The Hadooponomics Podcast, Episode 17 – Data and Decision Makers: The Human “Resources” for Big Data in HR

Hadooponomics17For our next episode of Hadooponomics, we look at the intersection of Human Resources (HR) and Big Data to determine how companies can take human-based data, which is inherently non-numeric, and attempt to quantify it. We have on the show Evan Sinar, Chief Scientist and Vice President of Development Dimensions International (DDI), a company focused on using data to identify and develop leadership in the enterprise. Evan applies his background in industrial organizational psychology to understanding the human side of Big Data and leadership.

We cover a lot of ground in this episode, from best practices in data visualization to the transformative power of Big Data, what it means to be a leader, and how HR and Big Data overlap. We’ve seen throughout the show the overwhelming impact Big Data and data-driven decision making has on organizations, and this is true across departments and functional groups. But as Evan points out, HR has typically been slow to adopt data-based decision making, and part of this has to do with the complex nature of human data.

According to Evan, organizations need to be savvier about people analytics, including what data they are collecting, how they are collecting it, and how they are using that data. The human-oriented nature of HR produces data that is typically non-numeric, and thus difficult to analyze using traditional Big Data methodologies. Evan describes how we can begin to quantify this “human element” that has been missing from Big Data of the past. But there’s another problem with relying on Big Data as a blanket solution: there should always be the role of a human expert to make the final judgment call.

How do we reconcile these seemingly contradictory ideals? And how can Big Data begin to understand the human element, enough to potentially assess tough-to-define qualities, like leadership ability, that previously required human judgment? Evan has some answers and we explore these concepts, and the data-oriented organizational structures and leadership of the future, in this episode.

Enjoy!

Listen to the Show:

Use the embedded media player to stream the full episode or you can subscribe to our iTunes and Stitcher channels. The full transcript of the interview is also available.

PodcastHadooponomics Podcast Home | Subscribe via iTunes | Stitcher Radio | Transcript

Additional Resources

Find Evan on Twitter @EvanSinar

Find Evan on LinkedIn

Follow Evan’s Blog at DDI

About Arcadia Data:

Arcadia Data Logo

Arcadia Data unifies visual analysis, business intelligence and data discovery; it runs natively on your Hadoop clusters without data extracts. Its easy-to-use browser-based visualizations deliver secure access for hundreds of concurrent users across hundreds of billions of rows in near-real time.

About James Haight

James Haight is a principal analyst at Blue Hill Research focusing on analytics and emerging enterprise technologies. His primary research includes exploring the business case development and solution assessment for data warehousing, data integration, advanced analytics and business intelligence applications. He also hosts Blue Hill's Emerging Tech Roundup Podcast, which features interviews with industry leaders and CEOs on the forefront of a variety of emerging technologies. Prior to Blue Hill Research, James worked in Radford Consulting's Executive and Board of Director Compensation practice, specializing in the high tech and life sciences industries. Currently he serves on the strategic advisory board of the Bentley Microfinance Group, a 501(c)(3) non-profit organization dedicated to community development through funding and consulting entrepreneurs in the Greater Boston area.
Posted on by James Haight

Leave a Reply

Your email address will not be published. Required fields are marked *

You may use these HTML tags and attributes: <a href="" title=""> <abbr title=""> <acronym title=""> <b> <blockquote cite=""> <cite> <code> <del datetime=""> <em> <i> <q cite=""> <strike> <strong>