Data management sits at the top of priority lists for asset managers and financial institutions. The ability to scale operations, stay competitive, meet regulatory demands, and more effectively serve clients, hinges on surfacing quality data that’s reliable, consistent, and timely.

But effectively developing and continually refining a data strategy can be overwhelming and can often hold firms back.

• Do you build and regularly upgrade the technology in-house? 
• Do you buy technology, and simply plug-and-play? 
• Do you partner and share infrastructure with one or more third-parties?

Many organizations are faced with interoperability challenges around integration of new data platforms and technology with legacy systems. Solving for interoperability is critical and should never be underestimated.

The pursuit of quality data also continues to be a challenge for many firms. According to EY’s 2023 survey of 50 asset managers, 40 cite poor data quality as a barrier to integration.

The scale of the challenge is one reason why Deloitte is seeing asset servicers increase their budget allocations in data management from 14% in 2021 to 35% in 2023.¹

As a global service provider, we see the data challenges that many of our asset managers and financial institution clients are grappling with. As we collaborate with our clients we often find that the most effective approach is the simplest one. Data management projects can be complex, expensive, and resource-heavy, so it’s critical to start with repeatable building blocks around specific and pragmatic business needs in order to help accelerate ROI. In the end, you have a data approach that is scalable and flexible to address future challenges.

So how can firms connect the dots between business need and data capabilities?

There are three priority points on the roadmap to a successful data strategy that might seem obvious, but should not be overlooked.

1. Take a balanced and flexible approach to technology

An organization’s technology portfolio can fluctuate, so technology must be complemented and augmented by multiple elements including a deep understanding of the data itself. However, there are near-countless vendors and tools in the market with a range of selling capabilities, many of which are complex to get up and running.

A key element of technology selection is to ensure interoperability and flexibility – critical operating datasets are almost always aggregated from multiple internal and external systems and it’s rare that formats, labeling, and connectivity is consistent across platforms. Keeping these tenets at the core of the evaluation process will help you preserve the best of in-house, vendor, and service provider solutions.

Seek out technology and service partners that allow you to combine a data solution with existing systems and processes that work well, to deliver meaningful results and reach business goals in much shorter timeframes.

2. Put Subject Matter Expertise at the heart of your data strategy

Regardless of how powerful technology solutions can be, business subject matter expertise (SME) is critical to effectively unlock quality data. Product SMEs must be highly integrated with an organization’s broader data strategy, and digital and data teams need to incorporate input from business experts who intimately understand the underlying data to meet business objectives and make informed decisions.

Managed service teams that are staffed, trained, and organized to manage the day-to-day maintenance of your data and get it into a usable form – identifying breaks, resolving exceptions, managing rules changes/updates, monitoring and providing real time oversight – are also critical.

3. Start small and then scale

Firms often attempt to boil the ocean in how they approach their data strategy. Given the ubiquity of business needs that data can help solve, it’s easy to overthink the enormity of the project and determine a ‘catch all’ approach is best. In some instances, firms launch data programs that are understandably wide ranging, but unfortunately not specific or targeted enough to any one area to generate meaningful economic returns overall.

In our experience, attacking concrete business problems yields better outcomes and efficiency faster. It is also helpful to prioritize data sets by volumes of usage across the organization so that once you have piloted one use case, it easier to scale and apply learnings to your next priority issues.

This approach creates a solution for one problem that delivers immediate benefits to a firm, while at the same time building the foundation to address others, for example:

1. Optimizing fund portfolio data for fund fact sheets
2. Tailoring shareholder reporting data for regulatory reporting purposes
3. Enhancing fact sheet data customized to separately managed account specifications.

Or in the case of trade information, the same updated data set can be useful for multiple purposes such as updating the Investment Book of Record, managing fails, regulatory reporting, and execution analysis.

A platform approach that uses data solutions to address individual business problems will enable a model that scales faster and can grow and expand exponentially as each new solution is deployed.

Conclusion

The road to effective data strategy development is complex to navigate. Increasingly, we see firms opting for solutions that create better business outcomes through interoperability and flexibility between existing systems alongside accessing both subject matter and data expertise.

This three-part roadmap provides the framework to create a scalable platform that can solve real challenges and have positive impacts across your organization.