Today’s businesses collect more data from more sources than ever before. From weblogs to transactional data, the Internet of Things (IoT) and everything in between, no company today could possibly say that they have a data acquisition problem. But many companies, whether they like to admit it or not, do have a data value problem. That is, they struggle to gain real business value from all of the data (or any of the data) they are collecting.
In the past, digitally-native tech companies (like Google, Apple, Facebook, and Amazon — GAFA) created value from data essentially by applying advanced machine learning techniques to a few key problems (how to make ads relevant, recommendations effective, etc.). Their problems were technically fairly challenging, but the means to solve them was fairly simple: hire 50 PhDs and talented engineers, and you’re probably bound to succeed.
As the employment of machine learning (ML) and artificial intelligence (AI) business solutions has become more ubiquitous, particularly through the introduction of open source technologies, businesses of all sizes have started to assemble or hire teams tasked with deriving insights from the sea of data they’re collecting. Companies with data teams hope to answer business questions and address needs using this raw information.
But when these teams are well organized and given the right tools and direction to succeed, they can do much more: data teams can serve as research and development departments, experimenting with raw data to explore possibilities and solutions that the business didn’t even know it had.
Successful data teams are innovative and creative but are also able to get past the experimental stage to actually tackle difficult business problems. But building this team isn’t as easy as hiring a staff and letting them go to work - companies with high-performing and innovative data teams empower them to be a bridge between IT and business departments to evangelize creative innovation while also finding (and deploying) real solutions.