Читать книгу Minding the Machines - Jeremy Adamson - Страница 18

All organizations have some capacity for gathering data, and once the quantity of that data reaches a certain size, a data-centric team (whether business intelligence, data services, self-service insights, or general information technology) is naturally formed. The purview of these data teams in many cases has been confused with analytics and data science, to the point that the intersection of the two is very often considered the scope for both. Though there is certainly a strong mutually supportive relationship between these two functions, they exist in different spaces and with different incentives—one being a cost center and one being a profit center. Executives with medium-term ambitions to develop a next-generation analytical function will frequently prime the pump by engaging the data team and expanding their accountabilities.

The outcome of this is usually expensive, avant-garde data solutions such as data lakes, premature cloud migrations, and unnecessary latency reduction strategies. The organization, still in its analytical infancy, is unable to take advantage of these powerful technologies, the cost of which is allocated to the analytics team. Organizations need to learn to walk before they can run, and the burden of these data-centric initiatives, executed far too early, has weakened many emerging analytics teams who have had to justify their inheritance after the fact.

These issues are the result of a lack of strategic focus. Well-intentioned projects initiated from a mature data team, supported by an executive, and imposed on future analytics teams leads invariably to technical debt and a handicapped team. Analytical excellence needs to be the focus and the function to be optimized and not confused with the activities that enable it. Early holistic strategy development that considers the interactions between these different teams is essential.