Big Data

Organizational Analytics Adoption: A Generation Away?

Bill Schmarzo By Bill Schmarzo CTO, Dell EMC Services (aka “Dean of Big Data”) February 20, 2017

A recent article titled “We Are Likely 3-5 Years Out From Advanced Analytics Being Critical To The Viability Of A Company” (and I thought my titles were too long) interviewed Walter Storm, the Chief Data Scientist at Lockheed Martin. The article offers some great perspectives such as:

“There’s also a culture shift required – moving from experience and knee-jerk reactions to immersion and exploration of rich insights and situational awareness.”

However, Mr. Storm believes that we are only 3 to 5 years away from advanced analytics being critical to the viability of a company:

“We are at a point where data-driven decisions may still offer companies a competitive advantage, however we are likely 3-5 years out from advanced analytics being table stakes and critical to the viability of a company to even remain in business.”

I think 3 to 5 years is overly optimistic.  While I think we will see spot adoption inside organizations (e.g., fraud detection in financial services, attribution analysis in digital media, predictive maintenance in manufacturing, inventory optimization in retail and consumer package goods, capacity planning in Telco), I am dubious that entire organizations will wholesale adopt analytics to power their business models.  If this were only a technology adoption issue, then I’d totally agree.  Unfortunately what I am finding across a wide variety of customer conversations is that analytics adoption is not a technology challenge; it’s a cultural challenge.  And because it is a cultural challenge, I fear that it’ll take a new generation of business leaders who have been trained to move away from gut and experience as the basis for critical business decisions, towards data and analytics that guide their critical and non-critical decisions.

Cultural Adoption:  A History Lesson

If history is any guide, we know that it takes a long time for some technologies to gain widespread cultural adoption.  The great visualization below, created by Nicholas Felton of the New York Times, shows how long it took various categories of products, from electricity to the Internet, to achieve different penetration levels in US households (see Figure 1).

Consumption Graph

Figure 1:  History of Technology Adoption

For example, it took decades for the telephone to be adopted by 50% of households in America.  And while it may seem that technology adoption is speeding up, I am fearful that wholesale organizational change in how organizations make decisions takes much longer.  In fact, I believe that it may require a whole new generation of business leaders who have been trained in the power of data and analytics to power their business models.

We at Dell EMC Services believe that to accelerate the generational change, organizations need to change the frame of the data and analytics conversation; that organizations need to understand:

Organizations do not need a big data strategy; they need a business strategy that incorporates big data.

While that may seem like a very simple statement, it reframes the conversation away from a technology adoption discussion to a business model transformation mandate.

The MIT Sloan Institute recently commissioned a study that is summarized in an article titled “Companies Brace for Decade of Disruption from AI”.  According to the survey – which tracks the views of senior corporate executives on disruptive capabilities ranging from Big Data to artificial intelligence – 46.6% of business executives see disruptive change coming fast.

While nearly half of the business executives fear that their companies are at significant risk of disruption or displacement, many companies do not know how to cross the “Business Model transformation” chasm (see Figure 2).

Big Data Adoption

Figure 2: Business Model Transformation Chasm

It is not a technology challenge that will strangle these companies, but it is the inability to drive organizational, cultural and business model transformation that dooms these organizations.

The Path Towards Business Model Transformation

So what do I recommend that you do to prepare yourself for the Business Model Transformation?  Here are some steps that your organization can take today:

  • How effective is your organization at leveraging data and analytics to power your business models?

As simple and straightforward as that question may be, organizations struggle because their natural tendency is to look at the problem from a technology perspective (and then consequently it is IT’s problem) instead of looking at the problem from a cultural perspective (where it then becomes a business leadership opportunity).

  • Empower the organization. Too many business leaders believe that they are the smartest person in the room, and do very little to empower creative and break-through thinking amongst the people who actually know the business better because they live it every day.  The power of data science is captured nicely in the below description:

Data Science is about identifying those variables and metrics that are better predictors of performance.

We have discovered that the secret to big data and data science success is unleashing the creative thinking of the business users; to charge them with identifying those variables and metrics that might be better predictors of performance, and then allowing the data science team to determine (quantify) which ones are actually better predictors of performance.

Check out the blog “Data Science: Identifying Variables That Might Be Better Predictors” for more details on how we empower the business teams and the potential results.

  • Start with business use cases. Stop starting the transformation challenge by force fitting technology decisions. Instead start the transformation by facilitating a cross-business unit exercise to identify, brainstorm, qualify and prioritize your top priority business use cases (see Figure 3).
Figure 3:  Identify, Validate and Prioritize Top Business Use Cases

Figure 3:  Identify, Validate and Prioritize Top Business Use Cases

Check out the site “Analytics Use Case Identification And Implementation” for more details on the Dell EMC Big Data Vision Workshop approach.

  • Treat Data As A Corporate Asset. Business leadership needs to accept responsibility to treat data and analytics as corporate assets to be maximized and exploited, instead of treating data as someone else’s (IT’s) problem.  This may be one of the organization’s biggest cultural challenges, because most organizations have treated data as a cost to be minimized instead of a source of customer, product, operational and market insights that can be used to optimize key business processes, uncover new monetization opportunities and create a more compelling customer experience.

See the blogs “Determining the Economic Value of Data” and “How to Avoid Orphaned Analytics” for more details on our approach to capturing, refining and monetizing the organizations data and analytic assets:

  • Embrace the Big Data MBA. I am an Executive Fellow at the University of San Francisco School of Management where I teach a class titled the “Big Data MBA.” The underlying premise is that tomorrow’s business leaders will need to adopt analytics as a business discipline versus an activity that is left to IT and the data science teams.   We take our students through a series of exercises to help them understand, or “envision”, the realm of what’s possible from a data and analytics perspective.

As part of the course, we teach the MBA students to “Think Like A Data Scientist”; to embrace the power of “might”; that data science is all about identifying those variables and metrics that might be better predictors of performance.  We teach the students the power of innovative thinking and the gospel of “fail fast / learn faster” as a way to uncover business insights that can optimize key business processes, uncover new monetization opportunities and drive a more compelling customer experience.

If fresh-faced students can do this, then so can line of business executives as long as they are willing to unlearn certain organizational and cultural perspectives (e.g., decisions based upon gut, only the best decisions can come from senior management) and embrace new learnings (“might” is the most important word in your business and your best ideas might come from people at the front-lines of your business).

Remember, as an organization, if you do not have enough “might” moments, you will never have any breakthrough moments.

 

Bill Schmarzo is a CTO, Dell EMC Services (aka “Dean of Big Data”).

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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4 thoughts on “Organizational Analytics Adoption: A Generation Away?

  1. “Organizations do not need a big data strategy; they need a business strategy that incorporates big data.”
    I couldn’t agree more with this insightful statement.

    In fact, I generally agree with most of what you said. I don’t, however, think that it need be so difficult and extraordinarily disruptive.
    It doesn’t need to be forced with the crowbar of Organizational Change Management, it can be insinuated into a program of offering better insights, and offering them in a more accessible, consumable way.

    Executive leadership at most organizations don’t shoot from the hip because they want to, they do so because they’re forced to. They’re aching for better, deeper insights delivered to them in a way that tightly aligns with their current decision making processes – and, ideally, improve them a bit along the way.

    Yes, Business Data Science needs to live within the business (unless your business processes are uncommonly tightly aligned with your IT processes) but that doesn’t necessarily mean the whole kit and caboodle needs to within the business – nor does it mean that Executives need to have a solid understanding of Data Science.

    What is critical is that there is a Business Function that contains talented Data Scientists, deeply experienced Data Visualization professionals, Business Process Engineers, and UI Designers who work closely with UX Management professionals. If this all-star team has a solid grasp of the business, how people work, and what they’re trying to accomplish, they can work with the Executives to re-engineer the business processes, and augment their existing environments to infuse that deeper level of insight directly into their operating environments in a way that is intuitive and non-invasive.

    My view is that Knowledge should be ubiquitous and transparent. For that, it needs to be readily and intuitively available in all tools, where and when it’s needed – rather than be presented in a purpose-built tool.
    I see it a bit like Augmented Reality for business tools.

    • Craig, I could not agree more. From your mouth to my ears. While the organizational structures might continue to morph as we become more expert at integrating data and analytics into more of our key business processes and functions, today I see the optimal data science organizational structure is to have the data science team hardline into the business function, but dotted line into the Chief Analytics Officer (or Chief Data Monetization Officer, if I had my say). The hardline into the business function ensures tight linkage and relevance to the business initiatives. And the dotted line to the Chief Analytics Officer ensures career and skills development.

  2. Very interesting concept. It ties well with Thomas Friedman’s book “Thank You For Being Late.” In it, he has similar adoption slides showing how we are entering the age of accelerations, with Moore’s Law Economics a key influencer.

    So, yes, adoption will take longer than most analysts forecast, but it will be faster than a generational cycle.

    • Geoff, I hope that you’re right about adoption happening faster than a generation, but I can’t believe how many clients I run into who still think Big Data this is an EDW 2.0 conversation, and really don’t grasp the power of predicting (data science) versus reporting (business intelligence).

      Would love to know the level of IT investment in Business Intelligence and Data Warehousing versus level of IT investment in Big Data and Data Science. Is that some information that you might have?