Data & AI Pockets of Maturity

No matter how immature a utility is when it comes to data management, there is a high likelihood that somewhere within it, some amazing things are being done with data.

It might be automation of a handful of KPI reports. Perhaps innovative use of AI for forecasting purposes. Or maybe even a stand-alone initiative to address data quality.

Meanwhile the rest of the organisation is still struggling with manual reporting processes, inaccessible data, questionable data quality, and limited analytics capabilities.

How did the organisation come to have these pockets of maturity?

Usually, it was to meet a pressing business need. Perhaps born from experimentation. Likely it started with a single project where data played an integral part.

Individuals were hired to meet that need. Or an existing employee learned on the job, or leveraged skills that were not in their formal job description.

Whatever their origin, a smart data leader will look to use this existing capability in pursuing broader organisational data goals.? Here are three ways to leverage pockets of maturity (PoM) within the business:

1)????? PoM projects can be used to showcase what can be done with data. The central data function does not need to be the origin and messenger of what is possible.

2)????? Involved employees can serve as champions for advancing enterprise data maturity. Their experience and position make them strong allies in driving awareness and adoption.

3)????? An existing PoM can be the perfect place to begin roll-out of an enterprise data program, given baseline levels of data literacy and appetite.

There are, however, reasons to be cautious.

Frequently these pockets evolved because it made more sense to do so in isolation than to coordinate with a central data or technology team. It's also possible that in building and demonstrating their data capability, they have become the organisation’s de-facto data and analytics centre of excellence.

Chances are that other business teams are turning to them for assistance with their own data needs. While these pockets may demonstrate mastery in one area of data management, they may be lacking in many others. Are they exercising effective data governance, or model management? Are they creating technical debt, installing unvetted software, or exposing sensitive data online? Is there key-person risk or a failure to document processes?

For the enterprise data team, the presence of these pockets within the business simply strengthens the case for engagement. To get already mature stakeholders on-board, demonstrate how the data program will build on gains they have made.

Recognising that different levels of data maturity exist in your organisation and tailoring your engagement approach can pay dividends. It takes more effort, but building on prior successes can give your data program just the start it needs.

The views reflected in this article are the views of the author and do not necessarily reflect the views of the global EY organization or its member firms.