Big Data MBA. Schmarzo Bill

enrich, and analyze many new sources of data, whether structured or unstructured. The data lake enables organizations to treat data as an organizational asset to be gathered and nurtured versus a cost to be minimized.

Organizations need to treat their reporting environments (traditional BI and data warehousing) and analytics (data science) environments differently. These two environments have very different characteristics and serve different purposes. The data lake can make both of the BI and data science environments more agile and more productive (Figure 1.2).

Figure 1.2 Modern data/analytics environment

      CROSS-REFERENCE

      Chapter 7 (”The Data Lake“) introduces the concept of a data lake and the role the data lake plays in supporting your existing data warehouse and Business Intelligence investments while providing the foundation for your data science environment. Chapter 7 discusses how the data lake can un-cuff your data scientists from the data warehouse to uncover those variables and metrics that might be better predictors of business performance. It also discusses how the data lake can free up expensive data warehouse resources, especially those resources associated with Extract, Transform, and Load (ETL) data processes.

      Don't Think “What Happened,” Think “What Will Happen”

      Business users have been trained to contemplate business questions that monitor the current state of the business and to focus on retrospective reporting on what happened. Business users have become conditioned by their BI and data warehouse environments to only consider questions that report on current business performance, such as “How many widgets did I sell last month?” and “What were my gross sales last quarter?”

Unfortunately, this retrospective view of the business doesn't help when trying to make decisions and take action about future situations. We need to get business users to “think differently” about the types of questions they can ask. We need to move the business investigation process beyond the performance monitoring questions to the predictive (e.g., What will likely happen?) and prescriptive (e.g., What should I do?) questions that organizations need to address in order to optimize key business processes and uncover new monetization opportunities (see Table 1.2).

Table 1.2 Evolution of the Business Questions

      CROSS-REFERENCE

      Chapter 8 (“Thinking Like a Data Scientist”) differentiates between descriptive analytics, predictive analytics, and prescriptive analytics. Chapters 9, 10, and 11 then introduce several techniques to help your business users identify the predictive (“What will happen?”) and prescriptive (“What should I do?”) questions that they need to more effectively drive the business. Yeah, this will mean lots of Post-it notes and whiteboards, my favorite tools.

      Don't Think HIPPO, Think Collaboration

      Unfortunately, today it is still the HIPPO – the Highest Paid Person's Opinion – that determines most of the business decisions. Reasons such as “We've always done things that way” or “My years of experience tell me …” or “This is what the CEO wants …” are still given as reasons for why the HIPPO needs to drive the important business decisions.

      Unfortunately, that type of thinking has led to siloed data fiefdoms, siloed decisions, and an un-empowered and frustrated business team. Organizations need to think differently about how they empower all of their employees. Organizations need to find a way to promote and nurture creative thinking and groundbreaking ideas across all levels of the organization. There is no edict that states that the best ideas only come from senior management.

      The key to big data success is empowering cross-functional collaboration and exploratory thinking to challenge long-held organizational rules of thumb, heuristics, and “gut” decision making. The business needs an approach that is inclusive of all the key stakeholders – IT, business users, business management, channel partners, and ultimately customers. The business potential of big data is only limited by the creative thinking of the organization.

      CROSS-REFERENCE

      Chapter 13 (“Power of Envisioning”) discusses how the BI and data science teams can collaborate to brainstorm, test, and refine new variables that might be better predictors of business performance. We will introduce several techniques and concepts that can be used to drive collaboration between the business and IT stakeholders and ultimately help your data science team uncover new customer, product, and operational insights that lead to better business performance. Chapter 14 (“Organizational Ramifications”) introduces organizational ramifications, especially the role of Chief Data Monetization Officer (CDMO).

      Summary

      Big data is interesting from a technology perspective, but the real story for big data is how organizations of different sizes are leveraging data and analytics to power their business models. Big data has the potential to uncover new customer, product, and operational insights that organizations can use to optimize key business processes, improve customer engagement, uncover new monetization opportunities, and re-wire the organization's value creation processes.

      As discussed in this chapter, organizations need to understand that big data is about business transformation and business model disruption. There will be winners and there will be losers, and having business leadership sit back and wait for IT to solve the big data problems for them quickly classifies into which group your organization will likely fall. Senior business leadership needs to determine where and how to leverage data and analytics to power your business models before a more nimble competitor or a hungrier competitor disintermediates your business.

      To realize the financial potential of big data, business leadership must make big data a top business priority, not just a top IT priority. Business leadership must actively participate in determining where and how big data can deliver business value, and the business leaders must be front and center in leading the integration of the resulting analytic insights into the organization's value creation processes.

      For leading organizations, big data provides a once-in-a-lifetime business opportunity to build key capabilities, skills, and applications that optimize key business processes, drive a more compelling customer experience, uncover new monetization opportunities, and drive competitive differentiation. Remember: buy for parity, but build for competitive differentiation.

      At its core, big data is about economic transformation. Big data should not be treated like just another technology science experiment. History is full of lessons of how organizations have been able to capitalize on economics-driven business transformations. Big data provides one of those economic “Forrest Gump” moments where organizations are fortunate to be at the right place at the right time. Don't miss this opportunity.

      Finally, organizations have been taught to think cheaper, smaller, and faster, but they have not been taught to think differently, and that's exactly what's required if you want to exploit the big data opportunity. Many of the data and analytics best practices that have been taught over the past several decades no longer hold true. Understand what has changed and learn to think differently about how your organization leverages data and analytics to deliver compelling business value.

      In summary, business leadership needs to lead the big data initiative, to step up and make big data a