You Can’t Buy Digital Transformation “Off the Shelf”…(And Expect Results)

Companies everywhere are setting aside budgets for investment in upgrading their analytics capabilities.  Very often, this involves investments in cloud-based computing, data warehouses, artificial intelligence, business intelligence dashboards, and multiple other forms of software and hardware investments.  But the truth of the matter is that digital transformation can’t be acquired “off the shelf”, installed on top of your ERP system, and Presto!:    Instant Analytics!  The real changes that will take place will require far more investment in people – and specifically, working on new and emerging ways of using data for improved decision-making.  This will require a lot of experimentation, noodling, and dead end streets before the real path ahead becomes apparent.  Companies are starting to consider investment in Centers for Analytics, which often require different types of individuals working together in unique ways.

My research suggests that three types of individuals with very specific skill sets are required to collaborate on any given analytics problem to arrive at a successful outcome:

• The Functional Subject Matter Expert who defines the business problem that needs to be solved or the insight that needs to be the lead for business functions as well as serves to review data inputs and executes or arranges for the data governance over the data quality by data stewards. These may be someone from finance, or operations, or cost management, or procurement, or others who have in-depth knowledge of the challenges faced by the business, and can put together the right research question that drives the analysis.
• The Discovery Analytics Expert who can provide deep dive data correlations and visualizations and recommend certain of these for institutionalization. In many cases, these individuals might also be functional subject matter experts. They are part of the self-service analytics process, but may also be assigned to specific tasks and projects.
• The Institutional Analytics Technician who is responsible for three primary activities:
  • To perform discovery analytics when so requested by certain functions
  • To act as an advisor or editor with respect to discovery analytics applications
  • To develop and standardize the visualization of institutionalized metrics

The Institutional Analytics Technician will not have close enough knowledge of any particular function to be able to define business problems to be solved not perform effective data quality analysis and governance.  However, this individual would be able to work on specific projects and would be directed by the Functional and Discover analysts.

The recommended approach to moving forward in an analytics initiative, as well as the organizational design, is to “learn by doing”, but also to “fail fast”. A proof of concept phase will allow all parts of the business to get a solid foothold into the BI space and determine to what extent practices can or should be harmonized or flexed in relation to the distinct business dynamics.  It may also help to understand how changes in business processes will need to adapt, as well as the types of visualizations and analytical outputs that will be the most valuable to different stakeholders in the organization. It will also allow time for the digital platform to scale and for governance practices to solidify.  Over time, others in the supply chain (supplier, customers) will also need to be plugged into these shifts in decision-making and operational execution practices.

This is going to take time – and won’t be an “off the shelf” solution.  Rather, each organization will need to discover what the right digital transformation pathway works for them, and create a strong overarching vision for what business advantages the enterprise expects to get from these changes.