8 min read
June 29, 2016
Last updated: September 4, 2019
Bill Schmarzo, author of “Big Data: Understanding How Data Powers Big Business”
and “Big Data MBA: Driving Business Strategies with Data Science,
” is CTO for EMC Global Services Big Data Practice
, responsible for setting strategy and defining Big Data service offerings and capabilities. He’s written several white papers, is an avid blogger and is a frequent speaker on the use of Big Data and data science to power the organization’s key business initiatives.
You are known for the development of the Big Data Business Model Maturity Index (BDBMMI). Can you please explain the theory behind BDBMMI?
There were three driving factors behind the BDBMMI:
- Organizations lacked a benchmark against which to measure how effective they were at leveraging data and analytics to drive or power their businesses,
- Organizations did not know what the Big Data end point might look like from a business and organizational perspective, and
- Organizations lacked a roadmap to get from where they were today, to where they wanted to be from a Big Data business strategy perspective.
So the BDBMMI was born to help address those issues. And over hundreds of customer discussions and engagements, I have continued to refine the BDBMMI stages and the BDBMMI guide for how an organization can advance from one stage to the next stage.
Businesses have to take big risks. Can you elaborate on the traps in decision making and how Big Data can help reduce risks?
Organizations suffer from many Big Data decision traps. Here are two that jump to mind:
Big Data often leads to significant business transformation. How can managers choose the right metrics to evaluate Big Data management?
- Organizations do not need a Big Data strategy; instead they need a business strategy that incorporates Big Data. Probably the biggest decision trap is for organizations to think that Big Data is about technology, when in fact the real Big Data opportunity is understand how to leverage Big Data to optimize key business processes, uncover new monetization opportunities and create a more compelling customer engagement. Begin with an end in mind, as Stephen Covey said, otherwise the organization risks turning their Big Data journey into a science experiment.
- Business leaders need to embrace analytics as a business discipline – not another technology that gets flipped over to IT. The business stakeholders need to understand that Big Data is about the business, and that unless the business stakeholders learn to embrace analytics as a business discipline, they risk having their business models disrupted and customer relationships disintermediated by organizations who have garnered superior customer, product and operational insights from their data.
Part of the challenge for an organization trying to “choose the right metrics” starts with focusing less on metrics that measure what happened (descriptive metrics), and more on metrics about what is likely to happen (predictive metrics). This is key if an organization wants to transition from having retrospective, rear-view mirror view of the business, to an organization that has a more forward-looking, predictive view of where the business is going. And this is where data science kicks in!
“Data science is about identifying those variables and metrics that might be better predictors of performance.” Who within the organization is best qualified to identify those variables and metrics? The business stakeholders. Who is best qualified to actually determine which variables and metrics are better predictors of performance? The data science team. Consequently, the data science team and the business stakeholders must collaborate in order to drive business relevance and value creation.
By the way, the most important word in that data science definition is the word “might.” The word “might” is a license to be wrong; a license to explore new variables and metrics without having to worry whether they are right or not. If an organization does not have enough “might” moments, then won’t have any breakthrough moments.
What technologies and analytic tools would you recommend to our readers who want to leverage Big Data?
I am obviously a big fan of open source technologies like Hadoop, Spark, HAWQ, HBase, Hive, R, and Mahout not because of what they are, but because of what I can do with them.
We tell our clients to think about these open source technologies as being disposable; that you might want to move from one technology to another based upon what you are trying to do with the analytics and the data. In the end, the data and the analytics will survive. The business value does not reside in the technology.
EMC has some marvellous products engineered to help our clients along their Big Data journey including Isilon and Pivotal. We have also developed a Business Data Lake that serves as the foundation for many of our Big Data engagements.
Our last question is about your favourite resources about Big Data. What would you suggest to our readers who want to learn more about it?
Man, there are lots of wonderful resources out there. Here are a couple of my “go to” sites:
I have also written two books on Big Data that I use as part of my teaching at the University of San Francisco School of Management:
Finally, sign up for some of the Big Data and data science mailing lists and attend some webinars and local meet ups on any of a multitude of Big Data topics. There are lots of really smart folks sharing lots of really valuable data. Jump into the water. It’s fine!
Thank you for the interview.