Read this article and learn about:
- Why data management projects should be owned by the business side and not IT
- Why data management varies from project to project depending on the specific goal
- Taking a more strategic approach to data
- Defining what data you need for what purpose
- Building a culture around data
- Why some firms don’t know what data they have, what they can use it for, or where to start
About the author:
Steve Young, Principal, Citisoft
Steve Young provides a blend of business understanding combined with an extensive knowledge of the technology and software landscape and of the services sector. Bringing more than 30 years investment management industry experience, Steve has held senior positions at Rhyme Systems, Thomson Reuters, Extel Financial, and Datastream.
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.