Mar 21, 2023

Many asset management firms today are focused on deploying a modern data platform, but that’s just going to cover the technology part of the equation.

What about your data management team itself?

Has your data management organization kept pace with modern data operations practices and adjusted to today’s higher requirements for quality data? Does the team provide support for self-service data and analytics? And have processes for onboarding data kept up to date with the technology?

Without the right organizational structure and data management practices, a modern data technology stack won’t solve your firm’s problems associated with older technology. In fact, without the proper support, it’s probably a waste of resources to spend money on the latest technology alone.

Most asset managers have traditionally fielded a data operations (or data integrity) team, which is usually limited in its responsibility in terms of the data and stakeholders it serves. A typical data operations team handles data entry and mastering security reference data, with a focus on the needs of trading, pre-trade compliance, investment operations, and accounting. That means focusing only on the key attributes of held securities needed to support those functions. Data operations teams also master other reference data domains, but again, with the same limited scope of stakeholders.

DataOps Can Lead Your Data Efforts

With the adoption of data governance best practices, firms recognized that their existing data operations team was one of the few groups with dedicated data professionals who were already engaged at least partially in many of the data management best practices that these firms sought to roll out across the enterprise. However, the following problems as well as new challenges accompanied this old approach:

  • Limited scope of data domains and data consumers supported by the team
  • Lack of strong data governance practices
  • Inability to keep up with accelerated need for new datasets
  • Resource demands required to support increase in self-service analytics

Today, firms are responding by upgrading their old data operations teams into DataOps teams. The old data operations work still must be done, but now it’s just part of the DataOps team’s responsibility. As a name, “DataOps” has become popular to reflect the growing firm-wide analytics focus of these teams and the increased need to shorten the time to provision data for the investment, operations, client service, and data science teams.

Broader Scope for DataOps

Investment firms’ data needs keep expanding and although ESG data is a current focus, new datasets to support the portfolio management teams are always evolving. New compliance and regulatory changes also add new data requirements. DataOps teams now get involved in onboarding new data sources and finding new uses for existing data.

Such efforts require data analysis to evaluate the fit for a given purpose and identify and correct problems with the data. DataOps teams are adding these data analysis capabilities to their teams, which also are aimed at supporting project work, and ensuring that the right data sources and elements are used as part of new implementation or development work. By investigating and fixing data issues, these data analysts can provide support for data stewards.

What’s more, DataOps teams help get data into people’s hands, focusing on analytics and self-service access. As new self-service data delivery solutions are rolled out and achieve wider adoption, firms recognize the degree to which data consumers need a stronger level of support. Data catalogs are essential for self-service data consumption, and someone needs to maintain them. Once again, firms now realize that their DataOps team members are best positioned to provide this support because they’re familiar with the data and its uses. These teams also help by leading or providing a center of excellence for analytics.

A DataOps team needs to take responsibility for the data requirements of all data consumers, including investment research, risk, performance, attribution, marketing and sales, and client reporting. This brings a whole new set of requirements to the data effort. DataOps teams support a broader set of data, including domains like portfolio data, client data, distribution reference, and competitor data to the traditional domains like security master, benchmarks, and pricing. Data teams are no longer hiding in the operations group or spread about in pockets across the entire firm. Firms now concentrate data teams into one entity, which allows the high data quality standards and best practices that these groups use to also be applied to the new domains.

DataOps Role in Data Governance

As DataOps teams take on a more enterprise-wide role, the practices used to support the data must keep pace. This requires more effort to create visibility, ensure data quality, and provide the needed controls. These tasks can’t be deferred to an IT group or relegated to part of a project process; they need to be part of the DataOps team’s daily process.

DataOps now provides data owners and data stewards for many enterprise data domains and focus on the firm’s data governance practices. The fact that DataOps teams now focus entirely on data management, rather than treating it as a part-time activity, allows them to help drive governance standards as well as deliver the data. Firms that once struggled to get the required focus from part-time data stewards for high-demand data domains now have full-time data professionals managing the data.

At some firms, the data governance leadership function now falls entirely under the DataOps team’s purview. This does not mean that the DataOps team must take responsibility for data stewardship and ownership for each and every data domain. For example, the accounting team still takes responsibility for providing the rest of the firm with a reliable ABOR, and the performance team continues to handle the performance and attribution data. Instead, it means that the DataOps team provides program management, training, and oversight to ensure that data stewards throughout the firm follow the same disciplined standards and practices that DataOps team members have developed for themselves.

The Future of DataOps

As firms recognize the value of a dedicated team of experienced, well-rounded data management professionals, the trend of asking for and getting more from the DataOps team will continue to grow. Firms will tackle the prevalence of manual data entry and manual data quality assurance processes across a wider array of data domains and achieve greater efficiency through automation and leveraging evolving AI and machine learning technologies. All of this will require thoughtful leadership and engagement from experienced experts.

For global firms struggling with timing issues and consistency of data and data management processes across geographical regions, DataOps team members are best positioned to help, given their experience solving those issues. While a modern data platform can potentially help solve some data management problems and enable new capabilities, it creates even more demand for effective data management practices. We believe firms that invest in the people who are best positioned to provide these services will reap the greatest rewards.

If you are interested in learning more on the topic of data management, take a peek at our Best Practices for Self-Service Data Analytics infographic.

To speak with a Cutter consultant about this topic, contact us at [email protected].