Steps to Remove Variant Data from your Multiverse

Your data commits crimes against your organization daily, creating variant data throughout the enterprise.

Borrowing from the Disney Loki series, we imagined the similarities between the Marvel Cinematic Universe (MCU) multiverse and client data in the enterprise.

For years, companies have wrestled with how to manage client data. Excel is a popular workaround for data workers who average 7 hours per week manually updating formulas, pivot tables, and cell and sheet references. Add in multiple sources, variations on attributes of interest, and volumes of data, and it's a complex situation that affects accuracy and efficiency.

As data makes its way through all the different groups and systems, it starts to morph, splintering your data universe into multiple variations of the truth. The same is seen in the Loki series when the timelines converge, creating different variants of individuals throughout the multiverse appearing on the same timeline.

We see this within enterprises all the time. Data workers use excel files to augment their work, but this often creates multiple versions of the truth.

Disparate business units want to look at data differently. Each business unit swaps pieces of data and creates alternative versions of a hierarchy, for example. When this happens, you've gone from an ideal situation with a single source of record to multiple variants that create havoc on operations.

And as you try to bring everything back together, how do you know why data is different across the disparate silos?

Understanding the uniqueness of data can help organizations rid themselves of data variants and achieve a single source of truth.

Matching on common identifiers works well when all groups have standard rules for how they apply an identifier. This can become problematic when separate units behave differently – such as when one business unit applies the LEI to any subsidiary within a hierarchy where the subsidiary entity doesn't have an LEI assigned. Matching on common attributes works well – as long as everyone accurately enters their data and maintains it over time. Matching on historical versions is complex. When you know Company ABC used to go by Company CBA, you can introduce that history into your matching and pull forward groups that have not kept up with corporate actions, such as reorganizations or mergers.

Is the historical versions method perfect? Not exactly. This can be problematic in some use cases. For example, a firm may merge with a Special Purpose Acquisition Company (SPAC) and reuse another firm's historical name by incorporating in another state where they didn't do business. The two firms may have a common name across time, but not have a common history.

Why One Universe is Better than Many

When you attain a single source of truth, it makes so many other things easier.

First and foremost, you actually understand who your customers are, and how many customers you have. You no longer have to sort through the many variations of data that apply to the customer. Do you want to go to one central system to understand your exposure? Or would you rather speak with 15 different groups to try and figure out what each one tracks? When you have to aggregate data across separate systems, you now have to rely on each business unit to follow the same expectations.

Now do this across the family tree to understand your total exposure to JP Morgan subsidiary entities, for example. If you can't track the relationships among the organizations, you're in a difficult spot. How will you understand the underlying relationships if you don’t have a common view?

In a perfect world, your functions are streamlined without variant data running around. You're running reports instead of having every business unit figure out the requirements and provide the data you need. You have a system that allows you to see the macro pictures of how your business operates, the real risks, and who you need to care about.

Where Should I Start?

Understand all the places where your data hides. What are your data quality rules? What common standards and conventions will you implement as a firm? This is where a data management group helps, getting information into your system and keeping it correct moving forward. Subject matter experts will understand what's unique about your data and how to keep it unique to support your business practices.

At Kingland, we've managed data and we understand what's essential about data. We know how to bring in many different sources to get to the best version of the record. Our work with clients allows us to bring together the full power of a secure cloud platform with proven data capabilities customized for specific business needs, much like the requirements needed to prune and manage data variants.

Follow the steps above to overcome variant-producing actions such as working from Excel files and improve your data's trust and quality.