Four Steps to Fixing Your Bad Data

It is widely known that S&P downgraded the U.S. government debt on Friday, a decision which has been heavily contested due to a $2.1 trillion error in their original rationale. Despite officials pointing out the error and S&P's earlier mismanagement in rating bundled mortgage products, the reputational damage done to the company is inevitable. This incident is a prime example of the risks of poor data quality, with much more severe repercussions than just a tarnished reputation.

Give them bad data

The financial crisis was caused by more than just greed - it was a result of bad data. False mortgage applications, incorrect credit ratings, and untrustworthy balance sheets all contributed to the destruction. And, unfortunately, the problems are still present today. Counterparties are suing each other, and courts deny foreclosures when the paperwork is unreliable.

It's not just finance that is affected; bad data is an issue for many industries. Health care, the military, and the food industry are just a few areas that bad data compromises. In marketing, it cannot be easy to target the right customers. In procurement and logistics, deliveries may be sent to the wrong places. In manufacturing, components may not be compatible.

Bad data can have a significant impact on customer satisfaction. Industries must take steps to ensure that their data is accurate and up-to-date to avoid costly mistakes.

A company that doesn't take data quality seriously is setting itself up for failure. Sending out incorrect bills to customers is a prime example of bad data that can have disastrous consequences on customer satisfaction, reputation, and revenue. The best way to avoid the pitfalls of bad data is to ensure that the data being collected is accurate and up-to-date. Implementing processes and procedures to ensure data accuracy will help ensure that customers receive correct bills and that the company operates with reliable data.

Acknowledging a data quality issue is the first step towards resolving it. After that, the following three steps should be taken to improve data quality: focus on the data being presented to customers, regulators and other external parties; implement data quality protocols; and review data quality on an ongoing basis.

Examine your system of controls thoroughly. Ensure that the proper authorities are in place and you correctly utilise them. Establish and execute an advanced data quality program. While it may be a practical short-term solution to ensure that data is sent out correctly, an immense amount of data is present and continues to increase.

Almost everyone recognises that data is one of the most valuable assets in any organisation. However, it is often overlooked, and the person responsible for data may not be easily identifiable. To ensure data quality, it is essential to have a quality program that prevents errors at the source. It is also necessary to take a more holistic approach to data management, examining how it is treated more generally. Doing so will help organisations realise the full potential of their data.

If this sounds familiar to you, it is time to implement a comprehensive data program that is backed by the right resources, authority, and commitment. This is not an easy task, but it is a necessary one. It is essential to be mindful that steps three and four are especially crucial; to ensure quality, many factors have consideration and managed adequately (such as focus, measurement, process management, and root cause analysis). Additionally, properly utilising data is much easier said than done; it impacts all aspects of the organisation, from how employees work and think to customer relationships.