These 3 Data Problems Could Be Draining Your Company’s Resources

Spend Matters welcomes this guest post from Brian Alster, global head of supply and compliance products at Dun & Bradstreet.

When you’re working on estimating the costs of your third-party relationships, the last thing you want to find are holes or inaccuracies in your data. It can leave you unable to make informed, data-driven decisions.

This fear might explain why, according to Forrester research, almost a third of enterprise architects and chief data officers spend at least 40% of their time discerning the credibility of their data before using it to make critical decisions. After all, it’s easy to justify such a time investment when you consider the alternative: Gartner found that organizations lose an average of $15 million each year because of subpar data quality.

Through our work with customers, we’ve seen the consequences of data problems firsthand. For example, a customer was once left wrestling with a mix of enterprise resource planning systems following a number of acquisitions. The multiple ERP systems prevented the company from having a comprehensive view of the total cost of each third-party relationship. As a result, company leaders were unable to analyze their data in a timely manner, causing the company to lose money in the process.

Any organization, from a startup to a multinational corporation, can suffer from bad data. Here are three data issues you need to understand and correct to help your business avoid costly errors in spend analyses:

1. Duplicate Data

There are many ways for a company to create duplicate data: multiple registration portals, mergers and acquisitions, data entry inconsistencies and more. Having more than one record for a certain vendor might seem harmless enough, but the consequences of having duplicate information within your company’s vendor master are wide-ranging and expensive.

The main issue is that duplicate data prevents organizations from identifying cost savings opportunities. Duplicates also limit your insight into enterprise spending and vendor relationships, making it impossible to know the exact price you’re paying for a specific item.

To prevent these errors, consider aligning your vendor records to a persistent permanent ID — essentially, this is an identifier that a specific piece of data can always use. Once your data is sorted out with PIDs, you’ll be able to take a deep look into your database using visualization tools like data heat maps and real-time alerts. This will help you understand any inefficiencies you have in your vendor database, thus reducing duplicates and allowing you to spend more money where you need it.

2. Inaccurate Data

Per an article in Harvard Business Review, nearly half of new data records contain some type of vital mistake. Corporate actions, merger and acquisition activity, and bankruptcies are just some of the events that can affect spend data and make these errors a common issue among procurement professionals. The frequency in which business data changes is astounding and can further complicate data problems; our company found that 64 businesses per hour change their address and 159 new businesses open their doors during the same time frame.

Too many companies fail to accurately track and capture these corporate changes on an ongoing basis, which creates confusion around vendor relationships. If, for instance, one of your suppliers were to declare bankruptcy, you would want to know as soon as possible to understand your mitigation strategy. By creating a comprehensive due diligence program that continually monitors third-party relationships using periodic campaigns or surveys of your vendors and a third-party verification source, you can mitigate that risk and capture changes in the vendor portfolio in real time.

The bottom line is this: Business data is always changing, making it essential that your data remains up-to-date.

3. Conflicting Data

When companies have multiple data sources, managing conflicting data can get tricky. Anything from a simple timing difference on self-identified data to latency in updating vendor systems can cause discrepancies in the data.

This not only makes it harder to provide an actionable analysis, but trying to sort these discrepancies out also wastes time and money. To prevent erroneous entries in the first place, companies should create data interoperability so they can manage analytics, regardless of multiple data collection methods. And by mapping data to a common key, they can still align the data despite having disparate systems.

There’s nothing worse than getting all the way through a data analysis and realizing that you were missing crucial data — or that the data you had wasn’t accurate. By understanding each of these data issues, you’ll take the first step in helping your company remedy current data problems and prevent them in the future.