Vision Maturity, Challenges, and Traditional Remedies

The vast majority of information and other leaders place their organizations’ information vision as Optimized (Level 2). Rightly or wrongly, they express a variety of reasons why establishing an information vision is so difficult. Most often they cite a lack of experienced resources, competing priorities, and difficulty with untangling legacy systems.

Other reasons mentioned include a lack of cultural acceptance for IM, poor data governance, and leadership throughout the organization being on different pages. It’s also difficult to establish an information vision when the organization lacks a well-articulated business vision, or when there’s no specific funding for EIM. Finally, some leaders suggest that the abundance of legacy data sources and poor customer and product reference data (i.e., master information) spanning systems also are vision inhibitors.

Typically, the first thing aspiring information leaders do is to establish a steering committee to hash out the objectives for deploying information and goals for information management. The steering committee attempts to align these goals and objectives with organizational priorities in order to help justify the benefits of EIM and secure executive buy-in. Unfortunately, these committees too often lack in business unit representation. Where sufficient leadership is lacking for EIM, often the CIO—but more often lately, the CEO or board—institutes a CDO (or similar) position. This can help to achieve improved cultural acceptance for the importance of EIM, and to help secure sufficient funding. None of these are bad ideas at all, but they may fall short in establishing a true vision of information as an actual enterprise asset. And lacking the means to calculate the value of information itself, even seasoned information executives such as CDOs struggle with quantifying the benefits of information management.

AIG’s CDO, Leandro DalleMule, told me that the core of his information vision is an overall philosophy of “data defense and data offense… building ‘data management in a box’ by attacking business cases module by module to create a single source of truth that ultimately pulls data from 3000 systems into one place.”¹

Applied Asset Management for Information Vision

After studying the library science domain, it’s clear that information should ascend to a level of importance on par with—or even above—other assets. However, information leaders should promote the principle that information capability is directly related to business process performance and a source of strategic advantage as their colleagues do in human capital management. Information is no longer just a business byproduct or resource. This aligns with the tenet that retained information should have a discernible business context as it does in records management.

The quality of information’s value should also be taken into consideration. For example, library science suggests that it’s important to note that the potential versus probable value of information must be discriminated and recognized. We can no longer afford to fixate only upon the current value of any information asset. We must be able to visualize its possibilities. Be aware of how others are curating and leveraging information assets. An information asset management (IAM) vision should focus not only upon the organization’s own information assets, but also take into consideration how others within and even outside the industry are amassing and deploying them.

Just as with other forms of intellectual property, we must pursue a variety of ways to generate simultaneous value streams from any information asset, not be blinded and limited by a single monetization method. Physical asset management charters are concerned with how to transform assets into business products and services. An information asset vision should include the same dominant principle. Moreover, it should include the ecosystem sustainability tenets of reusing, repurposing, and recycling as approaches for conceiving how to generate more value from information while incurring nominal incremental expense.

For establishing an information culture, existing standards and procedures from knowledge management and human capital can be readily applied. An IAM vision must recognize and support that knowledge-intensive work and business process digitalization shift the focus from the traditional job elements of individuals to information competencies. For example, business professionals of tomorrow who cannot interpret and analyze information beyond high school–level statistics will find themselves of only vocational use, or worse, expendable to their employers. The same with those who cannot creatively curate and deploy information assets.

If we dig deeper into our exploration of likening information environments to living ecosystems, we’re awakened to the concepts of climate changes, disturbances, and sustainability, and how to apply them in an information context. Business climates are changing more rapidly than ever. Call it “global business climate change.” This affects information ecosystems to the extent that these changes must be anticipated by any information vision. No longer can organizations afford merely to sense and respond to worldwide or industry changes brought about by widespread digitalization, business model disruption, and disintermediation, nor by the resulting orders of magnitude increases in the volume, variety, and velocity of information. In biological ecosystems, the concept of succession describes wholesale changes to an ecosystem based on major disturbances. To be part of that succession (rather than part of the extinction that often follows), a vision for IAM must either anticipate these disturbances, be flexible enough to handle any disturbances, or recognize your intention to create such disturbances.

Strategy Maturity, Challenges, and Traditional Remedies

Like with information vision, more than half of organizations place themselves at a Level 2 (Reactive) maturity. Information and business leaders themselves suggest their top inhibitors to higher levels of information strategy are: 1) siloed thinking about information across lines of business, 2) the lack of a dedicated sponsor, and 3) an immutable corporate culture. They also mention issues with differing business unit opinions about how information should be managed, and being able to define the strategy to secure funding. Less frequently cited, but no less important, are competing IT priorities and the inability to prioritize EIM activities, the fear of IT losing control, and disagreement on approaches by stakeholders.

Unfortunately, many organizations skip vision and jump right to strategy. Trying to determine the how before you’ve determined the why is never really a good approach. On the other hand, having no objectives ensures you’ll never fail to meet them!

As with vision, organizations seek to solicit executive sponsorship and make information strategy a C-suite action item. Doing so can help consolidate various departmental “small s” strategies into an enterprise “big S” strategy, leading to improved prioritization and culture change. But if you’re not agile and pragmatic, this can also lead to increased bureaucracy.

Liz Rowe, CDO for the State of New Jersey, told me how she makes the role of business sponsors clear to them: “Whatever I do has to tie with what you guys are doing. My data strategy supports your business strategy. I’m not going to say I have this data strategy without listening to what the business strategy is.”²

Applied Asset Management for Information Strategy

From the world of knowledge management (KM) we learn how important it is to have repeatable, experience-based methods. The IAM strategy should lay out these methods and indoctrinate the organization on them. However, KM processes also instruct on the importance of being able to adapt to new and exceptional situations. As the business climate—and thereby the vision for information—changes, so will the needs of users, applications, partners, suppliers, customers, and (depending upon your industry) regulatory bodies. The ecosystem metaphor reminds us that our IAM strategy must acknowledge the existence of, and plan for the behavior of, information networks formed by the interactions among data sources and other resources such as technologies, applications, individuals, and the business topology. A strategy for the unidirectional, unidimensional flow of information is insufficient. Moreover, as information sources, technologies, and business models rapidly evolve, organizations must continually evolve their IAM practices and competencies.

Thankfully, library science seems to offer a concise recommendation on where to start with managing information: an IAM strategy should ensure the most important information assets are tended to first. It’s too easy and seemingly lazy to treat all enterprise information assets equally. But this too is an inadequate strategy. Under Metrics below and in chapter 11, we will begin to surface ideas for how to prioritize information assets. Within an IAM strategy, those methods need to be applied.

Library science also compels us to learn how others are accumulating information assets, while financial management approaches remind us how to identify and assimilate ancillary information sources. The biological ecosystem metaphor explains why. Ecosystems with higher degrees of biodiversity support the production of a greater quantity of goods and services. Similarly, a robust variety of information assets available to the organization breeds improved business production and performance. Still, the study of ecosystems reminds us that there’s a balance which IAM strategy must take into consideration: Too much biodiversity (or “infodiversity”) can make the system difficult to balance and more susceptible to climate changes. Just as with ecosystem management, information ecosystem management is critical to maintaining an appropriate balance. Three of the “Rs” of sustainability—Refuse, Reduce, and Remove—suggest strategic approaches to ensure the information ecosystem does not become unnecessarily unruly.

As for delivering information to processes, employees, partners, and customers, IT service management (ITSM) methods and standards express in detail how to define a services approach. Applying this model to an IAM strategy can result in limiting the complexity of the information ecosystem while improving its performance. Rather than designing and developing custom datasets and interfaces for information users, an information services strategy and architecture lays out a defined set of information sources and standards along with ways to access and interact with them. Beyond the scope of this book are the information architecture concepts of data – service (DAAS), the logical data warehouse (LDW), data lakes, etc. Similarly, services that the information organization provides application developers, IT, and business users can and should be defined along the lines of ITSM and common enterprise content management (ECM) practices and principles. Too often, even in mature information organizations, the services and resources they provide and how to engage them are ill-defined when compared to the discipline of IT service management.

However, the supply chain management (SCM) discipline instructs that it’s not possible to pre-plan and predefine all the ways assets are going to be leveraged. Remember, a well-oiled supply chain process has the equal ability to handle make-to-stock and make-to-order requirements. Even the best data lake or DAAS architecture isn’t going to be able to accommodate all information needs. Therefore the IAM strategy must include approaches for creating common information layers and handling the intricacies of, and collaboration needed for, bespoke information requirements.

Finally, organizations often have entirely separate, siloed, and disconnected strategies for managing different kinds of data, enterprise content, knowledge, records, and intellectual property—each of which could and should fall under an “information asset” strategy umbrella. James Price, with the information strategy consultancy Experience Matters, recounted for me how one of his Australian clients unwittingly brought several of these domains together:

Metrics Maturity, Challenges, and Traditional Remedies

Most business and information leaders fairly consistently feel their organization’s information vision and strategy level of maturity is reactive. Whereas with information-related metrics, we see a fairly balanced split among organizations at the lowest Level 1 of maturity (Aware), Level 2 (Reactive), and Level 3 (Proactive). Interestingly, many of those who have achieved Level 3 seem to have done so without much of a vision or strategy. So one wonders how effective or aligned their metrics actually are.

The top challenges recounted by information and business leaders all relate to or hint at a lack of metrics-related capabilities. They include challenges with: 1) defining effective measurements, 2) developing an understanding of metrics, and 3) lacking quantification know-how. Other challenges frequently mentioned relate to creating metrics at an enterprise level, a lack of clarity on the benefits of EIM, and no organizational focus on measurement at any level. Some leaders also lament not knowing where to start, being mired in short-sighted metrics, being unable to understand the cost of data quality issues, and having difficulty relating information metrics to business processes.

Prior to joining Gartner as an account executive, Brian Ehlers sold enterprise data management solutions. He spoke to me about the lack of interest in metrics about information itself:

Defining effective information-related metrics is best achieved by tying actions to metrics. Metrics for information-related programs and activities, and for information itself, require thresholds and triggers, and must be actionable. They should adequately reflect the achievement of strategy milestones and toward the vision. And the data used to generate them should be trusted, complete, and accurate. As we’ll consider more in chapter 9, if we’re to begin treating information as an asset, there should be metrics defined, captured, and reported about it as well—not just the information management efforts themselves.

One excellent example of this is AIG’s analytics environment in which its CDO Leandro DalleMule also spoke with me about how he is incorporating data quality metrics into its data lake so users can determine their own comfort level with information.⁵

Applied Asset Management for Metrics

With EIM, most metrics are for justifying, funding and prioritizing, and gauging the success of initiatives—both those involving the management of information, and business initiatives utilizing information. In addition, metrics typically include those related to data quality.

Traditional supply chain doctrine advises on how to develop a complete financial picture of the information supply chain for any information asset. These factors include: the costs to acquire, administer, and apply an information asset; the fulfillment cycle times; return on assets; and return on working capital. Library science also includes the concepts of measuring an artifact’s potential versus probable value. And physical asset management methods suggest measuring the depreciation of inactive information. As well, the entire spectrum of information demand planning and management can be lifted from established supply chain methods.

As we extend the notion of EIM to IAM, many of the measurement concepts from the physical asset domain become relevant. For example, as with the PAS 55 standard, it is not enough to quantify the condition of any asset, but also the costs and benefits of taking corrective action versus performing preventative maintenance, or replacing the asset. While replacing an information asset may be rare indeed, circumstances arise when selecting a new information source becomes a strategy consideration. Human capital management procedures remind us that the quality of any asset can be measured and improved at various levels of granularity.

Unfortunately, most data quality initiatives are undertaken without a financial or business performance analysis of upstream preventative data cleansing versus downstream remediation.

The discussion of supply chain procedures in chapter 8 depicts over a dozen useful information and information management metrics paralleling those from the SCOR model, grouped by: reliability, responsiveness, agility, costs, and efficiency.

Furthermore, physical asset management discipline requires organizations to measure the risk profile of key assets. The security risks alone include hacking, damage, insider theft/disclosure, and accidental disclosure. Assessing these may require a manual audit by security professionals with sufficient expertise. And just as with physical assets, information-borne risks include: the business impact from inaccurate, missing, incomplete, and delayed information; integrity issues between multiple information assets; issues of precision; and information misunderstandings (typically due to a lack of metadata). It may seem cumbersome to assess risk at this level, but with data sampling techniques and data profiling technology, determining most of these metrics on a periodic basis can be straightforward and inexpensive. The trick then is to link these risk metrics to their probable business impact.

Once these probabilities are established they should be applied to the various remediation expenses involved, including:

• Financial data breach penalties,
• Breach notification costs,
• Opportunity cost due to loss of customer revenue,
• Replacement cost of deleted data due to lost customers, malicious or accidental activity,
• Data security remediation tasks,
• Additional data security product purchases,
• Increased cyber insurance premiums, and
• Legal expenses and potential litigation.

and to information risk–related fixed costs such as:

• Dataset acquisition, storage, and processing,
• Audit and compliance processes,
• Data security governance reviews and assessments,
• Data security product purchases, and
• Cyber insurance.

Governance Maturity, Challenges, and Traditional Remedies

Over one-third of organizations self-assess their information governance capabilities at Level 3 (Proactive) or above, with nearly two-thirds still in a reactive or lesser level of maturity. Leaders weighing in on the topic of information governance relate their top three challenges as: 1) moving accountabilities from IT to the business, 2) cultural/organizational buyin, and 3) an understanding of the role of governance. Other typical hurdles declared include: understanding the role of governance, executive sponsorship or CDO, and the notion that it is seen as extra work that nobody wants to own. Some information leaders suggest that a resistance to standard data definitions are limiting factors. One CDO lamented, “Exceptions are the rule, and legacy data cannot be standardized.”

Usual recommendations and efforts surrounding information governance also include the formation of a council, with defined information trustees, stewards, and custodians who develop and rally around a set of draconian policies—policies that are difficult to monitor or enforce. Some organizations have dispensed with the notion of an information owner (for reasons I’ll cover in chapter 10), indicating a clear delineation of information responsibilities and privileges. Sometimes incentives are introduced when policy adherence can be measured. And more frequently we see effective prioritization methods, such as Andrew White’s “three rings” approach,⁶ or those proffered by John Ladley, Robert Seiner, David Plotkin, and others. These enable the organization to focus better on the most important information assets rather than being overwhelmed by trying to govern them all equally (i.e., poorly).

Applied Asset Management for Improved Governance

Information governance (or “data governance,” as it’s more commonly called) deals with establishing and enforcing the principles, guidelines, policies, procedures, and standards for information within an organization.

No two organizations have a complementary set of information governance precepts, let alone a consistent process. Still, other than having a well-defined process, many of the lessons from other asset management domains are reflected in common approaches to (or at least attempts at) information governance.

Particularly with physical asset management, the prevailing standards detail a serious discipline around maintaining a register for recording the location and condition of key assets. Software for information catalogs, data dictionaries, and metadata management have been maturing for decades. But in my estimation they have not advanced in step with increased business needs and importance. Similarly, the procedures for maintaining them are scant in most organizations—typically a function of small budgets reflecting the low priority given to inventorying information assets. Even leading data governance software companies have yet to introduce detailed procedures and standards, or work to collaboratively develop industry accepted ones.

Physical asset management standards also impart the importance of, and steps for, documenting asset classifications, hierarchies, and relationships, and for tracking their utilization, failures, and maintenance histories.

One such company that has is Dell EMC. Barb Latulippe, its chief data governance officer, shared with me how she is able to tie data quality issues to various short- and long-term economic business impacts: “I can show people ‘a day in the life of data,’ including where data quality issues manifest and how they affect cash flow.”⁷

From library science, information governance should adopt the idea of including commentaries in information asset metadata. Detailed descriptions and explanations of information assets would be invaluable to most users—that is, if they were also accessible whenever and wherever a user needed them.

Finally, the world of financial management offers the concept of a true asset fi duciary—an individual with official, legal authority to manage and authorize the use of a particular asset or portfolio of assets. Typical information governance practices involve the assignment of data owners and stewards, but as I argued, the notion of “ownership” carries baggage, and often lacks the formalized, contractual significance of a fiduciary or trustee. Such a role would enable an organization to establish a system of rights and privileges for interacting with any information asset or class of information asset, and to document the economic benefits of these asset interactions.

People-Related Maturity, Challenges, and Traditional Remedies

More information and business leaders believe their organization structure and personnel installment has achieved Level 4 (Managed) maturity than any other of the seven dimensions. Yet over two-thirds still believe that they are reactive or less mature in this regard.

Most often they lament that the larger organization: 1) lacks an understanding of the importance of information management, 2) does not see information-related roles as a worthy investment, and 3) is fearful that growing an information management organization will just lead to personnel layoffs. Often there’s a perception that the CIO does not agree with the concept of a CDO for turf reasons, which may coincide with 65 percent of CDOs being requested by the CEO or board of directors, and only 29 percent of CDOs considering the CIO a “trusted ally.”⁸ This sentiment interferes with information leaders from moving data accountability outside of IT. Other leaders claim they have challenges with determining whether information-related roles should be centralized or whether they should be distributed regionally or divisionally. And funding difficulties are a common undercurrent throughout all of these challenges.

Most organizations seek to boost their maturity in this area by attempting to educate the organization on the importance of information. Unfortunately this may be putting the cart before the horse. Who is making this case effectively if not someone in an executive information role, like a CDO? It usually falls upon the CIO’s shoulders. Yet when we see large jumps in organizational maturity, it often involves bifurcating the IT organization, or at least designating a formal information management group within IT. Other advances in organizational maturity involve creating and budgeting new roles for curating and harvesting information, information architecture, data science, content management, metadata management, master data management, full-time information governance, and other newer roles.

Applied Asset Management for People-Related Improvements

The EIM dimension dealing with organization and roles is meant to ensure there are personnel resources and organizational structures for delivering the various capabilities of information enablement.

As we look to the personnel needs for IAM, the knowledge management discipline offers procedures and standards for aligning responsibilities with required capabilities, and for providing the training and certification for key roles. Moreover, it provides guidance on how to capture, codify, and share experience, including the creation of an “expert roster” to help identify and connect with designated resources and accomplished amateurs in various information competencies. KM is also strong in how to collaborate across the organization, providing information leaders and specialists with guidance on engaging others. Although documented KM standards seem to stop short at the four walls of the organization, they’re certainly applicable across an extended information ecosystem.

Supply chain management principles emphasize the importance of identifying each of the individuals and organizations up and down the supply chain. Who is affected downstream by changes to the way information is managed, and how is upstream IAM affected by changes in the way information is created, captured, accessed, and used? From the worlds of physical and financial asset management, the well-honed procedures and practices for supplier and partner relationship management should be applied to managing relationships with participants throughout the information ecosystem. Too often these relationships with data brokers, business partners, and information management vendors are handled indelicately or haphazardly.

As we learned in chapter 6, the language of information is important, and a recently emphasized concern yet lagging competency. With the data and analytics discipline now spanning well over twenty-five years, the diversity among professionals who lead, architect, design, and use information-related solutions has never been greater. Diversity related to business-IT heritage, veterans-versus-rookies, data-versus-analytics backgrounds, along with industry vertical diversity, creates an environment of professionals who do not share a common language, thereby creating a deficiency in information literacy.

Whether describing how the array of advanced analytics techniques can be applied to mine vast internal and external datasets, or explaining to data scientists the underlying data infrastructure complexity, or helping translate to the board how information manifests in company use cases, the information discipline is becoming the new language of the digital economy. The “speaking data” as a language is rarely recognized, but it is starting to be embraced as the new language of digital business. The rise of storytelling, customer journey maps, infographics, and visualization techniques signals the demand for an enhanced communications medium to convey the business impact of applying information to the business moments that matter most. The ability to speak this new language is a new organizational readiness factor: information literacy. Borrowing from language learning methods can help an organization on its “information as a second language” (ISL) journey.

Last but certainly not least is the concept of leadership. The human capital management (HCM) discipline emphasizes the importance of, and defines the characteristics of, strong leadership over key assets or resources. Until recently there’s been a void in the land of information management. Only lately have we seen the rapid ascent of the chief data officer (CDO) role, and the bifurcation of IT departments into autonomous “I” and “T” groups at companies like PNC Bank in the U.S., Coles Supermarkets in Australia, and the State of New Jersey, among others.

Later in this chapter we’ll cover the role of the CDO in some detail. HCM best practices support this notion, suggesting that one executive should be responsible for taking advantage of an asset, and another executive should be responsible for making it available.

Process Maturity, Challenges, and Traditional Remedies

Likely due to varied conceptions about what information processes or life-cycle means, enterprises’ maturity self-assessments for this dimension also vary. More people feel they’re reactive (Level 2) than at any other level of lifecycle maturity.

Among the most often cited information process concerns are: 1) a “keep data forever because we might need it” mentality, 2) a lack of understanding of the concept of an information lifecycle, and 3) once information is out of sight, it’s out of mind. Other challenges include: complications from differing country regulations, information architects lack influence, and that tending to lifecycle issues will impede time to market. Sometimes we hear about a lack of lifecycle ownership, information hoarding, a lack of rules, and the lifecycle procedures being time-consuming as additional roadblocks.

Information processes center on the lifecycle—procedures developed to execute the flow of information based on your information strategy, metrics, and governance. So the primary way to improve lifecycle maturity is to ensure that when developing your strategy, metrics, and governance you also are defining procedures associated with them. Too often, attention to lifecycle is deemed optional and the failure to plan becomes a plan to fail.

The question of risk also comes into play. What is the risk of copying or storing or deleting certain information assets? Who makes this determination? How are we quantifying this? With these questions answered, determine how you will operationalize those answers. Ratcheting information process maturity also can commence with simply knowing what information you have, where it is, and how it’s used today. And it ends with automating as many lifecycle processes as possible.

Applied Asset Management for Improved Information Processes

As we recall, information management processes perhaps are the most difficult of the EIM dimensions for information leaders to wrap their heads around. In short it’s the activities involved with defining an information architecture and the movement of information in support of the EIM strategy, within the constraints of information governance.

Elevating the concept to IAM really requires the introduction of a supply chain management (SCM) approach while acknowledging that information actually flows in manifold and increasingly unpredictable ways throughout an information ecosystem. The main objective of SCM is to specify and enable the activities and resources for moving a product from the point of manufacturing to the point of consumption. In this regard, the SCM metaphor and associated processes are particularly applicable to external information monetization such as creating information products.

Barb Latulippe at Dell EMC mentioned to me how she draws heavily upon her manufacturing background to encourage the management of data as an asset, including maturing it throughout its lifecycle up until its release to users.¹⁰

Established SCM standards for specifying the creation or capture of information assets, along with their storage, modification, conversion, movement, circulation, retrieval, and destruction, provide one handy framework for laying out an information asset’s lifecycle. A common simplified version which may work for some organizations is the set of source—make—deliver—return supply chain phases. This may even be an excellent way to organize and manage IAM resources, and support architecting the feedback loops endemic to an ecosystem. Also, to adapt this model better for IAM, consider replacing “return” with “service.”

Alternatively, as discussed in chapter 7, one could apply the straightforward Acquire—Administer—Apply framework I offered, including the eighteen information supply chain primitives (i.e., basic actions) for what is done to and with an information asset. This model may offer more specific information lifecycle steps, but can still draw upon SCM procedures and standards for managing the information lifecycle.

Other novel ideas from the supply chain domain for improved IAM lifecycle enablement include: the ability to simulate what-if scenarios for production, delivery and usage, demand-based inventory replenishment, and ensuring demand volatility can be handled via process flexibility and the ability to render resources dynamically.

Additionally, a couple key concepts from library science provide further information lifecycle guidance:

• Collect information from the most respected sources in the original format whenever possible, and
• Organize information in ways that make it easy to locate and near other related information assets.

Infrastructure Maturity, Challenges, and Traditional Remedies

Most information and business leaders, however, contend their level of information infrastructure maturity is at a Level 3 (Proactive), with less than one-third admitting to being in more of a reactive mode or worse.

The top three concerns from organizations about their information infrastructure are: 1) business units demanding their own tools and engaging vendors on their own, 2) IT’s poor response time, and 3) an undefined information architecture. They also lament that migrating from legacy systems, having a clear strategy and direction, and tying investments to benefits are significant inhibitors. And to a lesser extent, the other issues include: how to use cloud with legacy systems, supporting both business units and the enterprise, keeping current with technologies, and finding knowledgeable resources.

Traditional approaches to improving information infrastructure maturity often start with better collaboration between IT (or the EIM organization) and business units. As we have seen a growth in “shadow IT” over the past decade, information leaders typically attempt to set in motion a plan to support it or rein it in and consolidate it. Working with business units and IT also to create a reference architecture for information management can also lead to improved maturity. Other parallel approaches may involve growing IT-related skills and/or moving to cloud and software-as-a-service (SaaS) solutions.

But reference architectures and static plans can only help so much. As Brendan Smith, director of business development at GasBuddy, conceded to me, “With the way we are grabbing, aggregating, and consuming more and more data, it has required us to continually change the tools we’re using and bring in a lot of folks to handle the technical infrastructure.”¹¹

Applied Asset Management Principles for Improved Infrastructure

The information infrastructure includes the technologies used throughout the organization to capture, collect, or generate information; to integrate and transform information; and to move or enable the access to information.

Many of the practices, procedures, and standards from other asset management disciplines suggest the need for, or potential benefits of, additional or enhanced technologies. The broad capabilities of enterprise asset management (EAM) software for automating the tracking physical assets, or software asset management (SAM) for software assets, if reconfigured for information assets, would be a boon for IAM.

Other asset management principles merely support and emphasize the objectives of information technology, such as:

• Library science and financial management tenets assert the need for the primary availability of high-demand assets,
• Library science includes copious procedures for ensuring the protection and preservation of artifacts, and
• Enterprise content management standards require storing content in optimal structure, enabling its discoverability, and laying out work-flows to enable content creation and editing collaboration.

The Chief Data Officer: Foresight, Not Fad

The chief data officer role is foresight, not fad. To demonstrate, let’s start with looking at the path to the emergence of the chief data officer role itself.

I often speak to individuals including other executives who scoff at the notion of needing another chief somethingorother. Advances in business and management science have always required new kinds of specialist leaders. In the 1940s and 1950s, companies rarely had an executive head of human resources. In the 1960s and 1970s, companies rarely had a chief marketing officer. And in the 1980s and 1990s, few had an executive risk management leader. However, these roles now are common, reflecting the mission-critical importance of these functions and the assets they oversee. Today, a new kind of leader is starting to arise, to take charge of the management and exploitation of the information assets of the firm. However, this is an early stage for the role, and as one might expect, the new role is very roughly formed—giving rise to confusion and false starts.

However, their existence is an important signal that should be heeded. A plethora of new senior titles have emerged that have something to do with managing some aspect of data, and the CDO role is the most logical place for them to converge. They are pioneers of a key future discipline in infonomics.

Today’s CIOs may carry the badge “information officer,” but they are seldom—if ever—the chief of information in the firm. However, they do exercise authority over the IT systems that are the containers of that information. So they can see—better than almost anyone else—the nature of the information challenges and opportunities.

Indeed, the growing information “asset base” of the firm is also seen by more farsighted CEOs as a great opportunity to be harnessed. Today, nearly two-thirds of instantiated CDO positions are requested by the CEO, CFO, or board of directors, with over half of CDOs given enterprise-wide responsibilities. Yet, over one-quarter of the time, the anointing of a CDO is due to a particular data-related crisis.

Individuals with the CDO title report at various levels of the organization. 38 percent report to the CEO. Most others are still a rung further down the organization chart: 18 percent report to the COO, 11 percent to the CIO, and 6 percent to the CFO. Less often they report to another officer or senior vice president. Those with less positional authority are more often focused on defensive work, such as regulatory compliance or legal support. They also tend to be focused on narrower organizational scope—such as the needs of only one or two departments.¹²

CDOs involved with progressive business strategy and adding new business value tended to be reporting outside of IT and to a higher level. Their span of thinking about the kinds of information to be commanded tends to be wider, and their notion of information as a valuable corporate asset tends to be more complete.

This leads us to the concept of information value and economics. In parts I and II of this book, we covered monetizing and managing information as an asset. Now next in part III let’s examine how and why to measure information—its foundational properties like quality, along with its financial value—and ideas about how to fully capitalize on its unique economic properties.