Back in the 1990’s, decision science was all the rage. Often looked at as the precursor to Big Data, decision science focused on streamlining decision-making and using all available tools and data for advanced modeling. Consolidating and combining different, independent functions became a key enabler of decision science. For example, when a company decides to market financial-services offerings, if done independently of a risk-management function, the company will primarily focus on increasing revenue from new accounts. Risk management, however, is also needed to ensure that those new accounts will not ultimately become bad assets. Combining elements of both functions would allow for a more efficient, coordinated process, with better outcomes.
Twenty-five years later, decision science has been replaced with data science. Essentially the same concept, deploying better solutions through advanced data access and modeling, except now the data is at massive scale. Companies are deploying new technologies at a record pace, but many of those same companies are neglecting to update organizationally as they would have with decision science because it can be very hard to do. It’s one thing to bring on new technologies, but updating organizations, moving resources around, changing reporting relationships…that’s hard! The result, however, not just inhibits, but prohibits change.
To be effective, Big Data technology ultimately must rely on five core enablers:
1) Use-case generation, prioritization and approval to make sure analytics initiatives are delivering business value.
2) Leverage data lineage and metadata, tying use case implementation to core data assets.
4) Track success and identify lessons-learned so results can help drive business transformation.
5) Develop an operating model and map budget allocation to tackle the tough question: Who gets control?
Simply implementing policies or technologies to add these functions is not enough; the Organization must be set up to embody and embrace these core principles. That doesn’t happen without making some hard decisions.
Organizations must address questions such as which executive gets to approve analytics use cases, how to ensure full use of production capability, what chargeback model to use, who owns data-science resources and many, many more.
