Data is a cornerstone of the Investment Management industry – we get it, you get it. Firms have never been more focused on their data estate – how to manage it, how to control it, how to maximise its value. There has been the steady emergence of Chief Data Officers, Data Governance Forums, Data Visions and Data Management Strategies. There is no doubt that lots of time and money is being spent. Yet there is a fundamental problem, even for progressive firms: there is no easy way of measuring how effective or fit for purpose a data estate is or to quantify the impact of improvements. Enter the data maturity assessment.
Data comes from many sources, internal and external; is structured or unstructured; managed by different teams. Some is operational, some is key to decision-making. How do you make sense of it all, consistently, over time?
Strategy beyond execution
Across the industry, firms are in varying stages of their data journeys. You know you need a clear data vision, to develop a roadmap, identify data owners, a strategy and to build out data tools, alongside implementing controls and adopting data governance principles. All this is required to deliver trusted data to users in an efficient and safe way. But you cannot do everything at once. How do you prioritise? How do you know which initiatives will bring the greatest improvement?
Fast forward six, twelve, eighteen months – the key question remains the same – whether the quality of the data estate has been maintained, has improved, or degraded. But how do you really know what is working well, what isn’t, and why?
- What measures are you looking at?
- Do they reflect the unique aspects of your business processes?
- Are they qualitative or quantitative?
- Who else are you asking?
- Are you relying on a third-party provider to give you the answer?
- What structure does your evaluation process follow?
Starting at the end
Everyone can appreciate the importance of data on data. It is therefore logical to introduce a formal data maturity measure in order to track the development of the data estate and the effectiveness of individual projects. This measure and review goes beyond existing data management assessment approaches which are often seen as a box-ticking exercise, concerned only with asking if this or that forum or policy is in place, without actually assessing their quality. Measuring data maturity ought to be comprehensive and specific to an organisation, therefore it should include a combination of the quantitative assessment of accessibility, trust, flexibility, and capability supplemented by tailored surveys of data consumers and owners run on a consistent and regular basis. This approach captures the impact of specific projects as well as the natural evolution of needs and expectations across the firm.
The key aspect to consider when tailoring, is to spend sufficient time and thought on aligning the questions and criteria to the organisation’s overarching data vision. Only then is the data maturity result truly meaningful and reflects specific nuances and requirements of a complex data estate. Especially when considered at an early stage of delivering the data vision, it can be an extremely helpful tool for guidance and self-assessment. Cue “Where do we start?”.
Making benefits measurable
From a long-term perspective the ability of a data estate to support a firm depends not only on the existence of forums, policies and controls dedicated to key data themes – Data Governance, Data Accessibility, Data Quality, Data Lineage, Data Tools, Data Security to name a few – but also on understanding their effectiveness. This includes identifying the strengths, weaknesses, blockers and opportunities that lie below the surface, and why. As always there is considerable value in the detail. There are no short-cuts to this, however, the pay-off is two-fold: actionable insights, as well as weight and confidence behind the data organisation and its progress as a whole.