What is the role of the data scientist in the insight economy?
When we asked five leading data science professionals to tell us what the role of the data scientist is in the insight economy, here’s what they told us:
Andrew C. Oliver
Founder and president of Mammoth Data, Inc.
Andrew is the founder and president of Open Software Integrators, a company that works with primarily open-source companies, especially at the startup and initiation stage, to create and mature service offerings that include course development, training, consulting and support. His company allows its partners to deliver professional service offerings while focusing on their core areas of business.
The role of the data scientist is to help identify and codify new ways to make better decisions. In order to do that, she needs to understand the business, statistics, math and, most of all, how to tell a good story.
Bob E. Hayes
Chief research officer at AnalyticsWeek
Bob researches customer experience, big data and analytics and how they all work together, applying his findings to help businesses make fact-based decisions.
Generally speaking, I think that the role of data scientists is to extract value from data. Data scientists’ work helps improve how humans make decisions and how algorithms optimize outcomes. Through the collection, analysis and interpretation of data, data scientists extract empirically based insights that augment and enhance human decisions and algorithms.
Data scientist and Galvanize chief security officer teaching top-tier professionals in data science and machine learning
Mike is experienced in leading multiple data science teams to deliver industry applications for machine learning, predictive analytics and data architecture solutions, as well as in creating data products for recommender systems and for text comprehension, image recognition, targeted advertising, forecasting and user understanding, among other things.
Data science will be the engine of the insight economy in coming decades. Advances in distributed implementations of representation learning techniques found in deep learning and efficient factorization algorithms will reveal insights quicker, deeper, at larger scale and with greater speed than we have ever seen. Unprecedented customer personalization and insights driven from unstructured data footprints mean we have only seen the beginning of what data science and machine learning will do to drive the insight economy.
Data science educator, Data-Mania
After working in the chemical sciences as a Keck researcher investigating exchange reactions for DNA and RNA molecules, Lillian became a professional engineer. While speaking, training and consulting on data science, analytics and big data, Lillian has assisted global IT leaders, major governmental and non-governmental entities, prestigious media corporations and not-for-profit technology groups.
Data scientists and data engineers power the insight economy. Using (big) data that is captured and refined by data-engineered systems, data scientists are ultimately responsible for making sense of data and deriving insights that generate value. Data scientists generate ad hoc insights or generate them through automated systems that they build to provide intelligence on a reoccurring or real-time basis.
Matt is the former CEO of SideraWorks, a management consultancy acquired by CrowdSource, which appointed him head of market innovation, in which role he developed future market strategy and sizing for the series B financing round pitch deck. Matt is an entrepreneur, a business and marketing strategist and a frequent keynote speaker who has worked in digital technology on both the agency and enterprise sides since 1994. He ran interactive for the marketing agency of record for such established brands as Levi’s, Cisco and British Airways, as well as for the launch of ventures such as Jet Blue and RedSpark.
Matt has also worked as a partner to build out a consultancy of more than 300 persons. During the past decade, he has focused primarily on developing innovation cultures, driving change management initiatives and performing specialized market research to help organizations align themselves with their customer and partner bases in a fast-paced and increasingly transparent social world.
The two key words in this question are data and insight. I view them as two opposite sides of a bridge. Bridging that gap requires a number of disciplines, a critical one being data science. I tend to view most data science as a filter—a means to take big data, find patterns of alignment and incongruency and turn that data into smaller chunks of more meaningful data. Put more simply, data scientists provide that crucial first step to insight—not by providing answers, but in allowing us to ask better questions—to drive toward the why and not just the what.
All these experts and more can be found at Insight 2015 this week. For an in-depth look at how Spark has become a tool of the new breed of citizen data scientists, check out this informative IBM Analytics resource page.