As the importance of data increases, so does the attention paid by regulators to how it is managed, increasing the data management burden on buy-side firms and prompting them to consider new data management models, such as managed services, says Steve Cheng, global head of data management solutions, RIMES Technologies.
Data is a vital component of buy-side business processes. As a consequence, data governance is increasingly being seen within the industry, and by external regulators, as a board-level responsibility leading to an increasing focus on improved data management. With the rapid growth in complex data and new regulatory requirements, many firms are reaching a tipping point with their existing data management models, and are actively considering the adoption of new approaches for managing market data.
Successfully generating returns on an ongoing basis means that data— especially index and benchmark data—is becoming more complex, and the buy-side is having to work harder to generate investment returns, while being forced to do more with constrained resources. Complexity is also increasing from both a demand and supply perspective. On the demand side, the likes of pension funds and other asset owners are embracing more sophisticated investment strategies that require more customized and blended benchmarks. On the supply side, data vendors are offering a proliferation of products to support new investment strategies, asset classes, and data types. All these new and complex types of market data are increasingly costly to license, process and manage.
Most importantly though, the ongoing introduction of new regulatory requirements creates a perfect storm for the buy side. Many firms that have come to rely on enterprise data management (EDM) tools and data warehouses are struggling to manage the growing challenges posed by this challenging confluence of cost, data complexity and regulation. As a result, many asset management firms are realizing that the time has come to adopt a new approach that embraces managed data services for improved data management and data governance.
To survive these rapid changes, firms need to look out for several crucial characteristics, including:
- Flexibility: Change is a constant in the investment industry. The buy-side needs to respond to changing market conditions both strategically in the long term, and tactically in the near term. As firms look to bring in more business, they need to be able to efficiently and effectively add new datasets and make changes to the existing data being used. Firms also need flexibility for strategic change, which includes updating business systems and existing data management architectures to meet regulatory and market demands.
- Governance: In response to regulatory demands, investment managers must be able to support and deliver evidence of good data governance. This includes being able to show where data came from, what has been done with it, and how it is being used. One of the reasons that EDM tools and data warehouses have often failed in this area is that data is stored centrally but then extracted to multiple locations, making it difficult to track. Good data governance and customized managed services empower streamlined processes to answer these evolving regulatory demands.
- Total Cost of Ownership: As firms need to find new ways to achieve flexibility in data management, they must also have solutions that maximize their return on investment. The total cost of ownership of using EDM tools for data management is often not fully understood. Firms are increasingly seeing diminishing returns from incremental changes to existing data architectures.
Even though traditional data management architectures lack the flexibility and market responsiveness required today, the buy-side has managed to survive thus far. Still, most firms struggle to meet existing operational requirements, let alone support good data governance. Plus, the total cost of ownership of legacy solutions is expensive.
As a result, firms are reaching the limit of incremental improvements to existing data management architectures, and there is an increased need for solutions that can weather all the rapid market changes. The ongoing and growing conversation regarding market data management issues is increasingly focusing on the concept of managed data services.
Adoption of a managed data service approach is growing as the preferred method for data management as it provides the exact data that is required by disparate business systems within asset management as well as the flexibility, governance and total cost of ownership that will help firms easily adapt to rapid market changes. There is a general misconception that asset management firms are operating their businesses with similar data. However, the requirements for business processes within each of these firms vary widely, based on the requirements of application systems and the end users (internal and external) that they are serving.
As a result, it is imperative that firms implement a service that is fit for purpose and can offer data fully customized to meet the heterogeneous requirements of different business functions. In terms of governance, with managed data services, the data used throughout a firm’s process is easily discernable. For regulatory purposes, firms are equipped to identify the sources of their data, how data was used and by whom. Finally, a managed data service also factors in total cost of ownership as a firm itself does not incur the cost of having to manage data as that is left to specialists, freeing up internal resources to focus on key business initiatives.
The industry, including not only vendors, but also asset managers, is smart to be increasingly addressing the issues around effective data management. As the impacts of regulation take further hold and lead asset managers to the tipping point and beyond, the time for action is now.
RIMES global head of data management solutions, Steve Cheng, was recently featured on Inside Market Data’s “Open Platform” providing insight on the importance of implementing new data management models to better deal with increased volumes of data and regulatory demands.