- Kingland Platform
You type Bank of America into your system to discover important relationships. There's subsidiary A. And there's subsidiary B. You find several pieces of information regarding changes to the board, people filling senior-level positions, and you even see a listing of products and services.
The problem? The return of information can be maddening. You get pages of results, similar to a Google search, but you have to hunt for the connections. The lines of relationships connect to a few points and other lines lead to a dead end. It's a challenge to understand how different entities connect.
Names, companies and locations change. It's a battle to keep up with accurate information. Ten years ago you may have had 500,000 entities that were important in your world. Today, you may have over a million entities, and they're your clients, counterparties, suppliers, prospects, and more. It's a common problem in the capital markets and public accounting industries. Numerous data fields hold a single 'fact'. There are collections of fields and countless files holding related records. While hierarchy data management is hellacious, complex and overwhelming, this information is important and must be accurate.
Hierarchy data can help you see the relationships among your data, giving you a better understanding of how everything relates. But the value of hierarchy data can't be realized until you get a handle on these four areas:
In our more than three decades of enterprise data experience with hundreds of millions of records, we see two common threads throughout the industry when it comes to hierarchies - the data is usually filled with inaccuracies and hierarchies can be very expensive to manage. We've seen it all.
In our assessment, more than 90 percent of the issues related to hierarchy data fall into the following four categories.
This involves entities that are either missing immediate or ultimate parents, were set-up quickly without any parent information, or haven’t been maintained appropriately with the correct parent information. Oftentimes called stand-alone or orphaned entities, these records are easy for users to set up, but difficult to identify. This category of hierarchy data issues is the most common.
Everyone wants a golden copy or single version of the truth, but that's easier said than done when it comes to relationship data. Creating an immediate parent field is easy. Over years and years of data accumulation, we have seen a significant amount of inconsistencies in the rules used to populate these fields. The biggest hitter here is often fund or special purpose vehicle related entities, but we also see problems when it comes to less than 50% ownership and what to do when an entity has multiple owners, but only one field for the data. Additionally, it's common to see a need for a sales hierarchy vs. a risk hierarchy, which leads to inconsistencies in the data as enterprises try to move toward those golden copy data strategies.
It should be pretty easy to roll up entities to an ultimate parent, but challenges start to emerge as the data changes. As duplicate entities enter a system and are de-duped, the surviving ultimate parent relationships are often not analyzed as part of the de-duplication process. Additionally, as mergers or acquisitions occur, if the ultimate parent field is stored independently from a full hierarchy lineage, the result of merging two or more larger hierarchies together often leads to many ultimate parent contradictions or inconsistencies. The result? The ultimate parents, seemingly the easy entity to get right, simply don't make sense in the context of the whole universe of legal entity data. This is common within legacy systems.
The least common but most impactful of this list, a circular relationship exists when entity A is listed as the parent of entity B and entity B the parent of entity A. While this should be easy to identify, it becomes more complicated as multiple levels of relationships are inserted between two entities and as entities are named similarly or as duplication exists. Controls can help these situations, but when considering the other categories of issues listed above, these circular relationships become a bit more challenging than you might expect.
So what can you do? The simple answer points to the basics of any good data governance program. You need to assess the situation and quantify where you're at. When successful, data governance helps companies avoid inconsistent data silos in different business units. Compliance and regulation mishaps fade away. Decision makers use reliable information to make better informed business decisions.
Follow up with consistent rules, consistent data ownership, and technical controls to guide your way forward. You'll peel away the burdensome layers of data management and have more control over your data.
And last but not least, you need data maintenance to ensure that the hierarchy data is kept up to date. Organizations have spent billions of dollars on the right technology in order to "get their data right." The inherent complexity of hierarchy data makes this one of the top challenges.
While these aren't the only four issues you need to focus on, managing these common issues will improve how well you and the enterprise connect people, applications and processes with reliable data and analytics.