Big Data

5 Questions that Define Your Digital Transformation

Bill Schmarzo By Bill Schmarzo CTO, Dell EMC Services (aka “Dean of Big Data”) July 24, 2017

I recently had the opportunity to give a 10-minute keynote at DataWorks Summit 2017.  I know what most of you are thinking: Schmarzo can barely introduce himself in 10 minutes! What sort of keynote could he give in just 10 minutes?  And to be honest, I too struggled with what to say.

But after some brainstorming with my marketing experts (Jeff Abbott, Erin Banks, and Chris Hill), we came up with an idea:  Pose 5 questions that every organization needs to consider as they prepare themselves for digital transformation.  And while I didn’t have enough time in 10 minutes to answer those questions in a keynote, I certainly do in a blog!

So here we go!!

Question #1:  How effective is your organization at leveraging data and analytics to power your business model?

The Big Data Business Model Maturity Index was developed to help organizations to measure or benchmark their effectiveness in exploiting big data and advanced analytics to power their business models.

  • Advance beyond the Business Monitoring stage where organizations are using data and analytics (data warehouses and Business Intelligence) to monitor or report on what has happened
  • Transition to Business Insights (to predict what’s likely to happen) and Business Optimization (to prescriptive specific actions based upon those predictions)
  • Move into the Insights Monetization (not Data Monetization) phase where organizations monetize the insights that they have gleaned about their customers, products, operations and markets.

For more details, see the following blogs:

Question #2:  Do you understand your organization’s key business initiatives and how they benefit from big data?

If you want to deliver business value with your big data initiative, then it only makes sense to focus on what’s important to the business. That means you need to invest the time to understand the organization’s key business initiatives.

  • A business initiative is a cross-functional mandate on which the organization has been goaled to address, often within the next 9 to 12 months.
  • Once you have identified a key business initiative, then work with the key business stakeholders (those business functions that either impact or are impacted by the business initiative) to identify, group and prioritize the decisions that the business stakeholders need to make in support of the business initiative.
  • Consider the questions that must be answered in order for those stakeholders to make those decisions, and identify the data sources that might be useful to the data science team for answering those questions.
  • Identify the supporting architectures and enabling big data technologies.
Figure 2:  Start with the Organization’s Business Initiatives

Figure 2:  Start with the Organization’s Business Initiatives

For more details, see the following blogs:

Question #3:  Do you have business stakeholder active participation in setting your use case roadmap?

It’s critical to the success of your big data initiative to gain business stakeholder support and guidance on day one.

  • Think “Business First” by identifying, validating and prioritizing the use cases that support the targeted business initiative. Focus on the “4 M’s of Big Data” (Make Me More Money) and not on the “3 V’s of Big Data” (whatever they are).
  • Set up facilitated envisioning workshops to brainstorm with the business stakeholders the data sources that might yield better predictors of performance; that is, what business questions might we be able to answer in support of the key decisions, with various combinations of data
  • Prioritize the use cases and start by focusing on a single or couple of use cases. If you don’t “Prioritize and Focus”, you will be forced to embrace a “Technology First” approach in order to try to satisfy all the use cases, which means that you’ll likely solve none of the use cases.

 

Figure 3: Build Active Business Stakeholder Participation

For more details, see the following blogs:

Question #4:  Do you understand the economic value of your data and how that affects your technology and business investments?

Data is unlike any asset that we have ever seen before. There is no asset on your financial books that act or behave like data. Unfortunately, organizations have struggled to come up with an approach that captures the true value of the data.

  • Traditional accounting with cost-based valuation minus depreciation for business assets is the wrong approach. That values assets on historical value; the value that you paid for them, and the value goes down as the asset ages, or is “consumed”.
  • We needed a forward-looking valuation technique. We are embracing a methodology that uses a combination of economics (Data Multiplier Effect) and Data Science to determine the future or predictive value of data in optimizing the business use cases.
  • “Data is the New Oil” over-simplifies the dynamic value of data. Instead, imagine having a single barrel of oil that could be used to fuel an infinite number of vehicles…and it never depletes. That’s data!
Figure 4:  Determining the Economic Value of Data

Figure 4:  Determining the Economic Value of Data

For more details, see the following University of San Francisco research paper:

Question #5:  Do you understand how to create a platform that exploits the economic value of your data?

Having this valuable data asset does no good if one can’t create an environment that exploits its potential.

  • Create a “collaborative value creation” platform that nurtures the on-going collaboration between the business and the data science team.
  • Implement Data-as-a-service and ensure that proper data management services (e.g., data governance, metadata management, security, privacy, cataloging, indexing) are implemented to preserve and propagate the value of the organization’s data.
  • Build Analytic Profiles to ensure that the resulting analytics can be operationalized, and that the organization can extend the usefulness and value of the analytics across multiple use cases.
Figure 5:  Creating the Collaborative Value Creation Platform

Figure 5:  Creating the Collaborative Value Creation Platform

For more details, see the following blogs:

Summary

That’s it, just 5 questions. Addressing these 5 questions will ensure that your organization is exploiting the value of data and the power of analytics to guide your organization’s digital transformation.

Figure 6: 5 Questions to Guide Your Digital Transformation

Figure 6: 5 Questions to Guide Your Digital Transformation

You can also check out a video of my DataWorks Summit keynote presentation (enjoy my air guitar at the end!).

Bill Schmarzo

About Bill Schmarzo


CTO, Dell EMC Services (aka “Dean of Big Data”)

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 Dell EMC’s Big Data Practice. 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.

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2 thoughts on “5 Questions that Define Your Digital Transformation

  1. I love Bill’s key points about Big Data and the role it plays from an organizational perspective, there is one element that wasn’t addressed. Not only is it important to make use of data as a corporate asset and have a strategy for doing so, but it is equally important to have a plan in place to govern data access. This includes ownership, access, movement and monitoring for anomalous use of that data (moving to a cloud storage without permission as an example). Breaches today are not about simple access – it is about the harvesting and misuse of corporate data up to and including extortion (see HBO breach). Data Access Governance should be part of the Big Data strategy. To use it without a plan for protecting is asking for potential problems.

    • Hey Mary, totally agree. There’s an interesting by-product of the focus on quantifying the economic value of an organization’s data – once organization’s realize how valuable their data is, then they are finally ready to spend the money and create the governance capabilities to ensure that the data is accurate, complete, timely and properly governed. It finally gives organizations the incentive to properly invest in their data assets.

      In my years running data warehouse projects, I could never get anyone to pay for governance work. Very few companies thought of their data as an asset to be captured, harvested, exploited and governed. Instead they treated their data as a cost to be minimized (which is why you say aggregated data in data warehouses and not the detailed, granular transactional data).

      But maybe finally folks are realizing the data is a unique corporate asset that’s worthy of proper management and governance.

      Thanks again Mary for your contribution!