My experience of re-positioning data teams

In the last few years, there has been a significant change in the way data has been used in traditional organisations. Digital natives or companies that started with digital first, have understood the value of information right from the start, as they had access to significant amounts of information just because they were digital. Using that information for insights, for decision making and for predictions came naturally to them. On the other hand, companies that were born in the brick and mortar world, have had to go through a long and sometimes, painful journey of digital transformation. The goals of digital transformation were varied, from being present in channels where customers had migrated to streamlining internal processes. The benefit from the enormous amounts of new information that digital transformation generates was not immediately apparent to them.

Some of these companies have been faster than others in seeing the potential of combining data generated via digital transformation with data that their traditional business generated, to create disproportionate value for themselves and better experiences for their customers. Since the data generation happened almost as a by-product of digital transformation, and the approach to value creation using data was not a primary objective, some have struggled to understand how combining different data elements can lead to outsized returns. As a result, they now face competitors who are increasingly getting smarter because of their ability to mine information and provide better experiences to their customers at optimised costs.

Changing an organisation from using data primarily for reporting to using data for problem solving using advanced analytics is not without challenges, which is why, some organisations are still reluctant or mildly motivated to pursue this. Many of them have dipped their toes and tried to experiment with an advanced analytics project, some have come out of it after a year or so of trying, being disillusioned and without clear value being created.

This post lists my experiences around some of the key factors that an organisation needs to consider as they transform the role data plays in their organisation, and in the process, enhance the positioning of data teams as value creators for the company. Any large organisation has multiple moving parts; while there may be multiple approaches to a successful transformation of a traditional data team, there are some factors that matter more than others and in my experience, the first project to try out advanced analytics plays a crucial role in generating a lot of the momentum, which is necessary to drive such a transformation forward.

When I think about the first projects, where we tried to break the barriers of how data was used and attempted to use it in unconventional and interesting ways using data science, below are some of the key considerations that helped. Over the next weeks, I will explore the points mentioned below in separate posts.

1. Use cases - The first use case can be quite critical, the post will look at what types of use cases to consider for the first experiment
2. Team - Getting the right team together for the first experiment is crucial, as not everyone in current data teams will have the necessary attitude to get over the lows
3. Stakeholders - Stakeholders, which includes customers of the initiative will bring in motivations and limitations, so its important to select the right set of stakeholders
4. Benefits - Measuring benefits and providing visibility of those benefits will play a key role in generating and sustaining the momentum
5. Usability & Adoption - How much of an importance to place on usability and adoption is important, how much to worry about these

Note that my approach may not be the recipe for success for any individual scenario, but can provide ideas about some of the things to consider and experiment with.