This is the last post in a series of blogs on setting up a governance program for the effective management of investment product data, in this blog I will wrap up the series with a discussion on how to move your governance program towards a mature model.
If you have followed the earlier 9 posts you will probably recognize many of the steps in the data governance evolution scale I referred to in one of my previous blogs on maturity models for governance of investment product data (see image below).
The Data Governance Evolution Scale
As you embark on the instantiation of your program you will more than likely be working within a quite chaotic environment – one lacking in clear, top down driven policies and standards, and very much reactive.
As you start to take action and follow some of the steps that I have outlined in earlier posts you will start the journey towards maturity – initially you will need to go through the process of securing C-Level buy-in, and formulating terms of reference for the program, before embarking on the broader strategy definition.
Do not neglect the importance of the culture and organizational structure that will be needed to support and enable the governance program to succeed – in particular you have to think about the target operating model of stewardship that will be most effective for how your business is structured both now and into the future.
In order to move from the “chaotic” and “reactive” levels in the maturity model you must focus heavily on the following:
- Ensure your strategy is clearly defined and communicated to all actors within the domain you are trying to apply governance too
- Set out the policies and standards you want to promulgate, with clear alignment and reference to the strategic goals
- Once you have the policies and standards down pat you will need to focus on ensuring you have a set of applicable processes and procedures that are aligned with the overall strategy – each process should be directly traceable back to a specific standard/policy
- In parallel to the process of defining the policies, standards, processes and procedures (the how) you need to be working on building out your master data plan (the what)
If you have worked through the process above, you will have moved from a position of total vacuum, to a chaotic, to a reactive program of governance – in fact you have probably got further than many firms who claim to have governance will ever move past.
To move from “reactive” to “defined” you need to make sure that there is a clear understanding of the data within the terms of reference of the program – all participants have to use the same nomenclature when talking about data, as inappropriate usage, miscommunication and misunderstanding of what is being discussed is the most common root cause of data quality issues I see. To this end, the construction of a data dictionary is a fundamental step in moving your program to state of ”defined”.
Other areas you will need to focus on before you can consider your program to have achieved the level of “defined” in our maturity model are as follows:
- You need to start the process of measuring your program – don’t fall into the data quality denial trap – identify the key areas (KPI’s) where useful measurement can contribute to a balanced score card that reflects how well the program is being executed. Think about KPIs that can point to the quality of the data – focus on the standard facets of data quality – timeliness, consistency, completeness and accuracy
- Examine the operating model you have in place for stewardship - to what extent can it be made more effective and efficient by driving the rights issues to the right experts – in my opinion the stewardship model needs to be n-tiered in line with the back to front alignment of traditionally asset management businesses
- As you develop and hone the standards and policies, the underlying process and procedure will start to snow-ball and it will become increasingly difficult to achieve the oversight and accountability that all governance programs require to be successful – to that end you will need to consider how technology can help support and frame your program, but remember technology is not a panacea to the ills of data governance and quality
- For every issue, exception and concern raised through the program start tracking the root cause – this really does require a good underlying collaboration tool
Moving from the level of “defined” and onto “pro-active” and “predicative” will seem at face value to be relatively easy, but is very rarely achieved, in my opinion this is generally culture related – some of the key elements in moving onwards and upwards is buying into the continuous improvement principle – if this is not a core value in your firm you will find it difficult to progress in any meaningful way
So what are the key elements moving your program to the highest levels of maturity?
- You need complete (firm level) buy-in to the concept of continuous improvement – with the appropriate feedback loops designed into your processes to ensure this becomes a core tenet in your program’s modus operandi
- You need a specific set of activity centred on reviewing the root cause of issues being surfaced, such that they are being feed into the continuous improvement cycles for the relevant processes
- Your master data plan and data dictionary will be seen as living breathing entities, that are constantly being updated and reviewed in line with the changes in your BAU operating models – these artefacts must never become stale. I often find this is the easiest way to demonstrate to a firm that their program is still in the “defined” stages of maturity, when they might argue otherwise
- Your program will be making correct and efficient use of technology to empower and enable the data quality management process, supporting the people (stewards) to apply the governance
- Your data architecture will be coherent and well designed for your organization and how it does business – I find discussion on architecture and the correct use (or not) of MDM, warehouses, marts, hubs, silos etc can be fraught with generalisms and so I will avoid pontificating on what I consider good architecture
When you are able to demonstrate that your program is constantly being tuned and refined, that you have demonstrable audit-trails, and your people, processes and technology are working in harmony, you are well on the way to achieving a highly predictive and mature governance program.
Before signing off of this 10 blog series, remember to keep your eye on the ball – think about the green grass on the other side of the river – the reason we focus so heavily on applying good governance to client facing investment product data is because data is the oil in the sales engine! Feed it with bad data and the engine will start to seize up…..and a business without sales has no future!
Posted by Ronan Brennan 
