Benefits of a Data-Centric Culture

Struggling to create data-centric operations in your firm? It’s a challenge for most of the asset managers we work with. We all understand the importance of using data to capture alpha, but designing operating structures, forging partnerships with data-focused vendors, and implementing solutions that serve the demanding needs of the buy-side aren’t so cut-and-dry. One common trait of successful firms has been a clear understanding of the power of data to drive growth, profit, or preferably both (check out this case study for how a $300B asset manager designed its data-centric operating model). Once that direction is articulated, the hard work goes into the renovation (and sometimes reinvention) of a firm’s approach to data. At a basic level, a successful firm acts differently in regard to data across every function in the firm.

Anyone with a large data project underway can claim to be a "data-centric operation" but a more narrow range (that happens to align pretty well with a list of managers seeing significant inflows) focuses on how data can benefit every area of the business. If you’re curious how a different approach can positively impact your bottom line, read on for two compelling benefits of creating data-centric operations culture:

Cross-functional partnership

Many data-centric organizations have created effective partnership structures in which business, technology and operations are jointly responsible for the effectiveness of data, typically organized by data domain  (I’m avoiding tagging this as governance in hopes that you’ll keep reading...). In some ways, this shift is psychological as "I work on the pricing team" evolves into "I am responsible for accurate pricing of client assets." More tangibly, operations models that were once organized by high value v. low value tasks and now aligned by domain. Today, data-centric firms allocate staff by high value or low value data domains, mastered v. non-mastered data, and acquired v. derived data, as opposed to solely functional lines. Once achieved, the Ops v. Data wall really disappears and the operations is more aligned to scale, cross-train, and balance the needs of both BAU and projects while retaining control of their data.

Better measurement of servicing costs

It's important that we begin to talk about all the data assets a firm has at their disposal, not just the ones it buys or receives. Deeper, more fine-tuned operational statistics can improve the way an investment manager operates on a daily basis. This too is important data! Measuring the true cost of servicing a client, or set of products, is mandatory in today’s times given our industry's unrelenting fee compression and the rise of systemic/passive investing. To be clear, this goes beyond allocating market data costs. These costs have exploded recently so it is natural for firms to focus on procurement and reducing acquisition costs. Data-centric firms look at the non-market data costs with equal zeal. The true bottom line of servicing includes the cost of business and technical maintenance, stewardship, and iterative development of legacy apps. Data-centric operations have the ability to broaden their view of data and serve up new information to management that can help the top and bottom line. In turn, these firms then use this more informed view to make good platform and client service decisions, and right-size support.

The data-centric culture offers the organization alignment and better control of its data assets.  Further, it provides firms the ability to manage data for the benefit of many consumers, and to actually measure and improve on the cost of servicing a client, channel or product set. It’s easy to say that an organization is data-centric, but when it comes to operational data and functional silos, we often see firms lose out on the opportunity for collaboration and hidden cost efficiencies. That said, if you’re new to the data-centric organization, don’t skimp on the fundamentals—download our No BS Guide to Data Governance for our guiding principles on data management.