don't take the lead in identifying where and how to integrate big data into your business models, then you risk being disintermediated in a marketplace where more agile, hungrier competitors are learning that data and analytics can yield compelling competitive differentiation.
Homework Assignment
Use the following exercises to apply what you learned in this chapter.
Exercise #1: Identify a key business initiative for your organization, something the business is trying to accomplish over the next 9 to 12 months. It might be something like improve customer retention, optimize customer acquisition, reduce customer churn, optimize predictive maintenance, reduce revenue theft, and so on.
Exercise #2: Brainstorm and write down what (1) customer, (2) product, and (3) operational insights your organization would like to uncover in order to support the targeted business initiative. Start by capturing the different types of descriptive, predictive, and prescriptive questions you'd like to answer about the targeted business initiative. Tip: Don't worry about whether or not you have the data sources you need to derive the insights you want (yet).
Exercise #3: Brainstorm and write down data sources that might be useful in uncovering those key insights. Look both internally and externally for interesting data sources that might be useful. Tip: Think outside the box and imagine that you could access any data source in the world.
Chapter 2
Big Data Business Model Maturity Index
Organizations do not understand how far big data can take them from a business transformation perspective. Organizations don't have a way of understanding what the ultimate big data end state would or could look like or answering questions such as:
• Where and how should I start my big data journey?
• How can I create new revenue or monetization opportunities?
• How do I compare to others with respect to my organization's adoption of big data as a business enabler?
• How far can I push big data to power – even transform – my business models?
To help address these types of questions, I've created the Big Data Business Model Maturity Index. Not only can organizations can use this index to understand where they sit with respect to other organizations in exploiting big data and advanced analytics to power their business models, but the index provides a road map to help organizations accelerate the integration of data and analytics into their business models.
The Big Data Business Model Maturity Index is a critical foundational concept supporting the Big Data MBA and will be referenced regularly throughout the book. It's important to lay a strong base foundation in how organizations can use the Big Data Business Model Maturity Index to answer this fundamental big data business question: “How effective is my organization at integrating data and analytics into our business models?”
Chapter 2 Objectives
• Introduce the Big Data Business Model Maturity Index as a framework for organizations to measure how effective they are at leveraging data and analytics to power their business models
• Discuss the objectives and characteristics of each of the five phases of the Big Data Business Model Maturity Index: Business Monitoring, Business Insights, Business Optimization, Data Monetization, and Business Metamorphosis
• Discuss how the economics of big data and the four big data value drivers can enable organizations to cross the analytics chasm and advance past the Business Monitoring phase into the Business Insights and Business Optimization phases
• Review lessons learned that help organizations advance through the phases of the Big Data Business Model Maturity Index
Introducing the Big Data Business Model Maturity Index
Organizations are moving at different paces with respect to where and how they are adopting big data and advanced analytics to create business value. Some organizations are moving very cautiously, as they are unclear as to where and how to start and which of the bevy of new technology innovations they need to deploy in order to start their big data journeys. Others are moving at a more aggressive pace by acquiring and assembling a big data technology foundation built on many new big data technologies such as Hadoop, Spark, MapReduce, YARN, Mahout, Hive, HBase, and more.
However, a select few are looking beyond just the technology to identify where and how they should be integrating big data into their existing business processes. These organizations are aggressively looking to identify and exploit opportunities to optimize key business processes. And these organizations are seeking new monetization opportunities; that is, seeking out business opportunities where they can
• Package and sell their analytic insights to others
• Integrate advanced analytics into their products and services to create “intelligent” products
• Create entirely new products and services that help them enter new markets and target new customers
These are the folks who realize that they don't need a big data strategy as much as they need a business strategy that incorporates big data. And when organizations “flip that byte” on the focus of their big data initiatives, the business potential is almost boundless.
Organizations can use the Big Data Business Model Maturity Index as a framework against which they can measure where they sit today with respect to their adoption of big data. The Big Data Business Model Maturity Index provides a road map for helping organizations to identify where and how they can leverage data and analytics to power their business models (see Figure 2.1).
Figure 2.1 Big Data Business Model Maturity Index
Organizations tend to find themselves in one of five phases on the Big Data Business Model Maturity Index:
• Phase 1: Business Monitoring. In the Business Monitoring phase, organizations are applying data warehousing and Business Intelligence techniques and tools to monitor the organization's business performance (also called Business Performance Management).
• Phase 2: Business Insights. In the Business Insights phase, organizations aggressively expand their data assets by amassing all of their detailed transactional and operational data and coupling that transactional and operational data with new sources of internal data (e.g., consumer comments, e-mail conversations, technician notes) and external data (e.g., social media, weather, traffic, economic, data.gov) sources. Organizations in the Business Insights phase then use predictive analytics to uncover customer, product, and operational insights buried in and across these data sources.
• Phase 3: Business Optimization. In the Business Optimization phase, organizations build on the customer, product, and operational insights uncovered in the Business Insights phase by applying prescriptive analytics to optimize key business processes. Organizations in the Business Optimization phase push the analytic results (e.g., recommendations, scores, rules) to frontline employees and business managers to help them optimize the targeted business process through improved decision making. The Business Optimization phase also provides opportunities for organizations to push analytic insights to their customers in order to influence customer behaviors. An example of the Business Optimization phase is a retailer that delivers analytic-based merchandising recommendations to the store managers to optimize merchandise markdowns based on purchase patterns, inventory, weather conditions, holidays, consumer comments, and social media postings.
• Phase 4: Data Monetization. The Data Monetization phase is where organizations seek to create new sources of revenue. This could include selling