Big Data MBA. Schmarzo Bill

of all sizes must reframe the big data conversation with the business leaders in the boardroom. The critical and difficult big data question that business leaders must address is:

      How effective is our organization at integrating data and analytics into our business models?

Before business leaders can begin these discussions, organizations must understand their current level of big data maturity. Chapter 2 discusses in detail the “Big Data Business Model Maturity Index” (see Figure 1.1). The Big Data Business Model Maturity Index is a measure of how effective an organization is at integrating data and analytics to power their business model.

Figure 1.1 Big Data Business Model Maturity Index

      The Big Data Business Model Maturity Index provides a road map for how organizations can integrate data and analytics into their business models. The Big Data Business Model Maturity Index is composed of the following five phases:

      • Phase 1: Business Monitoring. In the Business Monitoring phase, organizations are leveraging data warehousing and Business Intelligence to monitor the organization's performance.

      • Phase 2: Business Insights. The Business Insights phase is about leveraging predictive analytics to uncover customer, product, and operational insights buried in the growing wealth of internal and external data sources. In this phase, organizations aggressively expand their data acquisition efforts by coupling all of their detailed transactional and operational data with internal data such as consumer comments, e-mail conversations, and technician notes, as well as external and publicly available data such as social media, weather, traffic, economic, demographics, home values, and local events data.

      • Phase 3: Business Optimization. In the Business Optimization phase, organizations apply prescriptive analytics to the customer, product, and operational insights uncovered in the Business Insights phase to deliver actionable insights or recommendations to frontline employees, business managers, and channel partners, as well as customers. The goal of the Business Optimization phase is to enable employees, partners, and customers to optimize their key decisions.

      • Phase 4: Data Monetization. In the Data Monetization phase, organizations leverage the customer, product, and operational insights to create new sources of revenue. This could include selling data – or insights – into new markets (a cellular phone provider selling customer behavioral data to advertisers), integrating analytics into products and services to create “smart” products, or re-packaging customer, product, and operational insights to create new products and services, to enter new markets, and/or to reach new audiences.

      • Phase 5: Business Metamorphosis. The holy grail of the Big Data Business Model Maturity Index is when an organization transitions its business model from selling products to selling “business-as-a-service.” Think GE selling “thrust” instead of jet engines. Think John Deere selling “farming optimization” instead of farming equipment. Think Boeing selling “air miles” instead of airplanes. And in the process, these organizations will create a platform enabling third-party developers to build and market solutions on top of the organization's business-as-a-service business model.

      Ultimately, big data only matters if it helps organizations make more money and improve operational effectiveness. Examples include increasing customer acquisition, reducing customer churn, reducing operational and maintenance costs, optimizing prices and yield, reducing risks and errors, improving compliance, improving the customer experience, and more.

      No matter the size of the organization, organizations don't need a big data strategy as much as they need a business strategy that incorporates big data.

      Focus Big Data on Driving Competitive Differentiation

      I'm always confused about how organizations struggle to differentiate between technology investments that drive competitive parity and those technology investments that create unique and compelling competitive differentiation. Let's explore this difference in a bit more detail.

       Competitive parity is achieving similar or same operational capabilities as those of your competitors. It involves leveraging industry best practices and pre-packaged software to create a baseline that, at worst, is equal to the operational capabilities across your industry. Organizations end up achieving competitive parity when they buy foundational and undifferentiated capabilities from enterprise software packages such as Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), and Sales Force Automation (SFA).

       Competitive differentiation is achieved when an organization leverages people, processes, and technology to create applications, programs, processes, etc., that differentiate its products and services from those of its competitors in ways that add unique value for the end customer and create competitive differentiation in the marketplace.

      Leading organizations should seek to “buy” foundational and undifferentiated capabilities but “build” what is differentiated and value-added for their customers. But sometimes organizations get confused between the two. Let's call this the ERP effect. ERP software packages were sold as a software solution that would make everyone more profitable by delivering operational excellence. But when everyone is running the same application, what's the source of the competitive differentiation?

      Analytics, on the other hand, enables organizations to uniquely optimize their key business processes, drive a more engaging customer experience, and uncover new monetization opportunities with unique insights that they gather about their customers, products, and operations.

      Leveraging Technology to Power Competitive Differentiation

      While most organizations have invested heavily in ERP-type operational systems, far fewer have been successful in leveraging data and analytics to build strategic applications that provide unique value to their customers and create competitive differentiation in the marketplace. Here are some examples of organizations that have invested in building differentiated capabilities by leveraging new sources of data and analytics:

      • Google: PageRank and Ad Serving

      • Yahoo: Behavioral Targeting and Retargeting

      • Facebook: Ad Serving and News Feed

      • Apple: iTunes

      • Netflix: Movie Recommendations

      • Amazon: “Customers Who Bought This Item,” 1-Click ordering, and Supply Chain & Logistics

      • Walmart: Demand Forecasting, Supply Chain Logistics, and Retail Link

      • Procter & Gamble: Brand and Category Management

      • Federal Express: Critical Inventory Logistics

      • American Express and Visa: Fraud Detection

      • GE: Asset Optimization and Operations Optimization (Predix)

      None of these organizations bought these strategic, business-differentiating applications off the shelf. They understood that it was necessary to provide differentiated value to their internal and external customers, and they leveraged data and analytics to build applications that delivered competitive differentiation.

      History Lesson on Economic-Driven Business Transformation

      More than anything else, the driving force behind big data is the economics of big data – it's 20 to 50 times cheaper to store, manage, and analyze data than it is to use traditional data warehousing technologies. This 20 to 50 times economic impact is courtesy of commodity hardware, open source software, an explosion of new open source tools coming out of academia, and ready access to free online training on topics such as big data architectures and data science. A client of mine in the insurance industry calculated a