Learning to Love and Value Asymmetrical Data

A guiding principle for business intelligence teams and distribution organizations is that the best available data should be used to make decisions. This sounds obvious and simple, but the reality is that it is becoming more challenging to do this.

Just for starters, distribution organizations are navigating team structures, multiple wrappers and different levels of partnership with distribution partners. The challenges to data strategy reflect this complexity—there is ever more data, data sources and creative ways for firms to personalize data to their distribution teams. Looking across the distribution environment there is a sea of non-standardized data.

However, dashboards and advisor scoring calculations yearn for standardized data across all advisors. We have seen firms opt for standardization to achieve more simplicity. Some of the standardized data comes from vendors who run different data through algorithms to force it into standardization. For all the good intentions, firms that are ignoring data sets are putting themselves at a disadvantage.

The embodiment of the issue can be seen with broker-dealer data packs. These data sets are asymmetrical in terms of the types of data, quality of data, frequency and availability. Attempting to build a standard advisor profile or advisor score that cleanly fits all advisors across various intermediaries is nearly impossible when relying only on data packs. Segmentation is more frequently cross-channel, so it is difficult to find the common denominators across advisors without throwing out some data points. Often the data that doesn’t assimilate well is that which is powerfully unique to a good data pack.

So what action can BI and distribution teams take to balance the complexity of data with the need for delivering simplified insights? There are three considerations we advocate for asset managers to incorporate into their data strategy.

• The human element—Input into how to value asymmetrical data and interpretation of data in different circumstances are vital overlays to data strategy. Employees with experience both in the distribution business and who have seen the impact of data-driven strategies will be the best fit for roles that are empowered to shape how asymmetrical data is sourced and used. Internal desk associates are often tasked with these roles that bridge data management and distribution. However, we have recently seen more experienced individuals move into these roles—for example, a firm converted a field sales professional into a BI professional to work between the BI team and divisional sales managers.
• The technology element—AI will be crucial in deriving insights from non-standardized data and helping asset managers leap ahead in the analysis of which data is most significant in driving impactful actions. The power of AI systems will offer iterative and more flexible approaches to relying on disparate data sources. Most asset managers will work with vendors to access AI, so there should not be a pit in your stomach when thinking about how your firm will build this capability.
• The flexibility element—Data strategy should strive to be quantitative without forbidding flexibility. This starts with data integration—importing all disparate data cleanly into repositories where it can be effectively managed. This is a massive undertaking but will make it clear where there is more granular data and where there are data gaps. Implementing this data into advisor scoring and performance benchmarking is where flexibility must be valued. For example, if your firm has advisor-level insight on private alternatives use only at one distributor, then incorporate the data outside of the standard segmentation or scoring model. If gross sales and redemption-based data and all of its derivatives are highly determinant of success measurements, then it should be included even if it is not available across all vehicle types.

Data should reflect the dynamic that distribution teams are embracing. So you should ask yourself of those standalone data points, “Does this provide valuable insight on our most critical markets, even if not standard across all advisors?” If it does, it’s important to keep the most true version of the data available. Relying and investing in distribution resources (humans and technology) can balance the goal of using the best data while delivering simplified guidance. After all, the goal with data is to take complex inputs and create useful and simple insights, rather than simple inputs to create useless and complex outputs.

The SS&C Distribution Solutions team works with asset managers across all stages of the data implementation process and is a ready resource to provide insight on best practices and solutions to optimize your data-driven strategy.