Chapter 20
The Importance of Big Data to Business
In This Chapter
Using big data as a business planning tool
Incorporating big data into your company’s planning process
Making business decisions with big data
Starting your big data journey
The idea of managing data to transform business is nothing new. As long as organizations have been capturing information about their business processes, their customers, their prospects, and their products, a big data problem has existed. It was simply not economical or practical for companies to be able to effectively manage all the data across their organizations. Therefore, for the past 30 years, companies have had to make compromises. Either data management professionals would have to compromise by saving only snapshots of data or they would have to create separate databases to store segments of data. Companies have tried complex work-arounds to try to integrate data together to improve business decision making. This often required programmers to develop complex programs to create the right business view of data.
The gating factors keeping businesses from being able to get the most business value from their data were varied and complicated. These factors included
The expense of purchasing enough systems and storage to physically contain the data
The problem of managing a database that was too big to be managed, backed up, or queried
The immaturity of available technology to manage the variety of the data at the right speed
The difficulty of programming to integrate data elements and then maintain that code
The complexity of keeping data up to date and relevant to emerging business requirements
In this chapter, we explore the business imperative behind the movement to big data and describe how companies leverage big data to affect business outcomes.
Big Data as a Business Planning Tool
What does the business hope to achieve by leveraging big data? This is not an easy question to answer. Different companies in different industries need to manage their data differently. But some common business issues are at the center of the way that big data is being considered as a way to both plan and execute for business strategy. While most businesses have mechanisms in place to track customer interactions, it is much more difficult to determine the relationships among a lot of data sources to understand changing customer requirements.
The greatest challenge for the business is to be able to look into the future and anticipate what might change and why. Companies want to be able to make informed decisions in a faster and more efficient manner. The business wants to apply that knowledge to take action that can change business outcomes. Leaders also need to understand the nuances of the business impacts that are across product lines and their partner ecosystem. The best businesses take a holistic approach to data. Four stages are part of the planning process that applies to big data: planning, doing, checking, and acting.
Stage 1: Planning with data
With the amount of data available to the business, dangers exist in making assumptions based on a single view of data. The only way to make sure that business leaders are taking a balanced perspective on all the elements of the business is to have a clear understanding of how these data sources are related. But companies typically only have a small amount of the data they will need to make informed decisions. The business needs a road map for determining what data is needed to plan for new strategies and new directions.
For example, if your company needs to expand the type of services it can offer to existing customers, you need to analyze as much data as possible about what customers are buying and how that is changing. What do customers like and dislike about products? What are competitors offering? What new macro trends are emerging that will change customer requirements? And how are your customers reacting to your products and those from your competitors? If you find ways to effectively manage the data, you may be able to have a powerful planning tool. While the data may confirm your existing strategy, it might send you in new unexpected directions. Part of your planning process requires that you use a variety of data to test assumptions and think differently about the business.
Stage 2: Doing the analysis
After your organization understands the business objectives, it is time to begin analyzing the data itself as part of the planning process. This is not a stand-alone process. Executing on big data analysis requires learning a set of new tools and new skills. Many organizations will need to hire some big data scientists who can understand how to take this massive amount of disparate data and begin to understand how all the data elements relate in the context of the business problem or opportunity.
The big data analytics market is very immature, so you find few highly abstracted and easy-to-use tools to support analysis. So right now, it will be necessary to find highly skilled professionals within consulting organizations who can help you make progress. Big data analytics is a dynamic area that is experiencing very rapid change. Combining the immaturity of the analytics with the needs of business to continually add new data sources that need to be added into the analytics approach will put a lot of pressure on the business to push the boundaries of what is possible. The businesses that are able to get a handle on applying big data analytics to their business planning will be able to identify business nuances and changes that can impact the bottom line. For example, if your company is in the e-commerce market, you will want to analyze the results of new partnerships to see whether they are generating both customer interest and new sales. You may want to see the reaction to the new services on social media sites. At the same time, you want to have a clear understanding of what your closest competitors are offering that could impact revenue.
Stage 3: Checking the results
It is easy to get caught up in the process of analyzing data and forget to do a reality check. Does the analysis reflect business outcomes? Is the data you are using accurate enough or do problems exist? Are the data sources going to truly help with planning? This is the time to make sure that you are not relying on data sources that will take you in the wrong direction. Many companies will use third-party data sources and may not take the time to vet the quality of the data. When you are planning and making business decisions based on analysis, you have to make sure that you are on a strong foundation.
Stage 4: Acting on the plan
After this cycle of analysis is complete, it is time to put the plan into action. But actions have to be part of an overall planning cycle that is repeated — especially as markets become more dynamic. Each time a business initiates a new strategy, it is critical to constantly create a big data business evaluation cycle. This approach of acting based on results of big data analytics and then testing the results of executing business strategy is the key to success. Big data adds the critical element of being able to leverage real results to verify that a strategy is working as intended. Sometimes the results of a new strategy do not match expectations. In some cases, this will mean resetting the strategy. In other situations, the unintended consequences will lead a company in a new direction that might have a better outcome.
Adding New Dimensions to the Planning Cycle
With the advent of big data, some changes can impact the way you approach business planning. As more businesses begin to use the cloud as a way to deploy new and innovative services to customers, the role of data analysis will explode. You might want to therefore think about another part of your planning process. After you make your initial road map and strategy, you may want to add three more stages to your data cycle: monitoring, adjusting, and experimenting.
Stage 5: Monitoring in real time
Big data analytics enables you to monitor data in near real time proactively. This can have a profound impact on your business. If you are a pharmaceutical company conducting a clinical trial, you may be able to adjust or cancel a trial to avoid a lawsuit. A manufacturing company may be able to monitor the results of sensors on equipment to fix a flaw in the manufacturing process before it has a greater impact.
Stage 6: Adjusting the impact
When your company has the tools to monitor continuously, it is possible to adjust processes and strategy based on data analytics. Being able to monitor quickly means that a process can be changed earlier and result in better overall quality. This type of adjustment is something new for most companies. In the past, analysts often were able to analyze the results of monitoring processes, but typically after a problem had already become apparent. Therefore, this type of analysis was used to find out why a problem happened and why a product failed or why a service did not meet customer expectations. While understanding the cause of failure is important, it is always better to be able to avoid mistakes in the first place.
Stage 7: Enabling experimentation
Being able to try out new product and service offerings is important in an increasingly real-time data world. But it is not without risk. Experimentation without the capability to understand the outcome quickly will only confuse customers and partners. Therefore, combining experimentation with real-time monitoring and rapid adjustment can transform a business strategy. You have less risk with experimentation because you can change directions and outcomes more easily if you are armed with the right data.
Keeping Data Analytics in Perspective
Big data is beginning to have an important impact on business strategy. As companies are putting a big data strategy in place, management is beginning to realize that they can begin leveraging data throughout the planning cycle rather than at the end. As the big data market begins to mature, companies will be able to run their business based on a data-centric view of the world. Predictive analytics, for example, is making it possible for companies to understand the small and subtle changes in customer buying patterns so that they can make changes in strategy earlier. For example, Walmart uses social media data to determine what new products customer are starting to demand earlier in the cycle. It is difficult for a retail company to change the products already on store shelves. If a company can predict changes in customer buying preferences six months in advance, it can have a huge impact on the bottom line.
It is easy to assume that all a company needs is to create a big data platform and the strategy will just happen. The reality, of course, is much more complicated. While big data will be an important business tool, a danger exists in relying too much on data alone. Business leaders need to make sure that they do not trust the results of big data analytics in isolation from other factors that cannot easily be codified into an algorithm. You find subtle issues such as what strategies are practical in light of changing business conditions. You’ll see emerging trends or a changing competitive landscape that isn’t showing up in the analysis. Senior leaders also bring intuition and knowledge to the table. So before you assume that big data is the panacea for all business strategy issues, make sure that you are taking a balanced approach.
Getting Started with the Right Foundation
So, how do you get started in your journey to creating the right environment so that you are ready to both experiment with big data and be prepared to expand your use of big data when you are ready? Will you have to invest in new technologies for your data center? Can you leverage cloud computing services? The answer to these questions is yes. You will have to make changes to support big data. First, you need to make sure that you understand the various types of data that are important to your organization. You also need to understand the new types of data management environments that are available. Each of these new options could be helpful in different types of situations.
For example, if you need to process data quickly, you might want to evaluate in-memory databases. If you have a lot of data that needs to be processed in real time, streaming data offerings are worth evaluating. Many different products can handle spatial data. Chapters 1 and 4 give you a good idea of some of the products and architectures that will support a variety of different data structures and different analytic processes. In addition, you will want to evaluate the cloud-based offerings that allow you to store massive amounts of information inexpensively. Several cloud-based analytics services are changing the way that companies can access and use complicated tools that were never affordable in the past.
Getting your big data strategy started
While clearly a huge amount of technology is involved in building your big data strategy, you have to get started by building the right team of individuals with both technical and business knowledge. You will need business leaders who are involved in planning the strategy for the next generation of products and services. You need to understand the types of answers that they are looking for and the types of questions that they are asking.
Therefore, the best way to get started is to build a team. You may want to involve consultants who have experience working on big data implementations and can help you with best practices. You should understand that at this stage in the industry, you would be working with low-level tools that typically involve a lot of programming. But as new tools emerge, you should continue to experiment to take advantage of innovations. In some cases, you will discover that vendors and consultants have packaged best practices into product offerings that can be customized for your markets and your business model.
But to take advantage of the emerging technologies, it is important that you focus on the basics. You need to make sure that after you select the right data elements for your analysis, that it all makes sense. You have to be able to trust the data so that you minimize the risks. Each new data source will have its own structure. These sources may not be well-vetted. So, before these sources are brought into an analytics framework, you will have to make sure that metadata is consistent.
Getting started will mean taking things slowly. Most organizations do not jump in and start doing full-fledged, corporate-wide big data analytics. Rather, most companies continue to progress with the analytics they have always been doing. However, they are adding pilot projects or planning to add pilots based on areas where the business needs to leverage new types of data at greater speed than ever before.
Planning for Big Data
The ways that big data can be applied to business problems are almost endless. Virtually every industry has the capability or potential to collect and analyze data to improve business outcomes. Some use cases are more obvious than others. You find hundreds of examples of how companies might use social media data to improve business planning and execution. But the capability to leverage big data touches everything from monitoring manufacturing processes to the detection of diseases. In the insurance industry, executives are using big data to figure out what product offerings are the best for a certain customer with the least amount of risk.
Executives in almost every industry want to be able to analyze patterns in all different types of structured and unstructured data to be able to predict outcomes. Companies are leveraging information from customer service notes and information collected from sensors and system logs to understand their businesses. Big data has the potential to help companies get a handle on both risk and opportunities in the best way. Chapters 21 and 22 list how big data is applied to specific industries.
Transforming Business Processes with Big Data
More and more organizations are discovering that they can take advantage of lots of different types of information in new ways. The maturation of the technology will coincide with business leaders’ ability to push the envelope on business strategy. We have only touched on the potential value of big data. Companies can save money by identifying fraud before money is paid out. Companies can determine the next best action based on real-time access to customer actions — what they are buying and what they are asking. Healthcare practitioners can leverage massive amounts of best practice data to be better prepared to treat patients more quickly with better results at a lower cost. Needless to say, this is only an early indicator of what will be possible. Preparing for this new world requires your organization to gain knowledge about the potential for technology to transform business processes.