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3 Data Lessons Learned from the Front Lines of COVID-19

Here are the top three takeaways we have discovered that contribute to data management success for buy-side firms.
Christopher Bloechle
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3 Data Lessons Learned from the Front Lines of COVID-19

At investment management firms, the COVID-19 pandemic has revealed areas that could be improved and, more specifically, has exposed common data and data management challenges. These three key lessons will shape post-COVID-19 expectations of data management and analytics.

According to a recent Gartner survey, a mere 18 percent of companies indicated they were prepared for the impact of coronavirus. Since the start of the crisis, it has become clear that the data and analytics challenges facing firms are critical and urgent, catalyzing rapid shifts in priorities.  

After ensuring continuity of operations, many firms focused on their firms' operating model, determining what worked and what needed improvement. Once armed with that knowledge, they began to develop plans to adjust operating models, utilize new data sources, and build agility into their investment decision processes.

Consequently, the focus on data management priorities is now a new norm as firms meet current needs and prepare for any new and future scenarios. We have seen several priorities taken on a greater focus for buy-side firms:

  • An agile data platform is key to data scaling and ingestion of new sources
  • Unifying siloed data for AI and ML-based analytics is performance-critical
  • Data collaboration should focus on speed and quality of insights
  • Controlled governance with a focus on reducing risk is paramount  

Portfolio BI has worked with hundreds of firms to implement and develop data solutions to accomplish these initiatives and support these shifting priorities. Here are the top three takeaways we have discovered that contribute to data management success for buy-side firms.  

Takeaway #1: Agility is critical, but it requires a strong foundation

Accelerating speed to market is challenging enough, but when it is executed at the scale of data that today's buy-side firms manage, the process can seem impossible. In today's environment, manual processes cannot scale to the challenge. Solving the demand for analytics drawn from vast amounts of data sources while delivering insights in time to be actionable is a core challenge for firms that want to compete with agile, data-driven competitors.  

Agile data operations are the key to moving forward with coronavirus initiatives and will ultimately be fundamental for future crisis-ready data management.

Takeaway #2: Streamlined Data Operations delivers scalability

The enormous quantity of data today is both daunting and disabling. Being able to deliver actionable insights across the firm's areas provides an advantage for firms capable of nimbly scaling their data management environments. Centralizing your data is the first step to being a data-driven organization.  

Hedge funds, particularly larger funds, often have a very complex set of data warehouses. Cloud-based database technology empowers them to leverage that data in innovative and simple ways.  

Having a data platform capable of ingesting vast amounts of near-real-time data at scale is critical to ongoing success and differentiation in today's industry.

Takeaway #3: Data governance with an end-to-end view reduces risk

The key to governance success lies in a platform that tracks and visually re-creates the lineage of data from ingestion all the way to consumption. Accurate and enriched data allows your business to run correctly and enables your people to make the right strategic and operational decisions. Having a single source of truth for your firm with the most relevant, actionable insights reduces risk.  

We have repeatedly seen that firms who started the crisis with these functions already in place were better positioned to scale and grow in the aftermath.  

Implementing the Takeaways

In the end, implementing an enterprise data management solution ensures your teams have both the accurate and timely data they need, ultimately facilitating better analytics through collaboration and transparency, and creating a scalable foundation for the future, unknown challenges.

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