Last month, Outlier attended an exciting Chief Data & Analytics Officers event. Data and Analytics Executives from around the globe shared how to transform data-awareness into insight-obsession. Our co-founder and CTO, Dr. Mike Kim, was honored to have the opportunity to give the keynote presentation: Achieving the “Nirvana” of Self-Service Data Science. As well as sharing business strategies for self-service analytics, our team connected with other data science experts to hear about the ability to analyze, ask questions and put systems in place where people can make more informed business decisions.
Data and the Business
One of the impactful topics at the event centered around understanding the right priorities of the business unit and how data science can impact the business. Many organizations struggle with being able to translate the work of data science into the business goals of the organization.
Questions arose including how businesses can drive how a client/prospect wants to receive marketing information from data science to hearing how there is a clear business output associated with the work.
Key learnings hinged on developing intuitive outputs that a business user can easily understand. In a similar vein, business users should also be able to grasp value from the data and acquire the information in an accessible interface.
Talent Retention and Recruitment
Most notably, attendees expressed how there simply are not enough data scientists in the marketplace to fill the jobs available and handle all of the data science work. Based on the Annual CDO Survey from Gartner, 59% of Chief Data Officers say they feel too far behind on attracting and hiring analyst talent. Once they do hire talent, organizations report they can only analyze 12% of their data accurately.
Organization of Data
As exemplified during Dr. Kim’s keynote, the great news is that it’s possible to have self-service data science. In his session, Dr. Kim explored current challenges to consider before migrating to self-service, how automating collection and analysis phases of data science can help to achieve self-service, and shared examples of organizations that have adopted a self-service data science philosophy.
Demystify, democratize, and digitize the data! The goal in the self-service model is like taking a photo. We’re not a bunch of professional photographers. Folks just want to push a button. No one wants (or knows how to) set aperture and focus. Marry that with how we approach data and the output is the same. Point and shoot. Get analysis of the right data, at the right time, no matter what else is in the picture.
Automated business analysis (ABA) leverages artificial intelligence (AI) techniques to analyze data and find unexpected changes in your data quickly, acting as a virtual business analyst. ABA can find these insights immediately, reducing the dependency on data scientists and instead serve up those insights automatically. In case you missed Dr. Kim’s session, you can check out his Self-Service Data Science webinar here.