A strategic data management approach

Data management is an interesting but also a very complex discipline in our industry. As a term, it is used as an umbrella to cover a wide range of different things, and what data management should be varies from project to project depending on the specific goal. In other words, data cannot be approached in a single uniform manner.

There has been a lot of hype around data in the past decade, and firms are actively looking to win business with better data management and analytics. This requires collecting more (and better) data and analyzing that data with much more sophisticated analytical tools. This sounds great, but in reality, the challenge most firms are facing (whether they admit it or not) is that they don’t really know what data they have, what they can use it for, or where to start.

… in reality, the challenge most firms are facing (whether they admit it or not) is that they don’t really know what data they have, what they can use it for, or where to start.Steve Young, Principal, Citisoft

The ideal scope of a data management project

Data management is more complex than most think and a lot of firms that start data projects never finish them. One of the reasons why these projects fail is that the exact scope is not nailed early on. Instead of being a huge basket case driven by IT departments and containing any data-related tasks, each data management project must be defined and driven by the business-side with a clear prioritization and a well-defined scope.

The business-side is the data consumer, so it should also drive data projects

Traditionally, data management projects have been led by IT departments, which may also be a reason for their failure. The business-side needs to drive such projects, and until they become the owners and value it, then these projects simply won’t succeed.

Data management projects should be owned by the business-side because:

• If the project is led by IT then the business-side, who will use the data, does not buy in
• The business-side knows what sort of data they want and need, and they should therefore set the requirements and objectives of data management projects from the outset.

Front office as key stakeholders for better decision making

The front office is a huge consumer of data and they want access to data when and where they request it. This means that if your front office is not involved as key stakeholder in the project, then it’s going to fail. Unless you can give them the data in a timely fashion and in the standard they want it, then they will go and get it themselves. Many firms want to bring more control into the front office, but in order to do that, you have to be able to meet their demands.

Slimming down on a data diet

Some firms are storing massive amounts of data in ‘data lakes’, storing every piece of data they create. But if you look at the cost of managing that data, against the benefits you get, there is not much logic to it. Asset managers need to look closely at their data and be more strategic and specific in what they store, and what benefits they can extrapolate from it.

Storing everything is not the answer. Not only because of the cost, but because it’s impossible to figure out where to start. A lot of firms need to go on a data diet and reduce the amount of data they collect and store. This starts with a clearer definition of data, and figuring out how best to approach it. This definition can (and should) vary from unit to unit and project to project as the objectives vary. This will help firms realize exactly what the goal of a data management project is.

Tactical vs. strategic approaches to data management

Many firms end up doing tactical data management projects to solve short-term issues because the big strategic programs are seen as just too painful. If legacy systems are involved and firms build various data layers to try and lower the risk of the legacy system, the consequence is heightening the risk by making things less transparent. The solution here may be to remove the legacy system, rather than build a manual workaround. The immediate threat of regulatory fines is not the logical or sustainable place to start a data management project.

Leading a strategic and long-term project will produce more concrete benefits. For it to succeed, however, a data management project should have a benefit stream, where the project delivers actual benefits on a regular basis. Data management is a journey, more than a project. Data is changing all the time, so it needs more flexibility.

… a data management project should have a benefit stream, where the project delivers actual benefits on a regular basis.Steve Young, Principal, Citisoft

Building a culture around data

Technology is often used as a scapegoat for project failure, but actually, it is often just as much about culture and education. Data projects require a behavioral change to succeed. This takes that ownership of these projects has to come from the top. If management wants a data-centric culture, then they need to lead by example as well as ensure to educate and incentivize.

Another reality is that not a lot of people have led successful data management projects, meaning that there is a serious lack of best practice and skill available. This makes it harder to build the right team and many teams are learning by doing.

Self-servicing is the future

Firms need to be creating more of a self-servicing type mentality with a reliable data store that people can access. In the past, self-servicing has meant that people went and found their own data from wherever they wanted and pumped it into the system themselves. As a result, data being used in one part of the organization was completely different to data being used in another department.

This is not sustainable in any way, and clearly, people are trying to get away from that towards a more consistent view of their data. For some firms implementing an investment book of record, or an IBOR, as the operational data management backbone is an example of taking a more strategic and data-centric view to asset management operations. By removing point-to-point integration and providing a single hub of timely, reliable and accurate data, firms will remove the need for departments to source and amend critical data and to invest their time in more business-critical activities.

Finding the ROI

A huge challenge everyone faces with data management projects is being able to calculate the exact return on investment (ROI). Projects get weighed down by too many diverse requirements, rather than having a couple of clear objectives. All these conflicting requirements means that people struggle to get their head around it and provide a clear argument for funding.

Data is a factor of every project. Many of the benefits of improved data management are difficult to quantify. As well as bringing significant business benefits to an organization, a good data program can usually save costs, especially around sourcing and duplication. These are far simpler to measure but do not show the complete picture. Long-term strategic goals can be more readily achieved with a well-designed data management approach. These benefits are far more challenging to quantify but are a more significant business change that should not be excluded from business cases.

Conclusion

To summarize, ask yourself this question before embarking on any project: “What data for what purpose?” This will help narrow the scope of your project. Don’t assume that there is a one-size-fits-all approach to data; there is data for regulation, data for risk, data for your front office, etc. Get specific before embarking on any journey. If the scope of the project is too big and too broad, then it is bound to fail. Turn it into bite-sized chunks, so that you can deliver visible benefits along the way.

IT exists to support and service the business-side. Because data is an intricate part of everything across the organization, data projects needs to be led by the business-side, otherwise it risks failing. Start by identifying the business value of the specific project, rather than talking about data in general terms. And finally, remember that data management projects are the most complex you can take on, so they take careful planning.