Welcome!

@DXWorldExpo Authors: Pat Romanski, Yeshim Deniz, Elizabeth White, Liz McMillan, William Schmarzo

Related Topics: @DXWorldExpo, Open Source Cloud, @CloudExpo

@DXWorldExpo: Article

Big Data Business Model Maturity Index Guide By @Schmarzo | @BigDataExpo #BigData

Helping organizations measure how effective they were at leveraging data and analytics to power their business model

We developed the Big Data Business Model Maturity Index to help organizations measure how effective they were at leveraging data and analytics to power their business models (see Figure 1).

What we hadn’t done until now, though, was to translate this index into a set of recommendations or actions to help organizations advance from one stage to the next.  That’s the objective of this blog (and maybe my next book…God forbid!!!):  to give our clients a checklist of actions to facilitate progressing along the Big Data Business Model Maturity Index.

Figure 1: Big Data Business Model Maturity Index

Understanding The Big Data Business Model Maturity Index
The vast majority of organizations with which I meet are stuck in Phase 1:  the Business Monitoring phase.  In this phase, organizations are using Business Intelligence (BI) and data warehousing tools to monitor the business; providing a retrospective, batch view of what the business has accomplished.  And while this is a critical foundation upon which to build big data capabilities, organizations have learned that they cannot become a real-time, predictive and prescriptive data-driven organization using these BI and data warehouse tools.  Organizations need something more, which is where the Big Data Business Model Maturity Index comes into play.

So what are the steps that an organization needs to accomplish in order to advance itself through the Big Data Business Model Maturity Index?  What actions do organizations need to take in order to become a more real-time, more predictive and prescriptive organization that is capable of monetizing opportunities with Big Data and developing entirely new business models around these new insights?  Organizations need a prescriptive guide (and future Big Data MBA course?) to progress through the Big Data Business Model Maturity Index; to become more effective at leveraging data and analytics to power their business models.

Steps to Progress from Monitoring to Insights
The Insights stage is about coupling the wealth of internal and external data with predictive analytics (and even machine learning) to uncover insights about the organization’s key (internal) business processes, product and service attributes, and/or customer behaviors and sentiments.  Key actions required to transition from the Monitoring to the Insights stage include:

  • Identify Key Business Decisions. Identify and understand the decisions that the key business stakeholders need to make to support an organization’s key business initiatives (e.g., “Reversing financial trading platform market share loss”)
  • Create Analytics Sandbox. Provide an analytics environment that allows the data scientists to rapidly ingest data (as-is; no schema required), explore the data, and test the data for its predictive capabilities in a fail fast environment that includes:
    • Historical detailed operational and transactional data at the most granular (individual investor, adviser or trader) level
    • Internal unstructured data about engagements and conversations (consumer comments, surveys, advisor notes, email conversations, etc.) with individual investors, advisers or traders
    • External unstructured data about individual investors, advisers or traders publicly available activities (social media postings, property values, property taxes, college donations, job promotions)
  • Deploy Predictive Analytics. Leverage predictive analytics and machine learning to mine the above wealth of data to uncover individuals’ relevant behaviors (e.g., trading and engagement tendencies, propensities, preferences, patterns, trends, interests, passions, affiliations, associations, sentiment)
  • Build Analytic Profiles. Create individual Analytic Profiles for the key individuals (e.g., investors, traders, advisors, portfolio managers) that includes basic demographic data (age, income level, education level, number of dependents, etc.) coupled with advanced demographic data (value of home, donations, vacations, travel, age to retirement, etc.) as well as “scores” that support the organization’s key business initiatives (e.g., Customer LTV, FICO, Retirement Readiness, Risk Tolerance, Children’s College Funding, Legacy Giving)
  • Deploy Right-time Analytics. Create a “Right time” analytics capabilities that can monitor individuals’ behaviors (across individuals’ transactions, engagements, events, activities, etc.) to flag behavioral changes or insights that might be worthy of analysis.
  • Train Business Users. Train business users in a process to think creatively (“Thinking Like A Data Scientist”) about identifying data sources, variables and metrics that could potentially be better predictors of business or individual performance
  • Capture Insights. Capture and catalogue the insights that are being uncovered about your key business entities (e.g., customers, products, operations, markets) for review and assessment in the Optimization phase of the Big Data Business Model Maturity Index.

Steps to Progress from Insights to Optimization
The Optimization stage applies prescriptive analytics to the customer, product, operational and market insights to deliver recommendations to front-line employees, partners and customers to improve effectiveness of the organization’s key (internal) business processes.  Key actions required to transition from the Insights to the Optimization stage include:

  • Evaluate Insights Business Relevance. Train business subject matter experts to assess the potential value of each of the customer, product, operational and market insights using the A.M. Test:
    • Are the insights Strategic for the targeted business initiative?
    • Are the insights Actionable?
    • Are the insights Material (i.e., the value of acting is greater than the cost of acting)?
  • Deploy Prescriptive Analytics. For insights that pass the S.A.M. Test, build prescriptive analytics to deliver actionable recommendations to the key business entities (investors, advisers, partners, agents, portfolio managers, etc.) that support the key decisions for the targeted business process
  • Deploy Data Lake. Build a Data Lake that supports rapid data ingestion, data engineering, data exploration and analytic modeling.  Key characteristics of a data lake include:
    • Captures data from a wide range of traditional and new sources as-is (structured and unstructured)
    • Enables you to store all your data in one environment for cross-functional business analysis
    • Enables in-place predictive analytics to uncover new customer, product, and operational insights
    • Enables prescriptive analytics to create recommendations that empower front-line employees and drive more profitable customer engagement
    • Enables the integration of the analytic results (scores, recommendations, rules) into operational and management systems
  • Leverage App Dev. Operationalize the recommendations by leveraging modern app/dev techniques to integrate the results with web-based, mobile app, dashboards, reports.,  This delivers the recommendations, scores and rules in a way that lets the business stakeholders  consume them easily.
  • Measure Decision Effectiveness. Instrument or configure the recommendations in order to determine the effectiveness of the recommendations (i.e., did the recommendations work as predicted).  Use the results of the effectiveness measurements to finetune the analytic models.

Steps to Progress from Optimization to Monetization
The Monetization stage leverages the approach from the Optimization phase to identify and execute on new (external) business opportunities within the context of the organization’s existing business strategy.  Key actions required to transition from the Optimization to the Monetization stage include:

  • Categorize Analytic Insights. Capture and catalogue the captured customer, product, operational and market insights in the data lake and/or analytic sandbox, and then validate the business relevance and business potential of those insights
  • Codify Monetization Opportunities. Decompose short-to-medium term business strategy and key business initiatives for growth to understand what the organization wants to accomplish and how data insight could accelerate those initiatives. Run envisioning exercises with key business subject matter experts (SME’s) to review and assess the value of insights as they relate to growth opportunities.  New business opportunities typically fall into the areas of new products, services, channels, audiences, markets, and/or partners.
  • Prove ROI. Provide a Proof of Value environment where the data science team can collaborate with the business SME’s to determine if the analytics can be turned into new business opportunities (i.e., validate market size and market share potential, determine the analytic lift and range of Return on Investment); determine how would these insights be used to identify and launch new market or revenue initiatives?
  • Operationalize New Products/Services. If there is a compelling ROI and the analytic models can generate the necessary lift, then push the new monetization opportunities to market launch.  Instrument the rollout to monitor the monetization effectiveness and make right-time course corrections.

Steps to Progress from Monetization to Metamorphosis
The Metamorphosis stage leverages the organization’s cumulative insights, data and analytics to create net new components of the business strategy – new business models, new consumption models, new corporate goals, new BHAGs!  Key actions required to transition from the Monetization stage to the Metamorphosis stage include:

  • Create New Customer Consumption Models. Consider your customers’ financial reasons for doing business with you; why and how are your customers making purchasing decisions and how can you improve the effectiveness and timeliness of those decisions.  Create new consumption models (e.g., convert CapEx to OpEx), and additional products/services that deliver connective value.
  • Create Analytics Platform. Extend your analytics platform to incorporate customer-facing interactivity (think GE Predix or Apple App Store) where customers can develop new apps that integrate into their business operations (business models).
  • Enable Third-Party App Developers. Determine how to enable, scale and secure the analytics platform so that third-party application developers can develop, market, sell and support new value-added applications (think Apple App Store and Google Play)

Big Data Business Model Maturity Index Summary
Figure 2 summarizes the steps in the process of becoming more effective at leveraging data and analytics to power the business.

Figure 2:  Big Data Business Model Maturity Index Guide

This is a process that any organization can follow.  However, resist the urge to jump to step 3 or 4 in the process, and resist the urge to just throw technology at the opportunity and hope that everything works out.  Hope is only a strategy if you are in the cosmetics business, but the rest of us need a guide.

By the way, if you are coming to Strata + Hadoop World in San Jose March 19 through March 21, be sure to catch my session “Developing a Big Data Business Strategy” on Wednesday, March 30th at 11:00 – 11:40am in Room: LL21 E/F.  Check out this link for more details: http://conferences.oreilly.com/strata/hadoop-big-data-ca/public/schedule/detail/49020.  See you there!!

The post Big Data Business Model Maturity Index Guide appeared first on InFocus.

More Stories By William Schmarzo

Bill Schmarzo, author of “Big Data: Understanding How Data Powers Big Business” and “Big Data MBA: Driving Business Strategies with Data Science”, is responsible for setting strategy and defining the Big Data service offerings for Hitachi Vantara as CTO, IoT and Analytics.

Previously, as a CTO within Dell EMC’s 2,000+ person consulting organization, he works with organizations to identify where and how to start their big data journeys. He’s written white papers, is an avid blogger and is a frequent speaker on the use of Big Data and data science to power an organization’s key business initiatives. He is a University of San Francisco School of Management (SOM) Executive Fellow where he teaches the “Big Data MBA” course. Bill also just completed a research paper on “Determining The Economic Value of Data”. Onalytica recently ranked Bill as #4 Big Data Influencer worldwide.

Bill has over three decades of experience in data warehousing, BI and analytics. Bill authored the Vision Workshop methodology that links an organization’s strategic business initiatives with their supporting data and analytic requirements. Bill serves on the City of San Jose’s Technology Innovation Board, and on the faculties of The Data Warehouse Institute and Strata.

Previously, Bill was vice president of Analytics at Yahoo where he was responsible for the development of Yahoo’s Advertiser and Website analytics products, including the delivery of “actionable insights” through a holistic user experience. Before that, Bill oversaw the Analytic Applications business unit at Business Objects, including the development, marketing and sales of their industry-defining analytic applications.

Bill holds a Masters Business Administration from University of Iowa and a Bachelor of Science degree in Mathematics, Computer Science and Business Administration from Coe College.

DXWorldEXPO Digital Transformation Stories
René Bostic is the Technical VP of the IBM Cloud Unit in North America. Enjoying her career with IBM during the modern millennial technological era, she is an expert in cloud computing, DevOps and emerging cloud technologies such as Blockchain. Her strengths and core competencies include a proven record of accomplishments in consensus building at all levels to assess, plan, and implement enterprise and cloud computing solutions. René is a member of the Society of Women Engineers (SWE) and a m...
With 10 simultaneous tracks, keynotes, general sessions and targeted breakout classes, @CloudEXPO and DXWorldEXPO are two of the most important technology events of the year. Since its launch over eight years ago, @CloudEXPO and DXWorldEXPO have presented a rock star faculty as well as showcased hundreds of sponsors and exhibitors! In this blog post, we provide 7 tips on how, as part of our world-class faculty, you can deliver one of the most popular sessions at our events. But before reading...
Poor data quality and analytics drive down business value. In fact, Gartner estimated that the average financial impact of poor data quality on organizations is $9.7 million per year. But bad data is much more than a cost center. By eroding trust in information, analytics and the business decisions based on these, it is a serious impediment to digital transformation.
Charles Araujo is an industry analyst, internationally recognized authority on the Digital Enterprise and author of The Quantum Age of IT: Why Everything You Know About IT is About to Change. As Principal Analyst with Intellyx, he writes, speaks and advises organizations on how to navigate through this time of disruption. He is also the founder of The Institute for Digital Transformation and a sought after keynote speaker. He has been a regular contributor to both InformationWeek and CIO Insight...
Bill Schmarzo, Tech Chair of "Big Data | Analytics" of upcoming CloudEXPO | DXWorldEXPO New York (November 12-13, 2018, New York City) today announced the outline and schedule of the track. "The track has been designed in experience/degree order," said Schmarzo. "So, that folks who attend the entire track can leave the conference with some of the skills necessary to get their work done when they get back to their offices. It actually ties back to some work that I'm doing at the University of ...
DXWorldEXPO LLC, the producer of the world's most influential technology conferences and trade shows has announced the 22nd International CloudEXPO | DXWorldEXPO "Early Bird Registration" is now open. Register for Full Conference "Gold Pass" ▸ Here (Expo Hall ▸ Here)
When it comes to cloud computing, the ability to turn massive amounts of compute cores on and off on demand sounds attractive to IT staff, who need to manage peaks and valleys in user activity. With cloud bursting, the majority of the data can stay on premises while tapping into compute from public cloud providers, reducing risk and minimizing need to move large files. In his session at 18th Cloud Expo, Scott Jeschonek, Director of Product Management at Avere Systems, discussed the IT and busine...
The revocation of Safe Harbor has radically affected data sovereignty strategy in the cloud. In his session at 17th Cloud Expo, Jeff Miller, Product Management at Cavirin Systems, discussed how to assess these changes across your own cloud strategy, and how you can mitigate risks previously covered under the agreement.
CloudEXPO New York 2018, colocated with DXWorldEXPO New York 2018 will be held November 11-13, 2018, in New York City and will bring together Cloud Computing, FinTech and Blockchain, Digital Transformation, Big Data, Internet of Things, DevOps, AI, Machine Learning and WebRTC to one location.
The Internet of Things will challenge the status quo of how IT and development organizations operate. Or will it? Certainly the fog layer of IoT requires special insights about data ontology, security and transactional integrity. But the developmental challenges are the same: People, Process and Platform and how we integrate our thinking to solve complicated problems. In his session at 19th Cloud Expo, Craig Sproule, CEO of Metavine, demonstrated how to move beyond today's coding paradigm and sh...