It seems like an easy process. Identify differences in the numbers, data, or information, correct exceptions and move on. Unfortunately, it often does not prove that easy. Some of the reasons for that are:
- The data doesn’t always line up
- Matching needs to be more intelligent than it often is
- Too many business users are using tools like Excel because it’s the only tool they have access to (and are able to easily use) that can come close to helping them
- There are (at least) two sides to every reconciliation, and each side is often doing their own reconciliation of data. THIS IS INEFFICENT!
We often find that companies and users fall into one of two camps: they either have a data reconciliation tool that has been setup and supported by their IT organization or they use a tool like Excel, which they can work with, to try to eliminate as many manual steps in performing a data compare, as possible. In the former, where IT organizations are the primary source of support for data reconciliation, there can often be a fall-off in focus and/or level of support once the project ends and other priorities take hold. In the latter, while users are empowered to create solutions on their own, those solutions are built in tools that aren’t ideal for data reconciliation or data compare and are wholly unaudited. Sometimes their existence is completely unknown to everyone but the person who created it.
Other Priorities Take Hold
Companies go through cycles where they are focused on one thing or another. It’s not only normal, it’s often necessary to get things done. In the case of data reconciliation, companies will often stand up a project to implement a software application to make things easier on the business group performing that function. As the project comes to conclusion, IT organizations ramp down the project resources and put support in place. They enter “maintenance mode.” The business has had their requirements met, can use the new application, and simply need to be supported, going forward.
The problem with this approach is that business requirements change and evolve. It could be the day immediately after the last day of the project and a whole new set of requirements arise. So, what happens? Often, those requirements either go unmet, are placed in a queue to be addressed when “budget is available,” or are only minimally met as resources become available. If enough time goes by without help, business users will often develop their own workarounds to minimize their own pain.
Enter: The Workaround
When timing becomes critical or the pain that a user is experiencing is high, people adapt and find ways to get stuff done in creative ways. In the case of data compare or data reconciliation, that creativity is often expressed in MS Excel, MS Access, or other tools that non-technical people understand and can work with. While that solves the immediate, tactical problem, it creates other problems for companies, not the least of which is increased business and key-person risk.
Few can be blamed for taking this approach. Business doesn’t stop and the volume of work to be completed by reconciliation groups only increases over time. The Workaround, however, is not a good long-term solution, is fraught with risk, and only costs companies more in the long run, as those workarounds pile up and are eventually unwound.
Alternatives In Reconciliation
The situation above is very typical, but it also assumes that there aren’t other, better ways to address IT resourcing challenges or avoid the development of workarounds by non-technical people. More and more, software is evolving to allow non-technical users to perform very complex, historically technical tasks, simply through evolved user interfaces and increased focus on User Experience (UX).
While I am not a fan of the term, citizen-user enablement is a real thing, and it continues to get traction across many areas and industries. This approach is all about empowering business users to perform historically complex or technical tasks easily. In the case of data reconciliation, it means letting users build matching rules using natural language. English sentences, instead of programming languages (SQL, Java, C-sharp, etc.). Or, providing the user with an interface that allows them to drag-and-drop fields for comparisons, using Machine Learning (ML) or Artificial Intelligence (AI) to build the match rules and data compares for them, or any other number of techniques that make the user experience straight-forward and simple.
When you give non-technical people tools that are friendly to them and empower them to do more, solve bigger problems, or increase their own efficiency, they grab hold of those tools. Those tools exist today and will be increasingly embraced as time goes on.
Another new, realistic option, made even more feasible due to the rise of Cloud Computing and related technologies, is Collaborative Reconciliation. In a traditional approach to data reconciliation, you have two companies attempting to keep their data in sync. For example, Company A has recorded a list of transactions that they completed with Company B, and they ask Company B to supply them with a list of transactions that Company B believes they have completed with Company A. Company A takes their list, compares it to the list of transactions provided by Company B, and they note any exceptions. Similarly, Company B performs the same activity, just using their list as their “source of truth” for transactions that were shared between the two companies. Effectively, the same data reconciliation, or data compare, is being performed twice. This is inefficient for both companies and, while the reconciliation is necessary, this way of going about it is unnecessary.
In Collaborative Reconciliation, each reconciliation is performed ONCE. Instead of Company A performing the reconciliation and Company B independently performing their own reconciliation, each in separate tools, a Collaborative Reconciliation is performed once, by both companies, in the same tool. Company A submits their transactions, Company B submits their transactions, and both companies are able to see any exceptions between the two. From there, they are able to work collaboratively to document, note, and resolve the exceptions with greatly reduced friction.
Data Reconciliation IS Easier Than Ever
The biggest issue with reconciliation is knowledge about the tools available today and a willingness to step away from complex, tech-heavy, legacy products of years past. Change is hard for most and it’s easy to adopt an “if it isn’t broken, don’t fix it” mentality. After all, many companies with data reconciliation software tools have made a significant investment in them.
The question should not be how much has been invested already in the build-out and use of these legacy reconciliation or data compare tools. The question should be, “How exposed are we due to workarounds being developed because our current tool doesn’t support the business need any more?” Or, companies should ask whether or not their data reconciliation team can keep up with the pace of change in business with their current IT-dependent toolset.
The process of data reconciliation is easier than it has ever been and it will continue to get even easier. If you are struggling with the reconciliation process, the answer very likely lies in the tools that you are trying to use to get the job done. You don’t have to get into bleeding-edge technology to find significant gains in: match rates, productivity, risk reduction, cost savings, or any number of additional metrics that are important to you but are suffering because of your current reconciliation process.
Emissary For Data Reconciliation and Data Compare
If you are struggling in this area, we can help. We can provide solutions in all of the areas outlined above. Improve productivity, reduce friction, simplify. We love helping companies improve how they operate. We know and believe that any complex problem can be simplified, and that’s what we do.