their peers. Do not assume these are always the most senior people in attendance: there are bound to be individuals in that group who are seen to be wise heads or the rising stars that will do a great job for you in ‘selling’ your message in terms that the audience will lap up.

You will find other opportunities too, such as a CEO briefing to all leaders or staff within the organisation, where even a brief three- to five-minute mention of the data strategy shows that it clearly matters to the people at the top of the organisation. Your sponsor, too, should be proactive, finding opportunities to promote the data strategy and why it matters. If you can get each executive board member to do a short article in turn once a month, in the organisation’s newsletter, on the intranet or via whatever other channel is available to you, then be prepared to provide the data strategy material relevant to their part of the organisation as well as the more corporate messages to be got across. Do not stifle their natural style of delivery; you want it to come across as genuine and personal to be most effective, as their people will spot if they are reading a script or using words that they would not normally use.

Review your dependencies, each of which presents a great opportunity to potentially jump on the back of communicating the delivery of that activity with a sideline of what it means in enabling your own implementation programme. In other words, do not pass up a good opportunity to weave your own messaging into the existing planned communications activity. It is often more effective to have the data strategy called out in connection with other newsworthy items than on its own, simply because the data strategy itself is an enabler and so aligns more effectively in partnership with a wider message.

If you are able to devote resource, or get resource assigned from a communications team within your organisation, then this is a sound investment, as it is understanding the nuances of the data strategy – its reach, impact and scale – that is important in maximising the opportunities to build a strong communications stream as part of your implementation plan. It is an investment, but one which will pay back in helping you gain traction and keep an enabling activity front of mind through spotting opportunities for linking to other news in the organisation.

12.8 ALIGNING AND EMBEDDING THE STRATEGIES

The data strategy should not live in a vacuum – it is an enabler to the organisation to reach its goals and beyond, realising what seemed aspirational much more efficiently through improvements to the corporate asset to which it probably has paid least attention: data. So long as your organisation has a corporate strategy, the data strategy is a key enabler to reach the goals through doing things smarter, utilising something which was always available to it but probably not seen in that light.

Of course, you may be working in a very advanced setting, one where data has always been seen to be at the forefront of differentiating your organisation from its competitors and driving innovation. If so, you are one of the lucky ones, as your task is probably to keep pushing the bounds of what can be achieved through innovation in data, and that is an exciting journey in its own right. If you are truly in the vanguard of thinking on data then you will know that data is already a differentiator, and you will be keenly seeking ways to keep your organisation several steps ahead of its competitors. If not, then just think how far you have to go before having to be so inventive, and how much there is to be gained from simply starting out with getting the basics right and building from there.

The data strategy should always align to the corporate strategy; anything else is either failing to comprehend how pivotal data is to the running of your organisation or destined to be seen to be irrelevant. If the corporate strategy is devoid of any understanding of what data can add, it may seem odd to tie yourself to something which has already marginalised data, and whilst the argument has some coherence it misses the one valuable lesson in getting buy-in to the data strategy – you have to work with the grain of supporting the organisation deliver its priorities, rather than use the data strategy as a Trojan Horse to undermine the corporate strategy and position something as ‘better’.

Therefore, you need to be fully aware as to the content of the corporate strategy, its thinking, baseline and goals, to know what is involved in achieving the overarching objective set. This will inform you of the priorities as the executive board members of your organisation see them, the measures on which they are likely to be judged by either shareholders or other interested controlling parties and, therefore, the factor which will likely determine their future in the organisation. The corporate strategy is the key measure of the board’s success in the eyes of those who pass judgement.

It is not enough to know the corporate strategy: you also need to comprehend the data that sits behind it. I don’t just mean the data that has generated those impressive targets for the next three to five years, but the data which will be required to evidence performance. Where does it come from? How reliable is it? What are the decisions that underpin how that data is gathered and evaluated? You need to start to answer these questions if you are to be able to influence an executive board that your data strategy is entirely compatible, supportive and, in fact, an accelerator for their success. If you are able to get the organisation more focused on the data that matters, its quality and driving activity to exploit it more effectively, then you are almost certainly in the right territory to make the executive board succeed. Once you have done your homework, mapping this out to be able to demonstrate how the data strategy will enhance current performance, you are in a position to share this with the executive board and get its commitment.

I have talked at length in this book about the corporate strategy. This is deliberate, as any forward-thinking organisation will have a clearly articulated vision of where it is going, how it will focus efforts to get there and how it will evidence this over time to be able to claim success. Whether your organisation has realised it or not, it has just given you a blank canvas on which to draw up a data strategy that is essential for it to buy in to in order to increase its odds of victory.

However, as referenced in Chapter 6, there are likely to be many interlocking strategies already in play or in development within your organisation. By its very nature, data will almost certainly have a role to play in all of these to varying degrees, and, of course, the extent to which data is recognised as a key enabler in these other strategies is also a clear indicator of just how well data is understood within your own organisation.

It is important to review the other strategies as early as possible in the strategy definition phase. It is a question not simply of identifying what data-related activity sits in there, though of course this is a given, but also of figuring out what is missing. By that I mean what should be in there, either to accelerate the delivery of that particular strategy or to enable it to be achieved much more easily or effectively. Your data strategy drafting process will bring in other strategies from across the organisation. You should seek to identify opportunities to enhance those existing strategies through making your own commitments which, in turn, may become dependencies for other strategies subject to a redraft to recognise the need for an update.

As also discussed at some length earlier in this book, if you are new to defining strategies altogether or just new to the organisation in which you are doing this, do take the opportunity to gather insight from those who have drafted other strategies within the organisation to learn from them the levers of success, the pitfalls to avoid, the expectations within the organisation (could be length, style, structure or more general things such as timescales and route to turn definition into execution) and the key individuals to keep onside. The more advice you can obtain the better; it would be ill-advised to think that those other strategies are so different from yours that there is nothing to be gained from building a knowledge base and taking some friendly advice from those who have been in similar shoes to you yourself.

If you have time, do seek feedback on the update cycles of the other strategies and influence them with your own thinking as you progress through your own definition stage. If there are multiple strategies being worked on at a similar time, it would seem more compelling to link dependencies and themes overtly between them to ensure that, when it is time for review, they are seen to have been developed collaboratively and there is a level of coherence between them. Better to invest that time through the definition phase than to be pulled up on such connections being lacking when you come to present your first draft to the executive board. You may even want to link up your sponsors, so they too have a consistent message to project to the audience and appear aligned in their thinking.

12.9 BALANCING RISK

You will face challenges in developing the data strategy and then moving through to its execution; this is unlikely to be a surprise especially if you have read the book to this point. The key is how you manage the challenges you face, and how prepared you are for them to materialise and be dealt with.

I believe you have to take a measured approach to risk in defining and executing a data strategy. It is impossible to control all the variables. Within the programme itself, there will be occasions where it is likely to deviate from what you anticipated, but there are then so many factors outside the immediate control of the implementation programme that you will have to reassess many times in the course of delivering the data strategy.

Therefore, you need to manage your risk and work out what you are comfortable living with and those risks that need mitigating due to their nature or the significance they pose to the data strategy implementation. This is not to say that the balance you strike won’t change: it probably will as you move into periods of greater certainty and those where the outlook seems very uncertain. However, you need to inform your team of the parameters you are willing to operate within and ensure they both accept the same level of risk and inform you at the earliest opportunity of any emerging risks that are outside those parameters.

There is a case for being prepared for risk and working through the detailed options to address it should various scenarios arise. However, this can consume a lot of effort and, inevitably, if the risk does not materialise it is of little value. Of course, being aware of potential risks and ready to respond as necessary is entirely sensible, and can put you on the front foot with the response you take swiftly which can mitigate the scale of impact. You must assess the risks, determine your parameters and then decide which risks you want to prepare contingency plans of varying levels of sophistication as your safeguard to being driven off course.

Some risks, as discussed in Chapter 10, are clearly more likely than others. For example, over a period of time, your team is likely to lose people who will have specialist knowledge and/or be experts in a particular line of the data strategy definition or execution. You should plan for such eventualities by having a form of succession planning in place, knowing how you would deal with having to shuffle resources or having natural deputies in place who could step up to provide continuity. If there is a lack of experience within the team, you can look to external sources of assistance, but you should only do so if you have a clear programme of how you intend to bring that knowledge in-house into the team around you, so you don’t build an unhealthy dependency with a third party.

If you have the opportunity to influence risks in other programmes, especially when it comes to those activities which are your dependencies, recognise that these may not be the most pressing for those leading the programmes. That is where you need to be able to influence the weighting applied within the programme to reflect the risk to your own programme, otherwise you risk that such key deliverables are deprioritised through a lack of awareness of the wider impact. Of course, the same applies to your own implementation programme – you may be responsible for a key deliverable for another programme and so should balance your own risk assessment with those other programmes’ perspective.

I would strongly recommend being open about the risks you are managing and report the status of them regularly, along with any issues flagged as red with the relevant mitigation actions on the register. It may be the case that those in senior roles within your organisation can assist in providing mitigation or alleviating the risk entirely by refocusing the activities they are responsible for, which those tasked with delivering the programme do not have the authority to do. By being open about your risk approach and reporting, you make it evident to all that there are activities which you are tracking but are not necessarily able to control, and you should assign where that ownership sits in the organisation so the accountability is clear.

12.10 PLAN FOR SUCCESS

Amongst the key messages I would wish you to take away from this book are the importance of planning and the approach you take to manage what I have referred to as waymarkers. The coherence of a plan through the implementation stage will be paramount to your success, and this needs to be the single source of truth when it comes to what you are seeking to deliver, by when, through which resources and to what end (in terms of what it achieves or enables).

Chapter 8 provided the key information to take away on the importance of the plan, and I would stress that the lack of this has been a recurring theme for the best part of two decades in organisations that have failed to turn strategy into execution. Without a plan that has the right elements to it, you are in danger of becoming another statistic!

The work to define the plan begins well in advance of mobilising the team to start on implementation. It takes research, piecing together the artefacts that led to the data strategy being defined and approved, recognising the baseline from which you are starting, deciding the priorities and practicalities of what to deliver when and why, and not least determining the right team to put in place to execute the data strategy. In essence, planning for success in advance of having a plan to go forward with.

There is a lot to comprehend in pulling your plan together, in particular the capabilities of the people around you who will take the data strategy into implementation, how you structure it as a programme and the criticality of aligning stakeholder engagement and effective communications to keep everyone focused. However, time well spent at this stage will pay back in the months and years ahead. Get it wrong now and you will repent throughout and be constantly on the back foot, having to respond to events that should have been foreseen, and identifying gaps and skill shortages that you needed to have spotted from the outset.

I do not want to give the impression that planning for a plan need take too long. Indeed, paralysis can often cause people to lose faith in the data strategy ever coming to fruition and there is a strong case to be made for striking whilst the iron is hot, as soon after getting approval from the executive board to embark on the data strategy implementation as time permits. However, there are ways and means of doing this without it needing to take an eternity, and it is essential that you create a sufficient window to give yourself the opportunity to set out as surefootedly as possible. As the saying goes, ‘act in haste, repent at leisure’. It may be that there are quick wins you can deliver, elements of activity already in train that you can bring into your scope to create a sense of momentum, even the act of bringing the team together in an engaging way with the executive board can give a sense of progress or the programme at least taking shape.

Recognise the key programmes already in train within the organisation, or about to commence in the next 12 months, and align yourself with those shaping them to ensure you are on their radar. Identify as soon as possible those areas of common interest, how you can work together and how your plan can dovetail with theirs to ensure there is a level of integration of activity. Review the key priorities of the major stakeholders within the organisation to assess how you keep them on board and build confidence that your programme is focused on delivering outcomes that will have a major impact on what they are seeking to achieve. Every step you take, think about how you build an engaged community of interest, working with your colleagues to enable their priorities rather than cutting across them to deliver what to them become your priorities in preference to theirs.

Every stage of the process – from data strategy definition to execution – is challenging in so many ways. The pre-mobilisation phase of implementation, structuring your programme and having a clear plan is the one phase that you will struggle to revisit and determines your approach to implementation for a considerable period of time. Get it wrong, and you may struggle to recover. Fail to build confidence and establish trust at an executive level, and you will be in the spotlight as to whether you are the right person for the role and always struggling to assert control over the programme. It is the moment when you dive off the top springboard and it is important you do so on the right trajectory to avoid a painful crash into the water below. Get it right, and you are on course for top marks as you glide through the water gracefully!

12.11 THE NEXT WAVE

I explained in Chapter 10 the merits in having a dynamic data strategy. In some cases, this may not be feasible, but I am focused on where this is practicable, as I certainly think it desirable, if possible, in your organisation.

The concept of a rolling, or dynamic, data strategy is to build a view for the year beyond the original term the data strategy applies to, such that the period it covers remains the same throughout. This has the advantage of retaining the focus and commitment to the data strategy, rather than having to reinvent every three, five or whatever number of years the data strategy represents, and continuing to evolve the thinking through applying the learning as you go. It is a more agile way of working, rather than a waterfall style of approach where the data strategy comes to the end of its term and there is a whole process to justify a data strategy again, which is not the best use of time or effort.

Assuming you are working on a rolling basis, then you need to be thinking of the subsequent year to add long before getting to the end of the first year of the data strategy. Remember the effort involved to get all stakeholders engaged and bought into the content of the data strategy, and whilst you are approaching this extension from a position of having been through it with them once, you do need to recognise that it has to reflect the corporate strategic direction as well as their own aspirations.

Preparing for the next wave of the data strategy basically needs to capture input from the data strategy implementation that has delivered thus far to learn from the experience to date. This could be insight into the progress made in terms of deliverables, the barriers faced (particularly those not anticipated at the outset), the experience of managing dependencies or the commitment from the various stakeholders and their groups. It is also the opportunity to determine whether the anticipated pace in the first year was overambitious or not demanding enough, and whether you have the right resources in place to implement the data strategy effectively. In other words, you will have learnt a lot inside the first year to be able to inform the thinking to plan ahead.

You will also have gained a sense of impact on the corporate strategy, to know whether the focus of your activity is having the impact you had hoped to enable the corporate strategy to reap the benefit. This will have played a key role in determining the value the executive team in your organisation will have seen in the data strategy implementation, and may have given scope to commit further to the data strategy if the rewards are seen to be strong.

I discussed in Chapter 3 the importance of lessons learnt activities every step of the way through the implementation. These are variously called post-implementation reviews, after-action reviews or, in Agile parlance, sprint retrospectives, but all are forms of gathering evidence on what was intended to happen, the activity itself, and the outcome to assess and evaluate what can be adapted or improved in future strands of activity. These are important to you if you are planning to operate a dynamic data strategy, as they inform the design of the further year based upon practical experience.

The planning for the next wave to be added to the data strategy needs to take account of the fact that there might have had to be some reprioritisation of activities in the light of known events or factors which determined a change of plan. This is not an unusual occurrence, nor should you anticipate it to be a one-off. Flexibility is the name of the game when it comes to strategy implementation: it is much more likely to be a success if you operate with a degree of fluidity to recognise potential changing organisational drivers than if you are a hostage to your initial plan.

The mobilisation phase needs to assess the readiness for change, and this needs to be revisited at the start of each annual implementation plan (assuming you work to an annual timeline, of course). It would be a dangerous assumption to move from one year to the next on the basis that momentum is established, as it can be lost quicker than it can be gained, and the transition from one planning year to the next does carry risks of focus shifting elsewhere. Whilst subsequent mobilisation periods should be shorter and focused, they still add value to keep alignment to corporate goals and to revisit prioritisation decisions.

Delivering something which is not deemed a priority and overlooking something which would constitute a far greater benefit to the organisation is a risky strategy if you are to retain the confidence of your sponsor and executive group. Therefore, consideration of the planning assumptions in the data strategy for the remaining period of the original version, as well as the additional year, provides the opportunity to ensure the data strategy remains closely aligned to the executive priorities.

There are also numerous avenues to be pursued in drafting the additional year to be added to the data strategy. Those who were involved in the original drafting may have been involved with the implementation, and can therefore give an assessment of how it has turned out in practice compared to the expectations at the outset. There will also have been others involved in the data strategy definition stage, especially representing the wider organisation, and their feedback on how it has transpired and felt for them from a functional area of responsibility will be key to capture and learn from. There is also your sponsor, who will have a view on how the experience to date has landed, what can be improved, and whether the focus and delivery model is working to be able to sustain their sponsorship going forward. All of these need to be captured in preparing for the next wave.

The further you progress the data strategy, the skills and expertise required become more challenging to source. That is not to say that having the right capabilities is easy to begin with – this is often a stumbling block for many organisations, and especially those that are embarking on a data strategy for the first time – but the more the organisation moves into advanced exploitation of the data the harder it is to find those resources. You may therefore find that you need to bring in such skills from external parties, whether as consultants or interim management resources, to get you started and to provide the experience and knowledge to know how to begin framing the data strategy within your organisation. This comes at a cost, and so has to have a clear focus on what is to be achieved and the measures of success within the remit you are assigning them to fulfil. If not, then it can be high cost for little gain, which clearly undermines the credibility of the data strategy and its implementation.

Utilising resources from outside your organisation in a flexible approach enables the experience of such individuals be applied to build an internal team that is sustainable through having learnt what are the right skillsets to source for your needs. It is always a challenge to embark on a new line of activity within an organisation and to demonstrate the value-add it brings, but the essence of a successful implementation programme is that it should have brought the organisation along with you to be confident that this is the right move at the right time.

Planning for this shift in focus requires time and effort, and it is often hard to enable colleagues to comprehend the art of the possible. This is further reasoning for the value of bringing in skilled personnel to help you get this mobilised – the credibility that those expert resources bring at the outset to get a headwind of activity established and success behind them will make it easier for those who follow and are hired on a more permanent basis.

This assumes that bringing this talent in-house at this time is the right thing to do. It is also an opportune time to determine whether a need to branch out to hire scarce resources in a highly competitive market is the right option to take, and there are plenty of consulting firms that would offer partnering services through which they could take the resourcing headache away and deliver analytics or similar services to your organisation as an outsourced arrangement. Do bear in mind, however, that this activity is a critical differentiator for your organisation in the future and therefore you may deem this to be such critical intellectual property that you would prefer to retain a greater level of control by having an in-house team undertaking this activity.

Either way, the process of an ongoing review of skills and capabilities assessment should form a key part of your rolling strategy, as the initial work will need to evolve as the nature of the data strategy deliverables progresses. Therefore, the assessment will be a developing toolset, expanding as new capabilities are required and exploring the options on how to acquire these in the short and longer term – build, buy, hybrid for flexibility: all are options on the table to determine the best approach to establishing a capability to translate the data strategy into a successful implementation programme.

12.12 IS A DATA STRATEGY SIMILAR FOR ALL ORGANISATIONS?

One of the things that surprises me, and continues to be a perceived barrier to those of us who regard ourselves as data professionals, is the notion that ‘my sector is different’ and the belief that without prior experience in that sector the skills you bring are somehow worthless because of the lack of deep knowledge about the specific sector. There are pockets of activity where specialist data experience can be useful –analytics in the medical sector, for example, works to a slightly different model than most sectors. However, data is fundamentally generic across organisations – one byte is much the same as another, I was told in jest many years ago – and any organisation looking to develop a data strategy will have employees, customers (or users), transactions of some kind, financial metrics and other measures of business performance.

Historically the public sector has been seen to operate on a completely different basis to the private sector. Whilst there are obvious differences – most public sector organisations are publicly funded, or at least have some central funding provided – the challenge with data is remarkably similar. Having split my career between public and private sector organisations, I can see that there are more similarities in challenges than there are differences.

The appreciation of the importance of data has been one of the positives within the public sector in the UK over the last decade or so. In fact, some public sector organisations are now leading the way in certain sectors of activity and setting benchmarks for what ‘good’ looks like, such has been the transformation of centrally delivered services to the citizen via the internet.

It is a similar story in the not-for-profit sector. The learning and opportunity to deploy solutions implemented successfully elsewhere are still as valid; the constraint is simply the environment in which you operate, in particular the funding challenges. Some of the best innovation comes from such environments as it may be impossible to seek investment to buy a solution, and so the only course of action is to learn from others and invent your own approach to solving that problem. It is amazing how much can be achieved with so little when there is clarity of direction and a need to deliver to make a significant difference. Commitment and a ‘can do’ attitude can make up for a lot that ‘politics’ within a bigger organisation may stifle.

Finally, I want to reference those organisations operating in a highly regulated environment. It may be challenging to press ahead on a novel approach to data if there isn’t the buy-in from the central regulatory body, which may well be encouraging greater uniformity of approach between organisations in that sector. The regulator therefore has to be seen in the same light as those key stakeholders who devised the corporate strategy and have been engaged from the outset to ensure there is an understanding as to how the data strategy impacts the ability to meet regulatory obligations.

Often, the regulator will be supportive if the data strategy delivers the potential of greater value to customers or a more efficient and effective delivery organisation at a lower cost. There may be a desire to encourage such ideas to be shared with rival organisations, but as the first mover there is a distinct advantage to setting the bar and being ready to meet it ahead of your rivals. If the regulated market has competition within it, then seizing the opportunity to differentiate and gain first mover advantage is one of the few ways to stand out from your competitors.

12.13 CASE STUDIES

When I deliver workshops on devising a data strategy, I utilise the data strategies already in the public domain to give an insight to the prospective author. This exercise tends to take a two-dimensional concept: considering the data strategy content for relevance and clarity to someone looking at it for the first time; and the ease with which any of the attendees believe they could translate the data strategy into execution. Without detailed knowledge of the organisation the attendees represent – its corporate strategy, goals and maturity – it is impossible for me to write a data strategy for them. Therefore, the workshop utilises the work of others to draw parallels with their own challenge of defining and executing a data strategy in their organisation.

An effective data strategy should outline clearly the current state of the organisation from a data perspective, highlighting the gaps in terms of the foundation layers of getting good-quality data into the right hands, and explaining the top priorities and timescales for delivering the data strategy.

The biggest challenge with most data strategies that are publicly accessible is that, by the very nature of being so, they tend to be published by public sector organisations and so give limited comprehension on those aspects which may differ and therefore be distinct in a private sector organisation. Nonetheless, the basic premise of a good structure with appropriate background to establish the baseline applies.

I ask teams to critique the data strategies I provide in terms of both positive and negative observations and find that this sharpens up the critical reasoning skills of the workshop attendees and starts to get them to think differently. The data strategies I choose are not necessarily exemplars, nor lacking in positive elements. I recognise that having delivered a data strategy, each of the authors of those I have utilised have succeeded in reaching the stage to be able to publish these in a public place for any interested party to view and pass judgement, which is an achievement in itself. I do not know how successful they have subsequently been in moving through the execution stage, and this is part of the challenge of the workshop – to consider the ease with which a participant could take over the reins and execute the data strategy before them.

The process of reflecting on them in a case-study approach enables those participating in the workshop to consider the key points I outline in this book and apply the approach I have proposed – see the Summary of Steps (Section 12.14) – to determine what they might have done differently. Of course, the elements which cannot be appreciated are the culture of the organisation, the resources available to define the data strategy or the expectations that were set at the outset of the process of defining the data strategy. In many ways, the constraints imposed – which could be direct or indirect, in so much as they may be physical constraints of resource, scope or budget as much as the cultural challenge to overcome – within the organisation are the very biggest challenge to comprehend and devise a way forward to either overcome or navigate around.

Links to the data strategies used in a recent workshop can be found via my website, where I maintain links to a variety of data strategies as I find and utilise them in my workshops.⁶ The workshop was divided into six parts, each reflecting the approach I have taken here on defining and executing a data strategy. The attendees critiqued the data strategies to be able to identify some key points in each section, as identified below.

1. Positioning and scope – ensure this is clear and engaging.

• Participants identified that the background is important, to set context.
• There is a need to articulate the why, what and how in explaining the reasoning for defining a data strategy.
• There is a need to set a logical order to flow the scope through the data strategy.
• Goals – corporate and data strategy need to be clearly articulated.
• Define what ‘data’ and ‘data strategy’ mean in the context of the data strategy, so there is absolute clarity for all.
• If diagrams are included, ensure they are clear and articulate the point being made. Generally a positive thing if done well – helps those who can relate to a visual better than blocks of text.
• Clarity is essential – a general point, avoiding jargon or assuming knowledge.
• Audience – ensure the data strategy is accessible to all staff.
• Ensure the vision of the data strategy is actionable, and there is clarity of how this is to be delivered.

2. Audience and route map – pitch and targeting should be key. Set timescales and waymarkers.

• Consider the audience: how does this relate to their goals?
• Clarity of the route map is often lacking, neither waymarkers or milestones to know what progress would look like.
• Delivery or implementation approach is unclear, to know how this would be translated into action.
• Use of case studies as an annex can help guide the reader, and bring context and meaning.
• One of the key quotes from the group: ‘this data strategy is shorter, but seems longer, as it has more impact’.

3. Content – be clear, have high-level direction and set strategy rather than execution.

• A need to review the high level scope and structure, and the level of detail (often felt to be too much) and alignment to other strategies (not clear in the data strategy, so may be implicit, but this is a risky strategy in itself to let others imply links).
• Timing of deliverables unclear – no milestones, certainly no waymarkers, included to know expected pace or achievability of the data strategy.

4. The plan – be focused on delivery, whilst mindful of dependencies, and ensure readiness.

• A switch of mindsets; need to focus on planning cycles and establish dependencies.
• Agility – not clear on readiness to achieve goals to know how feasible the plan is, the way it is to be tackled or the flexibility to adapt through any contingency built in.
• Links to other strategies essential to know what is coming from where, and when, to track effectively.
• Plan should be more about reality, but potentially still visionary.

5. Delivery and flexibility – focus on outcome, stakeholder engagement and benefits.

• Unclear on what benefits are being tracked, how these would be tracked and what the evaluation criteria are.
• Communication – lack of clarity on how stakeholders will be engaged, and what communication strategies are being adopted in the delivery of the data strategy.
• Need to build in review points into the plan to flex the delivery; inevitably it will not go entirely as expected, so build in contingency to provide some options and identify those opportunities to reprioritise.

6. Value – measure, assess, capture benefits as realised, and plan ahead.

• Ensure the data strategy is relevant to all stakeholders to help them identify value.
• Review regularly, and update if necessary, to ensure value is being achieved.
• Focus on benefits realisation and how this is to be reported.
• Plan ahead, so change can be accommodated such that it does not diminish value being achieved.

12.14 SUMMARY OF STEPS – DATA STRATEGY: FROM DEFINITION TO EXECUTION

This section provides the process – reflected in the flow of chapters – that you should undertake to be able to define and execute a data strategy effectively (see Figures 12.1 and 12.2). There is a logic to the process, albeit that the process should have flexibility and agility to be able to refine and adapt based upon what is learnt over the course of following this process.

12.14.1 Data strategy – definition

1. Positioning – Start with positioning the data strategy. Why is it being commissioned and by whom, what do they understand a data strategy to be and what is driving this focus now? Unless you have the clarity as to why you are being asked to take the lead on defining a data strategy and, ultimately, what its purpose is, then you are likely to be second-guessing what is required and therefore likely to fail. Take the time to establish the rationale, key drivers, interested parties and expectations, and you have the foundations from which to work. There should be a coherent business case which defines the expected benefits of the data strategy.

2. Readiness and scope – Consider how ready the organisation is to embrace a data strategy. Establish some context, including how this aligns in the thinking to the corporate strategy, and the scope. Understand the timings, consider sponsorship (if not already determined for you) and assess the stakeholders – who they are and what they will want or expect.

3. Definition – Keep it simple and accessible for all. Set waymarkers and signpost the alignment with the corporate strategy. Identify resources to deliver and the impact of successful delivery for the organisation by reference to the baseline. Understand sign-off for delivery.

4. Route map – Check clarity of data strategy through an implementation lens, establish the timeline, and include waymarkers and measurement as critical elements. Ensure alignment of route map to stakeholder expectations and manage accordingly.

5. Content, structure and alignment – Review final content internally as well as externally. Aim for 12–20 pages, and reflect on simplicity and accessibility. Maturity assessments provide a recognisable baseline from which to plan and improve using a recognised structure. Assess capabilities, and ensure communication with stakeholders throughout to ‘sell’ the story focused on high-level direction across the organisation. Review readiness for implementation for smooth transition.

12.14.2 Data strategy – execution

1. Communication, culture and change readiness – Avoid culture eating your strategy for breakfast! Understand the ‘local’ culture, prepare for resistance to change and unfreeze before embarking on change, then freeze again. Cement your relationship with the sponsor, and ensure all communication channels are exploited. Integrate communications into the programme and be creative in spotting opportunities; don’t be afraid to repeat messages. Utilise local knowledge to build credibility.

2. Mobilisation and planning – Have continuity in the transition from definition to execution, and establish a mobilisation phase to capture all relevant information pre-delivery. Ensure strategic alignment to the corporate strategy, and define milestones with detail and measures included. Plan in dependencies, and prepare to be agile and innovative. Benchmark capabilities for implementation but avoid strategy paralysis.

3. Delivery – Establish and communicate roles and responsibilities. Anticipate change to plan, but it is important to retain the link to data strategy as signed off. Utilise communications but be creative to secure employee engagement. Use Agile to deliver the plan in sprints, utilising the range of expertise in the programme team underpinned by a strong PMO. Ensure agreement to prioritisation, implement governance, and capture requirements and benefits to be tracked.

4. Flexibility – Align to other change programmes and keep tracking, especially dependencies. Be agile in responding to need to change, utilise a change control mechanism and review resource impact. Plan for change to the implementation team members, and develop their skills through the implementation. Use maturity assessments to track progress against the baseline. Build advocacy in the organisation through the change. Explore opportunity for a rolling data strategy, and ensure value through implementation is clearly demonstrated.

5. Demonstrating value – Clarify the baseline to establish the ongoing measurement of the implementation programme. Establish key evaluation criteria, identify benefits to track and adopt a performance management approach. Incorporate maturity assessments to assess progress with appropriate governance and controls. Data is an asset – the new water: explore assigning value to it to focus on investment and maintenance in the asset.

12.15 SOME FINAL PERSONAL REFLECTIONS

There are a few observations I would like to share with you based upon my experience. I recognise that my experience is over a prolonged period of time, perhaps a period in which change in the workplace has been more dramatic than at any other time. As such, some of it may seem a bit dated, but I am a great believer that whilst the environment may change, the fundamentals – people and data – remain constant.

I began my working career delivering analytics and insight at a time in which the marketing team I was part of had one desktop computer between us. Whilst this was only the case for the first year or so, it gave me an incredible insight into how the advent of the PC so quickly changed the workplace and the decision-making process. Suddenly, everyone had the means to communicate, regardless of whether the individual they were communicating with was in the same office, busy doing other things or even remote from the office.

The immediacy of data had arrived, and with it the ability to process data in a way that was previously unimaginable. I built my first segmentation model using large sheets of paper stuck to a wall, a calculator and a lot of trial and error. The evolution of the thinking and calculations were there for all to see. Suddenly, I could do this faster, smarter and more reliably than ever before.

The organisation had bought into an early concept called an enterprise planning system, and whilst it took several years to deploy it successfully, it demonstrated to me how it brought together data from across functions so that it could be used commonly for the benefit of all. Once the technology worked, it demonstrated the failings of the data: not only the quality (and the gaps), but the different terminology that led to differing expectations of what that data represented – metadata, as it was called.

I reflect on this as the impact this had on my earliest dealings with data taught me that it was a universal language spoken with different dialects and different interpretations of terms. When people in organisations talk, they often use expressions that are known within the organisation and used commonly, such as acronyms. When we enter data into a system, we have to have the common understanding of what that field actually means – the metadata – to be able to enter data consistently. For instance, does address include postcode or not? If asked for forename and initials, does it include the first name initial or just those of middle names? This is the sort of confusion that leads to data inaccuracies, mismatches and so on.

When I started, these things were rarely defined, and unless we could establish a common vocabulary which had a consistency of meaning there would always be errors, not because the data was wrong, but because it wasn’t being captured in any system with that consistent understanding and purpose in mind (the metadata).

In other words, a strategic view needed to be taken in order to gain maximum value from the potential to harness data across the entire organisation. It was not enough to be looking at it through a functional lens: if finance numbers did not accord with operations, then each would resort to their own numbers – talking their own language, if you will – and distrust and undermine the credibility of the other function. Further, from a sales and marketing perspective, the importance of understanding the customer was inextricably linked to being able to communicate in an increasingly technical way – they were improving their IT systems too, and so had expectations of their suppliers.

I was tasked with working with Honda at Swindon for a few weeks, understanding how they were applying a just-in-time approach to logistics, managing a supply chain to remove any inefficiency, and reducing waste in their processes to increase effectiveness and, ultimately, impact on the bottom line. It was fascinating to see at first hand the practices commonplace in Japan but still relatively alien to the UK, and to learn how data was at the heart of being able to operate in this way.

Some years later, I had the task of leading a single customer view programme for one of the UK banks. Integrating every customer record across more than 50 legacy systems that were never designed to be integrated into anything else demonstrated that the thinking of organisations had moved on, but the barriers were still very similar – poor data quality, gaps where data should have been captured, clear inaccuracies in data (especially date of birth, with the all too common problem of a default date in thousands of cases being traced back to an error in a previously badly implemented data integration project), to name just a few.

The bank had not appreciated at the outset the scale of data cleansing it was committing to in order to build a single customer view, but, to give it credit, saw the enormous benefit of delivering the project to enforce this being addressed, system by system. I was the first non-IT buyer of the data quality toolset then leading the market worldwide, which demonstrated how little the alignment of data with business owners had borne fruit at this stage. It was satisfying to realise that I was a pioneer in aligning data quality with business ownership, a concept which, whilst still not fully understood or implemented universally to this day, we now recognise as data governance. The notion of managing metadata, establishing data standards, meeting compliance obligations and, most importantly for me at the time, being able to exploit that data to enable the bank to outperform its competitors became the common currency of the organisation and a by-word for differentiating itself from rivals.

I recall these experiences as key drivers in what was a truly transformational period to be in data. The mobile phone had yet to become ubiquitous as a workplace tool at this early point in my career, and it was barely thought of as a sophisticated computer in its own right. I remember seeing a Blackberry for the first time, being shown how it could send and receive emails at a time when hardly anyone comprehended the notion of texting on a mobile phone, and how revolutionary it seemed to be able to receive emails other than whilst at a desk. Of course, this only expanded the data volumes further, and by this time call centres were becoming a very standard way to do business.

I had also been part of a new publishing operation within a small automotive organisation in the mid-1990s and, as a newcomer to the market, took advantage of developing a basic website to promote the publications to organisations around the world. We were delighted to get orders from Japan, Malaysia, South Korea, the USA, amongst others, all places we would never have been able to reach without a low-cost presence that anyone could access. It led to a number of automotive-related organisations in the publishing arena approaching us to act as agents, developing a network of geographically diverse businesses also promoting our products. As a result, our sales figures far exceeded our original expectations. Suddenly, with limited infrastructure or history, the organisation was competing against others with a longer history and brand as if we were equals.

I believe that data exploitation is bounded only by the knowledge we individually have and the constraints we therefore impose on what we do with it. There are plenty of organisations doing some really innovative data projects that shifts the way they tackle some of the biggest challenges they face to be transformational in the direction they are heading. There are some pioneering activities, but many organisations are still lagging behind when it comes to exploiting their data effectively.

In the first half of my career, I can claim to have been a pioneer in so much of what was achieved. None of this was inventing something from scratch; instead it was applying a number of activities or techniques collectively in a way that delivered something that had not been considered before or executed effectively. I look at my work today and the same applies – how can we take a number of things which are known and understood in their own right, but collectively achieve something which is transformational or delivering value that would otherwise fail to be realised.

When you embark on a data strategy, take a moment to think about the aspirations of the organisation and think outside the box. We are in a world awash with data – organisations have been deluged with what was termed ‘big data’ by marketing people out to invent a term for something many organisations already possessed but had made little use of – and the internet contains so much data it is often difficult to sort the wheat from the chaff.

Increasingly, any search for insight will produce as many positive as negative views on what is purportedly reality – the whole debate on coronavirus vaccines raging at the time of writing this book is evidence in itself of being able to use data in a way which confounds scientific sense in terms of a risk assessment and where the experts are losing the battle over the misinterpretation of data, whether innocently or maliciously. However, be bold, do not be timid in seizing the opportunity that defining a data strategy presents to you. It is your opportunity to demonstrate ‘the art of the possible’, to paint a picture that is visionary in what data could do for your organisation. You may be wary of setting high expectations, and that aspiration is fine so long as you have a clearly defined path to how you get there and, most importantly, the foundations that are essential for this to be deliverable.

A data strategy is an opportunity to say ‘I know you say you want to achieve that, but what if we were able to go further, and achieve this?’ It is your opportunity to bring data out from the shadows of the organisation. Let me bring this to life for you.

I mentioned earlier in the book that data is like water; it is renewable and a source of life for your organisation. In the UK a litre of water costs around one penny, and is therefore a commodity much like data in its raw form. Both water and data can degrade if not properly maintained and there is a need for constant review to ensure compliance with quality standards at every step to it reaching the end user. However, think about the value of water and the parallels with data increase. Champagne is 85 per cent water (albeit not straight from a domestic tap), so – whether it is the UK’s favourite Moët & Chandon at £38 a bottle, or a Taste of Diamonds 2013 at £1.2 million – consider how much value has been added to that 85 per cent water content. Then think about the parallels with data, how data captured at minimal cost in the course of an interaction with a supplier, customer or other party can support decisions worth millions to your organisation.

This is why data strategy is important. You have the opportunity to distinguish your organisation, through its performance, as one of the pre-eminent champagne houses in your particular market. Every organisation has access to data; we all have the opportunity to collect and exploit it and do so securely, compliantly and effectively. It is a common denominator but is still a differentiator due to many organisations not seizing the opportunity to establish data as an asset and manage it accordingly to maximise its value. How do you do it better than anyone else, how does yours become the organisation that lifts itself above its peers and how are you seen as a top performer in the sector in which you operate? That is your opportunity, and that is your challenge.

12.16 TEN TO TAKE AWAY

This chapter has summarised the book as a whole, and highlighted some final thoughts. The final ten points to take away are less a repetition of points already made and referenced in the preceding chapters, and more advice in conclusion.

1. Reflect on the opening quote to this chapter. How much of what it says about the barriers being imagination and inertia is reflective of your own experience in your organisation, and where do you intend to start to challenge the culture? This is not a generic quote: it is directly about data and analytics, the subject of your data strategy, so it is likely reflective of your own organisation to some extent too – consider the culture of your organisation and baseline the starting point before embarking on an ambitious vision.
2. Are you going to change the culture or seek to get it to embrace what you are doing? Neither is easy, both present a challenge, but the tactics you adopt need to be clear. You cannot ignore culture, just as culture will not ignore what you are doing. Whether you are aware or not, culture is watching your every move, so bring it to the fore of your thinking, from strategy definition to execution.
3. Resilience is key. You may have a project team, or it may be a singular endeavour. Either way, there will be ups and downs in the programme, and you need to prepare for these and keep others motivated when there are days that don’t go so well. Chart your course; recognise storms will arise and you may have to navigate your way differently from time to time to stay afloat. Don’t lose sight of your destination, and make progress rapidly when conditions permit. There is no harm in getting ahead of the plan if conditions allow, just as it is not a sign of failure to use contingency or revisit some of the deliverables if necessary.
4. Consider the journey, your organisation and the implementation approach. Are you a revolutionary or more likely to succeed as an evolutionary? Recognise that this is entirely organisation dependent, potentially at a point in time, and choose accordingly. It may even be a combination, should you discover parts of the data strategy implementation need a more revolutionary approach due to the nature of the situation you find yourself in.
5. Keep a focus on prioritisation, dependencies and capabilities. These are three areas which may keep you awake at night if not managed tightly by using an integrated plan to keep a balance as you navigate. There are often creative ways in which you can adjust, often in the short term, so do not sacrifice the opportunity to do so through a dogmatic approach to the plan if it is likely to hinder you. Again, communication about these three is critical. Keep stakeholders informed, explain your reasoning and build their trust in your leadership.
6. Reflect on the pyramid of sponsor, senior executives and stakeholder engagement to secure buy-in. If you lose sight of these, even for a moment, you will undermine your programme. An effective sponsor can smooth the path, building consensus and winning over doubters through providing senior-level objectivity and credibility, so ensure you provide your sponsor with as much useful information that you can to provide backing to your programme.
7. Bring communications into the fold as a stream in itself within your programme. The evidence provided throughout this book has demonstrated that poor communication is one of the biggest causes of failure in strategy definition and execution, so don’t become another statistic in the failed programme column. Identify as many opportunities as you can to use communications to your advantage, make the messaging about the core content where possible rather than calling out the data strategy specifically and build trust through using others’ content. The more voices, the more routes to get the message across, the greater the chance of it landing.
8. Throughout the book the message of alignment with the corporate strategy has been stressed. It is an imperative to ground your data strategy in a common purpose that the corporate strategy should provide. Consider how your strategy aligns with other strategies within the organisation and what dependencies exist between the data strategy and others already out there. Build links, establish common goals and share messaging. There will be a greater chance of combined success by working together than each operating in a silo.
9. A strategy cannot be delivered without a plan; implementation needs coherence to bring together the moving parts in a structured approach that is coordinated and interlocking. Through the course of the strategy definition, you should have had a mind to how implementation would look, and in the implementation, you need a focus on delivery and managing dependencies. Make sure your plan is measurable, as your sponsor and senior executives will want evidence of progress and how implementation is delivering what they signed up to in the data strategy.
10. Finally, learn as you deliver. I am an advocate of rolling strategies, adjusting and reflecting as each year passes, using learning to adapt and evolve the strategy to suit business needs and the experience gained in strategy implementation. My experience tells me that the implementation rarely goes according to plan, and so it is feasible that progress may be slower than envisaged (though occasionally can be faster!) due to obstacles which were not foreseen at the outset or changes in direction or resource availability. Consider what has been achieved, what lies ahead and how the corporate goals are shaping up – is the data strategy still relevant and, if so, how can the data strategy implementation help the organisation reach its goals faster?

I would like to wish you well in your endeavour and to remind you that the course from start to finish on the data strategy journey is likely to be a challenging one, almost certainly not as you envisaged it at the outset. Do not lose faith, or feel like it is overwhelming and lose confidence, and do not become consumed by the number of moving parts, trying to keep your focus on too many things. Keep your eye on the goal and ensure you keep communicating. Recognise that there is a need to be agile in your approach and keep focused on where you are going and what have you learnt.

Each day in the world of data strategy is an opportunity to move forward and apply what you didn’t know just a day, a week or a month ago. Do not fall into the trap of being driven by your own plan and fail to apply that learning, for it is the best guide you will ever have as to how your organisation is likely to react. Whilst past performance is no guarantee of future outcomes, in an organisation with a strong culture, a history of ‘this is how we do things’ and a low level of data maturity, it offers the most powerful insight you can have. Learn, adapt and avoid making the same mistakes – such repetition is likely to fatally undermine your credibility – and you will have every chance of keeping on the challenging path to using the data strategy as a key enabler of transforming your organisation and delivering the corporate goals.

Good luck with your next steps and I wish you well on the journey to a successful data strategy implementation!

1 M. Buluswar, How Companies Are Using Big Data and Analytics. 2016. https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/how-companies-are-using-big-data-and-analytics.

2 Charles Handy, Gods of Management: The Changing Work of Organisations. London: Souvenir Press Ltd, 1978.

3 G. Johnson, R. Whittington and K Scholes, Fundamentals of Strategy. Hemel Hempstead: Pearson Education, 2012.

4 J. Lang, How Many Ships Disappear Each Year? 2014. http://actuarialeye.com/2014/03/30/how-many-ships-disappear-each-year/.

5 Robert Matthews, Chancing It. London: Profile Books, 2017.

6 https://www.datastrategists.com.