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The Need To Evolve Business Intelligence Within An Enterprise Setting
Frederique De Letter, Director Data Intel & Enterprise Information Mgmt, Dominos
The last couple of years we all have experienced a wave of disruption across multiple dimensions, i.e. people, process, data and technology, representing an interesting challenge for organizations to embrace and turn those into an opportunity and ultimately a sustainable competitive advantage. What used to be a specific competency that only certain teams within an organization mastered (and thus centralized) has become more and more democratized (not necessarily commoditized) due to the rise of the “data citizen” and data everywhere combined with process and technology advancements more readily available at lower cost.
Amidst this transformational need a traditional centralized Business Intelligence function will not be generating the desired business impact as it once did. As Jack Welch puts it eloquently “Change before you have to” as the alternative is being obsolete and non-relevant. Gartner (2016) identified a number of those megatrends early on across the data value chain which provides organizations a reference point to define their interpretation of change required to be geared towards the future.
Defining and limiting the BI function going forward to only “analytics enablement” with focus on reporting and platform (and often referred to as a defensive play) does not seize some of the emerging opportunities and capabilities that enterprise organizations need to activate on to maximize both value and speed to market going forward (and hence going offensive).
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
It is therefore important to reflect what makes the right “SWAT”-team composition and associated competences to address more complicated organizational business questions that the evolving BI function can take on (should they wish to) and that fits within your organizational structure and specific context.
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.