Big Data & Analytics Heroes

David M. Lawson

Co-Founder & CEO, NewSci, LLC.

“As you dig deeper into unstructured data where feelings, emotions and other often messy information reside, you have to embrace qualitative metrics which don’t fit neatly into traditional quantitative analysis,” says David M. Lawson, co-founder & CEO of NewSci, LLC and this week’s Big Data & Analytics Hero.

What were the biggest challenges with getting started with big data and analytics?

The first challenge was overcoming the disbelief that technology works as advertised. This disbelief is fueled by years of costly, delayed and too often failed attempts at integrating data applications. A close second are organizational silos, which can turn the promise of bringing all of an organization’s data together into a political quagmire.

How did you get organizational support for your big data initiatives?

Job one was proving the technology could perform, so we used proofs-of-concept to show skeptics our platforms’ capabilities using their own data. We have two approaches to silos: If we are able to get the attention of the C-suite then we can make our case for a cross-organizational approach; if not, we identify the silo with forward thinking leadership and make them a success which attracts the attention of the C-suite, and the other silos.

What is the market still missing for big data and ­analytics to really deliver ROI?

Fully embracing the possibilities of big data is difficult for many because we have all been conditioned to accept the limitations of traditional technology. This too often leads to underutilization of big data platforms, using them only to make incremental improvements in existing processes. What is needed are new questions, and new actions based on the new insights in order for the full ROI of to be realized.

Should tomorrow's generation acquire analytic skills no matter the degree? Why or why not?

Big data analysis requires a level of creativity not always found in the left-brain dominated data science world. As you dig deeper into unstructured data where feelings, emotions and other often messy information reside, you have to embrace qualitative metrics which don’t fit neatly into traditional quantitative analysis. Data artist is perhaps the better term for someone who can be successful with big data analytics. Data artists will have a strong foundation in data science, an understanding of human behavior, the ability to think creatively and a willingness to truly understand what the users of their work are trying to accomplish.


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