Foundational pieces to this equation can be mapped out around a simplified workflow of business problem statement to data prep to potentially ML/AI to operationalization to gain the impact and attribution. Beyond the core competences of data science and data engineering a key skillset that has been less formalized is bridging the business domain context (including the business SME) with in-depth data domain expertise. In addition, taking a broader more holistic look, i.e. enterprise-wide, across domains to look at a problem statement can sometimes be very beneficial in the design phase. Value-to-market comes into play here.

Another area of opportunity is Augmented Analytics. Given the explosion of data across the 4 Vs, and the need to automate actionable insights at scale across the organization and achieve data ROI, organizations need to be both agile and surgical in their “last mile” to ensure the go-to-market is well mapped out and executed. Recommendations must sit where it can create impact and where it must be consumed whether it is a transactional system (and consumer facing) or an analytics portfolio embedded within the organization. Guiding and providing internal thought leadership across the entire data value chain and effectively acting as the glue to scale that data intelligence maturity represents a sizable opportunity.

In order to stay relevant as a BI function in any organization requires one to reflect on what value-add it generates and contributes within the larger enterprise success. Without that mindset the team will not have a strategic seat at the table in order to drive business transformation in the age of disruption.