The asset management industry is going through a fundamental shift in its approach to data. Today, as data transparency and frequency demands from both regulators and investors become more important and the complexity of investment vehicles and strategies increases, organizations are facing a very real struggle when it comes to their traditional data warehouse operations.
Asset managers and their service providers are awash in data that must be stored, managed and reported on, from client names, to transactional information, attribution analysis information, positional information, the list goes on. To be able to house all of this information in one central location, many organizations have turned to data warehouse strategies. However, today’s data warehouses are unable to provide the flexibility and agility required when navigating a highly volatile regulatory environment.
As opposed to data management platforms, data warehouses do not provide the consistent information the business needs and often result in multiple and inconsistent data sets being stored. Conversely, a data management platform enables businesses to take advantage of diverse kinds of information coming from a variety of sources that is then managed in such a way that the business intelligence output truly benefits the enterprise.
Many people incorrectly use the terms “data warehouse” and “data management” synonymously. However, there is a very distinct difference between the two. While a data management platform typically provides for data extraction for use on live applications and other downstream resources, this is not always the case for internal database resources like a central data warehouse. Another feature that differentiates the two is that a platform should be agnostic towards the different kinds of data it aggregates regardless of how seemingly incompatible it may appear.
Only a few years ago, many organizations in the industry listed the ability to get quality data as a major concern and challenge. But as the landscape shifted towards more complex instruments and increased transparency, the focus is now on defining strategies to incorporate all of this disparate and complex data into the format required for both regulatory compliance and shareholder reporting.
In an Information Week article, 5 Analytics, BI, Data Management Trends for 2015, only 51% of information management professionals surveyed put data quality on their “barriers” list for 2015, versus 59% last year, a clear indication that data quality concerns are easing.
Today, data management solutions provide a more integrated approach by eliminating the natural silos that data warehouses and other types of relational databases can cause. A more integrated data model can be shared across an organization and leads to more accurate results because of the inherent flexibility needed to meet more data standards and requirements. This is in direct comparison with data warehouses where rigidity is a primary component of the core architecture.
A few high-level recommendations for implementing a data management strategy:
- Take a systematic approach to implementing a data management strategy. Trying to incorporate all areas of the organization into the project at once will only bog down the process. With the flexibility of a data management platform, organizations can systematically implement a solution in a more deliberate fashion.
- Identify your key domain areas and then coordinate and configure one domain at a time. Use the initial domain models identified to help set the appropriate foundation to layer the additional requirements, domains, etc. into the project.
- Make the data management platform the master owner of the data and rules. When necessary, it should feed data into the data warehouse if a data warehouse or downstream recipients are to be maintained going forward.
Utilizing a data management strategy in place of a data warehouse strategy allows for more seamless reuse of data and domain models across multiple areas and corresponding processes within an organization. Significant efficiencies and a reduced risk profile are just some of the benefits realized through the maintenance and operation of a successful data management strategy.
As the regulatory landscape continues to shift and more complex instruments, fund structures and derivative instruments are introduced, it is imperative that data strategies have the flexibility to incorporate this unstructured data efficiently and seamlessly. Data management strategies provide a more fluid, flexible and integrated data approach that enables organizations to not only meet the needs of a sophisticated client base, but also the ever-increasing regulatory demands placed upon the industry.