Big Data

Lessons from Game of Thrones: Stopping the White Walkers of Data Monetization

Bill Schmarzo By Bill Schmarzo CTO, Dell EMC Services (aka “Dean of Big Data”) December 13, 2017

As we end 2017, I’m tired of writing “lecturing” blogs about what organizations should be doing to master data monetization in order to power their business models and achieve digital transformation. While the objective of every organization should be to master big data and data science (artificial intelligence, machine learning, deep learning) to drive “data monetization,” let’s take a breath and have some fun.

My recent ankle surgery afforded me the opportunity to binge watch “Game of Thrones.”  As I watched the impending battle between the White Walkers and humanity, I couldn’t help but identify a number of lessons that we can learn from Jon Snow’s battle with the leader of the White Walkers…and the power of Valyrian steel! Game of Thrones and data, not exactly two things you think are in harmony, but this is where I find myself.

Figure 1: White Walker’s “Oh Sh*t” moment when he realizes Jon Snow’s sword is made of Valyrian steel…Oh yea!

 

So let’s grab our favorite Valyrian steel sword and stop the White Walkers from hindering data monetization!

  • “Reporting” White Walker. The “Reporting” White Walker is still fixated on developing reports and dashboards that monitor what happened versus building advanced analytics to predict what’s likely to happen. If I can predict what’s likely to happen, then I can prescribe corrective or preventive actions to either avoid (or prevent) costly situations or uncover new monetization opportunities. Being able to leverage big data with data science to predict what’s likely to happen in order to prescribe corrective actions is the heart of the data monetization process.

See “Using Data Analytics to Prevent, Not Just Report” for more details on monetizing predictive data analytics.

  • “Technology First” White Walker. Almost every organization seems to want to start their data monetization efforts by loading up on technology. But starting with the technology is like pushing a rope. Organizations need to first understand what’s important to the business by identifying use cases and the financial value of those use cases. Then, take the time to understand and envision how big data and data science can enable those use cases. It is the business and operational use cases, and the financial values of those use cases that underpin an organization’s data monetization efforts.

See “Artificial Intelligence is not Fake Intelligence” for more details on the maturity of artificial intelligence and its role in data monetization.

  • “Data Warehouse” White Walker.Announcing that you are going to drive your big data and data science journey via your data warehouse is like declaring you’re going to the moon and then climbing a tree. Yes, it’s true you’re closer, but you can’t get to the moon from the top of a tree. Instead, organizations need to embrace the data lake as the “collaborative value creation” platform that enables the organization’s data monetization efforts. The data lake is the ideal platform for data exploration in order to identify variables and metrics that might be better predictors of business and operational performance.

See “Data Lake Business Model Maturity Index” for more details on how to transform your data lake into a “collaborative value creation” platform.

  • “It’s an IT Problem” White Walker. Business leaders who think data monetization is an IT (Information Technology) function are doomed to march with the army of the dead. The data monetization process starts with, and is hinged on, identifying, validating and prioritizing the organization’s key business and operational use cases.That requires intimate and early business stakeholder involvement to drive that process, and as equally important, to determine the financial ramifications (e.g., return on investment, net present value, internal rate of return) from optimizing those use cases.

See the blog “Use Case Identification, Validation and Prioritization” for more details on stopping the “It’s an IT Problem” White Walker.

  • “No Chief Data Officer” White Walker. You may already know from my previous blogs how much I dislike the “Chief Data Officer” (CDO) title. Many organizations implement the CDO role to offload the data management tasks of the CIO. I prefer to view the CDO as the “Chief Data Monetization Officer” whose primary business responsibility is to identify strategies for maximizing the organization’s data monetization efforts; to lead the organization’s initiatives around integrating data and analytics to optimize the organization’s key business and operational use cases; and determine the associated economic value of those data sources and analytics.

See” Chief Data Officer: The True Dean of Big Data?” for more details on defining the role of the CDO.

  • “Data is a Cost” White Walker. Too many organizations still think 1) data is a cost to be minimized and 2) make it the responsibility of the IT organization to do that. For example, while Hadoop can certainly be used to reduce an organization’s Extract Transform Load (ETL) costs in populating their data warehouse, the real value of the data is in the unique asset characteristics of these digital business assets. These assets never deplete, never wear out, and can be used across an infinite number of use cases at near-zero marginal cost. Organizations don’t have any other assets with those unique and exploitable characteristics. Data isn’t the new oil; Data is the sun!

See “Economic Value of Data (EvD) Challenges” and the associated University of San Francisco research paper for more details on embracing a data science and economics perspective to data monetization.

So grab your Valyrian steel sword and act today to stop these data monetization White Walkers! Otherwise be prepared to enjoy taking orders from a White Walker (and I notice they don’t spend much time at Starbucks or Chipotle).

Winter is coming…

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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