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The New Normal: Big Data Business Model Disintermediation and Disruption By @Schmarzo | @BigDataExpo #BigData

Across multiple industries, startups are coupling new big data technologies and new sources of data with advanced analytics

On January 4, General Motors announced a $500 million investment in Lyft, the #2 player in the rapidly growing ride sharing market. To quote the press release[1]:

“We see the future of personal mobility as connected, seamless and autonomous,” said GM President Dan Ammann. “With GM and Lyft working together, we believe we can successfully implement this vision more rapidly.”

John Zimmer, president and co-founder of Lyft, said: “Working with GM, Lyft will continue to unlock new transportation experiences that bring positive change to our daily lives. Together we will build a better future by redefining traditional car ownership.”

But Lyft is not alone in challenging existing business models.  Here are some other organizations that are challenging long-held business models:

  • Uber: The world’s largest taxi company owns 0 taxis
  • Airbnb: The largest accommodation provider does not own real estate
  • TripAdvisor: The world’s largest travel company owns 0 inventory
  • Skype, Whatsapp, WeChat: The largest phone companies do not own any telco infrastructure
  • SocietyOne: The fastest growing bank has no actual money
  • Amazon: The world’s most valuable retailer has no inventory
  • Apple & Google: The largest software vendors don’t write the apps
  • Facebook: The most popular media owner does not create content
  • Netflix: The world’s largest movie house does not own any cinemas

Across multiple industries, startups are coupling new big data technologies and new sources of data with advanced analytics (data science) to uncover new customer, product, operational and market insights in order to disintermediate existing customer relationships and disrupt existing business models (see Figure 1).

Business_Model_Disintermdeiation and Disruption

Figure 1:  Business Model Disruption

These startups are being successful because they are more effective at leveraging big data technologies, new sources of customer, product and operational data and advanced analytics (data science) to:

  • Disintermediate existing customer relationships. Startups are leveraging new digital platforms (websites, mobile devices, wearables) to step between organizations and their existing customers to provide new sources of value to customers and deliver a more compelling and differentiated user experience.  One of my favorites is Mint.  Mint provides a digital app that pulls together all of my credit card, loan and banking data into a single interface, and then applies predictive and prescriptive analytics to uncover areas where I can optimize my spend and investments.  Customer relationship disintermediation should scare any organization that treats their customers with disdain and contempt (you know who you are airline companies!!).
  • Disrupt existing business models. Startups are leveraging big data technologies and data science to uncover new customer, product, operational and market insights that can be used to:
    • Optimize key business processes (customer acquisition, cross-sell/up-sell optimization, employee retention, advocacy development, predictive maintenance, marketing spend optimization, etc.)
    • Uncover new monetization opportunities (new products, services, markets, audiences, channels, partners, etc.)

These startups are the most dangerous of competitors because they are playing with house (Venture Capital) money and are not afraid to try something and fail – as long as they fail fast and the failure carries a lesson for the next iteration.

Customer relationship disintermediation and business model disruption is coming to any industry where:

  • There is a lot of money
  • The market is too fragmented to provide a complete customer solution
  • Customer experiences are unsatisfactory, or they actually just plain suck
  • Outcomes are questionable, or downright bad
  • “Legacy thinking” permeates the executive team

The Debilitating Impact of Legacy Thinking
Probably the biggest threat to incumbent organizations is the creativity destroying impact of “legacy thinking.”

“Legacy thinking” is believing that only senior management can have the best ideas

Organizations that do not know how to nurture the organization’s creative thinking will fail to realize the business potential of big data.  Organizations need to encourage and accept that many of the organization’s best ideas may actually come from front-line employees and managers.

To avoid being disintermediated and disrupted, organizations need to adopt a “think like a data scientist” mentality where all ideas from all parts of the organization are worthy of consideration.  That does not mean that all ideas are good.  Heck, many of them may not be good and some may just outright suck, but by adopting a culture that is willing to consider all ideas, leaders can unleash the creative thinking of the organization.

This may not please the HIPPO’s (Highest Paid Persons Opinion) of the organization, but tomorrow’s successful organizations will learn to nurture the creative thinking of the organization and not just rely on the few business leaders to generate all the best ideas.

Big Data Business Model Maturity Index
I have talked countless times about the Big Data Business Model Maturity Index (BDBMI) as a measure of how effective your organization is at leveraging data and analytics to power your business models (see Figure 2).

Big Data Business Model Maturity Index

Figure 2:  Big Data Business Model Maturity Index

The BDBMI measures how effective your organization is at leveraging data and analytics to power your business models by guiding organizations through the following business phases.

  • Business Monitoring: Most organizations are stuck in the Business Monitoring phase (the classic business intelligence and data warehouse phase) and struggle cross the “analytics chasm” to transform into a real-time, predictive organization.  But the economics of big data (where it is 20x to 50x cheaper to store, manage and analyze data using big data technologies that traditional data warehousing technologies) enables organizations to “think differently” about how they apply data and analytics to their key business processes.  In particular, the “economics of big data” enable organizations to:
    • Complete access to the complete history operational and transactional data at the individual customer, employee or agent level (investor, broker, financial advisor, money manager)
    • Access to external (e.g., social media, news feeds, weather, traffic, building permits) and internal (e.g., consumer comments, email conversations, physician notes, work orders, clinical studies) unstructured data at the individual level
    • Right-time analysis to identify customer situations worthy of action in a more timely manner
    • Predictive analytics to uncover areas of “unusualness” (or insights) at the individual level
  • Business Insights: When organizations are leveraging the wealth of predictive analytics, data mining and machine learning tools to uncover customer, product and operational insights across the wealth of data that might be worthy of investigation, organizations have successful leaped over the analytics chasm to move into the Business Insights phase.
  • Business Optimization: Build prescriptive analytics to deliver recommendations to investors and front-line employees (think Netflix, Amazon, Pandora)
  • Monetization: Leverage insights being uncovered about investors, financial products and markets to create new monetization opportunities
  • Metamorphosis: Integrate your business models into your customers’ “business” models (purchasing a home, paying for college, retiring)

Big Data Vision Workshop
For anyone looking to take action on moving up the maturity curve, EMC Global Services offers the Big Data Vision Workshop (BDVW), that I developed to enable organizations to identify where and how to leverage data and analytics to become one of these market disrupters.  The BDVW is a 3 to 4 week engagement that identifies how data and analytics can power the organization’s key business initiatives by determining a high-value analytics use case upon which the customer can act quickly. Send me a comment if you want more information!

Summary
In the movie “Annie Hall,” Woody Allen’s character (Alvy Singer) makes the following statement:

“A relationship, I think, is like a shark. You know? It has to constantly move forward or it dies. And I think what we got on our hands is a dead shark.”

Organizations must constantly move forward – in strengthening their customer relationships and expanding/optimizing their business models – or risk dying.  Big data (big data technologies and new sources of customer, product and operational data coupled with data science) provides the fuel for improving your customer relationships and re-wiring your business models.  Because if you don’t constantly move your customer relationships and business models forward, what you got on your hands is a dead shark (business).

[1] “GM and Lyft to Shape the Future of Mobility” January 4, 2016

The New Normal: Big Data Business Model Disintermediation and Disruption
Bill Schmarzo

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More Stories By William Schmarzo

Bill Schmarzo, author of “Big Data: Understanding How Data Powers Big Business”, is responsible for setting the strategy and defining the Big Data service line offerings and capabilities for the EMC Global Services organization. As part of Bill’s CTO charter, he is responsible for working with organizations to help them identify where and how to start their big data journeys. He’s written several white papers, avid blogger and is a frequent speaker on the use of Big Data and advanced analytics to power organization’s key business initiatives. He also teaches the “Big Data MBA” at the University of San Francisco School of Management.

Bill has nearly three decades of experience in data warehousing, BI and analytics. Bill authored EMC’s Vision Workshop methodology that links an organization’s strategic business initiatives with their supporting data and analytic requirements, and co-authored with Ralph Kimball a series of articles on analytic applications. Bill has served on The Data Warehouse Institute’s faculty as the head of the analytic applications curriculum.

Previously, Bill was the Vice President of Advertiser Analytics at Yahoo and the Vice President of Analytic Applications at Business Objects.

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