Every day employees spend more time than they should trying to find the best available data to make decisions. While some of those decisions are more vital to business operations than others, how would people know in the moment? Leveraging poor quality data for one key decision could have a significant, expansive downstream impact on the corporate bottom line. The bigger issue is that if the data is of poor quality, it could cost the organization unknown amounts of money in a variety of ways that may only surface in “big case” scenarios. However, the smaller ones which go undetected and become incremental for months or years, could cost far more.
Individuals are focused on getting their job done to the best of their ability and will use the sources of information that are available to them. In many cases these sources have no measures to express the quality of the content and individuals use them assuming they are accurate and complete; something that is broadly acknowledged as being incorrect. This is why it is so important to improve the quality of all data sources that are used for making operational business decisions. It’s also important to retire those which are insufficiently accurate or current to warrant keeping around. There may also be significant savings to be gained by retiring them in terms of the savings from unused licenses and support, which is something we will get into in a future blog.
When entering into a situation where a decision needs to be made, an individual will seek out the data sources they need and are aware of to help make that decision. In most cases, they already know how much time and effort they will need to exert to massage the data in order to get it into the right format or context. They may even know that the data needs to be validated against a second or third source before being acted on. All this expended effort takes significant time, and time is money.
If you could improve the quality, remove the need to question the accuracy, and eliminate the comparisons against secondary or tertiary validations, your employees could not only act much faster, their decisions would be more informed, leading to savings and improved business outcomes. Organizations need to help their employees by improving the quality of data they are consuming. Improved data quality is vital to all business operations and reducing the inherent risk of making poorly informed decisions helps with resource retention.
Finding the best data for normal daily decisions doesn’t have to be a struggle. The environment can be improved by simply improving the data quality of some sources and eliminating those which are too far gone to save.
Once this is accomplished, the ability to understand how decisions impact business operations become much clearer. Employees can then act far more intelligently in prioritizing work and focusing on those efforts that have greater downstream impacts.
Improved data quality leads to a broader sense of knowledge on how the business operates, resulting in costs saving at all levels. 451 Research recently published a report titled “The State of Enterprise Data Quality 2016: The Role of DQM in Machine Learning and Predictive Analysis”, which discusses the current application and adoption of data quality initiatives across 200 enterprises. The full report can be downloaded at this link. You can view the recorded webinar discussing the findings here.