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Organizational Analytics Adoption | @BigDataExpo #BigData #DataLake #Analytics

If history is any guide, we know that it takes a long time for some technologies to gain widespread cultural adoption

Organizational Analytics Adoption: A Generation Away?

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).

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).

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

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.

The post Organizational Analytics Adoption: A Generation Away? appeared first on InFocus Blog | Dell EMC Services.

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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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