As the technological landscape changes there are few issues more critical than data quality. Yet, despite the increased importance of data and the role that it plays in organizations, the question of quality remains a hurdle for many, one that they struggle to overcome.
Recently Forbes, working with KPMG, came out with a study that underscores this. About 24% of those surveyed claimed that they faced issues of data quality and accuracy, and that it presented an obstacle for them to overcome in order to reach their goals. Of the analysts and users that were included only 42% were confident in the quality of data. It is perhaps not terribly surprising then that 44 percent believed that their in-house analytics were not of the same caliber as third-party partners. This leads to increased costs for businesses, to the tune of $9.7 million annually according to Gartner.
The problem is that with the increased importance of data, those who do not put either do not put a high premium on quality or lag behind in the management of their data find that they are at a competitive disadvantage. Missed opportunities, and poor strategic decisions can mount, creating obstacles that are hard to overcome.
This means that the total benefit of actionable insights is never quite reached, and the integrity of the data is compromised in a way that undermines confidence in it.
In a sense it’s like playing darts with a blindfold. You may be pointed in the right direction, you may have the darts in hand, but the chances are any bullseye you may hit is going to be purely coincidental, rather than intentional.
So how is that avoidable?
Through strong data governance.
Data needs to be viewed as an asset, as well as a resource. It needs to be handled as such. Just as there are procedures in place that are utilized to manage an organizations other assets so too it needs to be with data. Processes need to implemented that allows for it to be properly and routinely checked and maintained to the standards that are necessary to ensure that it is not only of the highest possible quality, but that it is also useful. This includes a clear chain of command of people with the necessary skills to handle the data, implement best practices and turn it into actionable insights for best uses. It also means establishing clear responsibility for who is in charge of the care and quality the data that the organization collects.
What should be remembered is that a framework is being put into place that can be used to increase productivity as well as better inform decision making across departments. It’s cross-functional nature means that the effects of poor data quality can reach across different platforms, different divisions and have a profound effect on more than one might initially believe it would. That is what makes the situation as bad as it is. Whereas one might believe it can be contained in one area or one aspect of an organizations performance, it doesn’t. In a best case, Even if it is diluted as it moves, essentially isolated to one area, the truth is that it still will inform false narratives across different aspects of the organizations decision making process as nothing tends to live in a vacuum. In a worst-case scenario, it isn’t diluted and moves through, infecting the thinking of decision makers.
In the end it is about how you are going to be successful with your data, how it can help your decision making process and serve as a powerful tool at your disposal. That can’t happen with poor quality data.
The best advice that an organization can get is to ensure data quality is maintained to the highest of standards. Realize we have transcended the age of spreadsheets, and invest in the infrastructure necessary to get the job done right. Yes, it is an investment, but if you don’t make that investment early you are going to regret it later when your competition has the people, procedures and software in place to effectively utilize this asset and your organization lags behind.
Remember data isn’t just about who has it. Any organization can collect data about anything now. It is about who has the best data with clear vision of what it means and what it should do and who knows how to integrate and implement it to create value. If your organization isn’t doing that it is time to re-evaluate.