Wow! This entire week analytics and data science has popped up in nearly every conversation I have had. I had a call with someone asking me if there was an analytics tools on the market that could surface deeper financial data than what current public records access allowed.
I’ve read a multitude of articles about analytics applications in the legal industry from TAR to Knowledge Management. I also read an interesting article about data scientists as the “hot job” that explored how journalists and information professionals could expand their roles to collaborate with data scientists.
Jinfo surfaced some thoughtful questions about the role of researchers with analytics in this blog post. Defining the role of the information professional in light of the evolving world of analytics and data science isn’t impossible but it does require some deep thinking. I’m still processing what others are saying from both my recent reading and conversations, but I have a few initial thoughts.
The technologies used to extract meaning from data are sophisticated and require expert care and feeding. This is not so far removed from what happened when online legal research products were introduced. Librarians stepped in to fill that role.
The skills needed to care, feed, and evolve services that use the technologies related to analytics and data science are specialty skills. Technical expertise is essential. Analytical expertise is essential. So librarians uncomfortable with analyzing results need not apply to this new wave of requirements.
Information professionals that are curious, ever-expanding their knowledge and skill sets, and have a desire to apply expertise to sophisticated technologies are well on their way to embracing the opportunities that about in analytics and data science. Information professional can use core skills and specialized expertise to create innovative solutions for knowledge and data management as well as analysis and application of the extracted data.
Information is our richest raw resource. We are just starting to scratch the surface of how to maximize the profits available through the mining of data. A key opportunity is the transformation from data to decision and analytics is the key process.
Note: The data science article references requires a subscription for full-text.
November 17, 2016