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The Four Battles Asset Managers Need to Overcome to Fast Track Data Programs

Establishing a sustainable data governance and management practice requires a significant commitment to drive value across organizations. Still, many asset managers and other financial firms see data programs as a nice-to-have. In fact, that’s why it’s taking firms years to build up their practices, and why many are still not there yet.

I recently spoke on a panel at FIMA West which covered a range topics surrounding building and modernizing architecture to serve the business and improve value. One of the main discussions focused more generally on the challenge to obtain internal buy-in to recognize data programs and properly fund and staff them.

However, as tighter budgets and lower profit margins impact firms, it’s an uphill battle for data programs to prove they are able to either save money or generate value in the same way other groups, like trading or portfolio management, can.

In most cases, those running the data programs will face multiple battles which could limit their ability to minimize risk, enable faster processes and better trades, develop efficient operations and generate higher profits. These include:

Battle #1: Stakeholder buy-in 

Effective data governance and data management are increasingly important as data sets expand and firms face more demands both internally and externally. Despite this trend, many data programs are struggling to communicate their worth as they are competing against multiple internal groups vying for a similar allocation of budget dollars.

The key factor to obtain buy-in is communicating the program’s significance to the right people. It’s not a matter of having key stakeholders say a data project is good or bad. It’s a matter of having them recognize the need for it and then actually prioritize the need for it, penalizing colleagues internally should that need not be met.

An organization needs to be able to collectively say, “Data is not something we want to do, data is something we need to do. We need to manage our information. We need master data management (ensuring information is captured in one place). We need to control our data, maximize its value and eliminate risks wherever possible.” The conversation must move toward enterprise data management, which differs from master data management because it brings all the different business lines under one understandable umbrella from a P&L and client perspective.

This articulation must be emphasized to the appropriate stakeholders so they listen and take the necessary steps to include data and information management as a top priority.

Battle #2: Obtaining budget  

One of the biggest restrictions on data programs is formal recognition (i.e., budget allocation). Firms might recognize the need for it in one particular area but not necessarily as an enterprise-wide initiative. However, the only way to achieve data governance is by having and maintaining a baseline of data quality in every area.

To achieve this goal, there has to be an understanding that the firm will spend X amount of dollars on new products, on client acquisitions, on new trading investments and on growing AUM in addition to X amount of dollars on data. The challenge with communicating this lies in the fact that data affects every corner of an organization and yet seemingly doesn’t generate income directly, making it difficult to compartmentalize.

You can’t necessarily say what the optimal data governance model will be for every area of your organization. But what you can say is the bare minimum. You can say the program has to perform at a certain level and capacity with the data owned and stored at a specific minimum level. Once that’s agreed on, you can then make it a need to enforce it and prioritize it with a specific allocation of resources and people.

Battle #3: Funding prioritization

Data projects require support, infrastructure and people to put the right processes and system architecture in place. Of course, all of these require a funding commitment from the organization. However, one of the main issues when firms are prioritizing their data program needs is that the conversations only involve business and technical staff. In a typical asset management shop, that includes traders and IT people. For a bank, it could be marketing teams, investor relations and technical or IT staff.

Oftentimes, firms overlook input from the people actually operating and running the different systems and processes—the UX and design teams or the trade and investment support staff. Without these components, the technical support team loses functional context and the business people turn their focus only on the business (i.e., making more money, minimizing costs and gaining new clients).

To prioritize data as a wider need, bringing in the right operations people so that you can communicate the entire value from the technical and operations perspectives through to the business drivers is crucial.

Battle #4: Data governance is new

After the need is prioritized, the problem then turns to the infancy of data governance. For most asset managers, it’s relatively new. Data governance practices aren’t more than five or ten years old.

You are introducing governance to an existing corporate or political structure. You’re taking one group (data) that doesn’t generate money and you’re trying to use that group to implement new processes and governance on areas of the organization that are already generating money and that may not feel they need to be governed. The same problem organizations have budgeting data governance in with new product investment, client acquisitions and other long-standing components of an asset management firm is the same issue you have getting multiple groups to comply with data governance.

It becomes more of a suggested idea for an organization rather than a real priority. As a result, you end up with data teams that work with one group because they have resources and not another group because they lack sufficient budget. But this is not proportionate to risk, money or value. For example, operational trade support teams deal with massive amounts of data but operational trade support for fixed income securities is not nearly as risky as in respect to OTC products, similar to how the function of a portfolio management group, which deals with extremely complex data and generates income, is more critical than the operational trade support areas.

The Path Forward

To deliver a sustainable data management and governance strategy, firms should proportionately understand risk, money and value with respect to business, operations, IT and data needs. One solution may include hiring an internal or external enterprise business architect who can mediate between business, operations, IT and data to get everyone aligned on a budget and advise on the big picture.

For many firms, a good way to begin moving data programs forward is to create a baseline level of data quality across these four groups—with an eye toward incorporating new technology that could change the way the industry manages data in the future.


Author

joshua-sattenJoshua Q. I. Satten – Director of Business Consulting

Joshua is a Director of Business Consulting, in San Francisco. As a member of Sapient Global Markets’ West Coast Leadership team, Joshua leads business development and market strategy. He has over 15 years of experience leading operations management, strategic innovation and enterprise business architecture for major buy-side, sell-side and third-party outsourced and administration entities. He specializes in change management and growth of full life cycle trading operations across listed and OTC products combining strong management, leadership and strategic planning skills focused on globally balanced, sustainable growth across a wide range of specialty areas. Joshua is an active industry participant and speaker, and has worked with and chaired working groups within ISDA, ISITC, SIFMA, AMF and others.