De-mystifying the Big Data Business Model Maturity Index

I know that I’ve hit the big (data) time when concepts that I developed start to appear as infographics. Today I am very proud to announce the launching of the demystified Big Data Business Model Maturity Index (BDBMMI) infographic!

Through the usage of clear, simple language paired with practical examples that illustrate each stage of the big data maturity journey, the goal of this infographic is to demystify the BDBMMI – to make it easier for customers (and readers) to understand what the BDBMMI is, and how to use it to successfully leverage data and analytics to power their business models.

Figure 1: Big Data Business Model Maturity Index Infographic

Today you’ll learn more about Dave’s story and how he navigated through the stages of the Big Data Business Model Maturity Index and put big data to work for his organization.

Before we get started – I’d like you to meet Dave. Dave is a manufacturing executive in the automobile industry. Dave pursued the opportunity to transform his business through more effective use of data and analytics. He saw the opportunities to apply data science to the data that his business had collected throughout the years, and to take actions to optimize the company’s key business processes, create new revenue opportunities and enter new markets. Besides that, Dave likes to eat burritos, drink chai lattes and to go for walks with his dog.

Big Data Business Model Maturity Index

Not only does the BDBMMI provide a benchmark that compares an organization to others in the use of data and analytics, but equally important, the BDBMMI provides a roadmap to guide organizations to be more effective at exploiting data and analytics to optimize key business processes, uncover new monetization opportunities and create a more compelling, more prescriptive user experience.

The Big Data Business Model Maturity Index is comprised of 5 stages, all of which Dave’s company has gone through:

1. Business Monitoring. Sometimes called Business Performance Management, Business Monitoring is the phase where companies are deploying Business Intelligence (BI) and data warehousing products to monitor on-going business performance.At that stage, Dave would use basic reports and dashboards to monitor on “what happened?” about the business. Among a million other questions, he asked himself:What are my best selling cars?

1. Who is buying those cars?
2. What were sales last year?

Many organizations are stuck in the Business Monitoring stage. Organizations have struggled to leverage BI and data warehousing technologies to become more real-time, more predictive, more prescriptive and eventually more disruptive with their analytics. To move beyond the Business Monitoring stage, organizations need to exploit the “economic drivers” of big data analytics, to include:

• Access to the complete, detailed history of operational and transactional (structured) data at the level of the individual human and/or product, including all orders, sales, payments, returns, maintenance, servicing, etc.
• Leverage the growing wealth of unstructured data sources, found internally in the organization (e.g., consumer comments, email conversations, work orders, physician notes, clinical studies) as well as publicly available data sources (e.g., traffic, weather, social feeds, home values, patent filings, voter registration) to identify those variables and metrics that might be better predictors of business performance.
• Embrace “right time” analytics to identify changes in customer, employee, product and device behaviors that provide an opportunity to intervene (e.g., location-based marketing, fraud detection, predictive maintenance, network optimization).
• Exploit the power of predictive analytics (including data mining, machine learning and cognitive computing) to predict changes in the behaviors, tendencies, patterns and trends of customers, products, operations and markets that can be used to optimize key business processes.

2. Business Insights. This phase takes Business Monitoring to the next level by applying predictive analytics (including practices such as machine learning, data mining and cognitive computing) to the growing wealth of organization data – including granular transactional and operational data (e.g., point of sales, call detail records, ecommerce, credit card transactions, prescriptions, e-payments), internal unstructured data sources (e.g., consumer comments, email threads, work orders, physician notes, clinical studies), and publicly available data (e.g., social media, traffic, weather, home values, tax records, patent filings, college donations) – to uncover potential insights buried in the data that might be useful to the business.

After monitoring the business, Dave realized that he had lots of untapped data, both internally generated as well as data from partners and publicly available data. Dave reached the Business Insights phase when he used data and analytics to become aware of the following insights about a key demographic for his business:

• The group with the highest propensity to buy fully featured hybrid cars consists of millennials who are online gamers and have the most current smartphones.

3. Business Optimization. The Business Optimization phase is where organizations use prescriptive analytics to make actionable recommendations to employees (e.g., physicians, teachers, parole officers, store managers, mechanics, technicians), customers and/or machines/devices (e.g., wind turbines, jet engines, cars, locomotives, washing machines). The specific actions recommended can be taken to improve business performance and optimize the targeted business initiative.

In the course of the last two BDBMMI stages, Dave expected the overall demand for hybrid cars to continue to rise. He also identified the buyers for these cars consisted heavily of tech-savvy young people who constantly carried their smartphones around. This profitable niche certainly valued having a mobile device integration unit in their cars.

With the goal of driving repeat purchases, increasing customer advocacy and improving the customer experience, Dave’s data science team built prescriptive models that came up with the following recommendation:

• We should shift our marketing spend from automobile magazines to gaming magazines and websites to increase sales of hybrid cars to millennials by 12%.

3. Insight Monetization. The Insight Monetization phase is where organizations are leveraging insights about their key business entities (e.g., customers, products, employees, stores, plants, suppliers) to identify “white spaces” or under-served, un-served or un-met market that yield new monetization opportunities (e.g., new products, new services, new partners, new channels, new markets).

Dave leveraged the behavioral insights gathered about his current customers and their product usage patterns to introduce a new innovative car feature that enables LED dashboards to mirror the customers’ mobile devices through Bluetooth connectivity.

Dave was able to leverage the insights about customer usage behaviors and car driving and performance patterns to create new monetization or revenue opportunities such as:

• Offering the new screen-mirroring feature as an add-in option to the company’s cars.

5. Business Metamorphosis. This phase is the ultimate goal for organizations that want to leverage their superior customer, product, operational and market insights to create a platform that enables third-party developers to build value-added products and services upon the customer’s analytics platform. It also includes the integration of data and analytic-based thinking into the culture of the organization, and can impact how employees are hired, paid, promoted, and managed. It might even change the metrics that the business reports to Wall Street in an effort to highlight the organization’s new value creation metrics.

Supported by his customer, product and market insights, Dave embarked on a strategy to license the mirroring capability into new markets such as aviation, railroad and hospitality, supported by a SaaS business model.

The result of Dave’s new mobile device integration business was an entire ecosystem built on top of his analytics-driven product development that enabled him to expand past auto manufacturing into new markets with new business models. Third-parties are now set to prosper with value-add services and accessories – firmly embedding Dave’s business into the models of his customers, partners and end users.

Big Data Business Model Maturity Index Roadmap

What clients need after they ascertain where they are and where they want to be on the BDBMMI, is a roadmap to help them advance from stage to stage. Figure 2 provides some recommendations as to what organizations can do to progress up the BDBMMI.

Figure 2: Big Data Business Model Maturity Index Roadmap

The big data journey was great for Dave and his company. Applying the power of data science to the data gathered throughout Dave’s big data journey, Dave’s team was able to gather detailed insights into the behaviors, tendencies and interests of his customers, dealers and even performance behaviors and tendencies from the cars themselves. Dave was able to exploit these superior customer, dealer and product insights to:

• Optimize key business processes
• Improve the customer experience
• Create new revenue opportunities

The Big Data Business Model Maturity Index can be a critical tool for customers who are trying to figure out where and how they can exploit big data for business benefits. It challenges our customers around a very simple but powerful question:  How effective is your organization at leveraging data and analytics to power your business models? Hopefully this infographic will help your organization to answer that all-important question.

For more information about how we help our clients progress up the BDBMMI, check out the Big Data Vision Workshop and how it helps organizations to identify where and how to deploy big data analytics to power their business models.

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Thanks for taking the time to read my post. I’m fortunate that I spend most of my time with very interesting clients which fuel many of my topics. I hope that you are able to leave a comment or some thoughts about the blog. If you would like to read my regular blogs, please follow me on LinkedIn and/or Twitter.

In case you are interested, here are some of my favorite posts:

·     Determining the Economic Value of Data

·     The Big Data Intellectual Capital Rubik’s Cube

·     How to Avoid “Orphaned Analytics” 

·     To Achieve Big Data’s Potential, Get It Into The Boardroom

·     Vision Workshop

·     Big Data Business Model Maturity Index (animation)

·     How I’ve Learned To Stop Worrying And Love The Data Lake

I am the author of two Big Data books: “Big Data: Understanding How Data Powers Big Business” and “Big Data MBA: Driving Business Strategies with Data Science”.   I also teach the "Big Data MBA" at the University of San Francisco (USF) School of Management, where I was named the School of Management’s first Executive Fellow. The opportunity to teach at USF gives me the perfect petri dish to test new ideas and concepts both in the classroom and in the field with clients.