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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
By: William Schmarzo
Mar. 2, 2017 10:00 AM
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
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
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).
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.
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.
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:
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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