Regardless of the role, anyone that deals with data of any kind probably faced poor quality data at some point. It doesn’t matter if it’s data reporting, preparing, cleaning, comparing, or analyzing, if it’s bad quality data, it will impact the work that needs to be done and all the decisions the company has to make.
Of course, different roles will have different impacts inside the organization, but there’s no denying: poor quality data has a price that not everyone can afford to pay, and will require a robust data compare in the process to ensure that everything is in place.
Poor quality data costs an average of around $14.2 million dollars annually for companies, according to a survey conducted by Gartner to discover the average price of bad data. Meanwhile, an IBM research found that US companies alone lose around $3.1 trillion annually due to bad quality data.
But there are ways of avoiding paying the price of poor data quality, and we’ll explore them in this article.
Strategic Ways to Avoid Paying the Price of Poor Data Quality
There are a few solutions that can be taken to improve the quality of the data you already have and avoid poor data quality that can directly impact productivity and finance.
Create a Team to Exclusively Own the Data
Being serious about data and understanding how important it is to always keep the quality high is having an exclusive team to “govern” and own the data quality processes.
It doesn’t matter the size of this team – no need to allocate massive resources to it – but the people that occupy these roles will need to understand their impact on the organization and know all the processes very clearly.
The data quality team needs to have goals and clear objectives with measured metrics for data quality to ensure maximum performance and high-quality data across the entire company. This team needs to keep everything running smoothly for all the teams that need the data.
Keep the Focus on the Most Important Data
If everything is important at the same time, nothing will end up being prioritized and quality can be compromised. The team set to govern the data quality process will have to focus on the data that you or your organization as a whole need first.
The relevant business issues will need to be treated first – especially when there are various projects running at the same time, and they all need data to happen – but the ones that can lead to bigger issues and can present a real risk should be the priority.
If the company has robust data quality processes and routines in place, the chances of poor quality data presenting a real risk to the teams and the people that need it is very low, but prioritizing is the key to avoiding disruptions and problems.
Automate as Many Data Processes as You Can
As mentioned above, the size of the team that will own the data quality processes doesn’t matter, they should be enough to fit all the needs of the company. But this team, and the other teams that deal with data, should have one thing in place: fully automated processes.
The volume of data handled daily can be massive, and keeping the quality can be hard when the processes are manual. There are data quality tools available for all companies sizes and needs, so you will definitely find one that will work for your data and budget.
With automation tools, the data quality team will take care of the processes to keep it working and improving. Avoiding poor data quality requires time, investment and continuous improvement.
The Impacts of Poor Quality Data
Poor quality data has a high price, which can be a dealbreaker for many companies. But dealing with bad data can also mean productivity impacts inside your organization.
To make important decisions, companies need to analyze accurate data. But if the data quality is not being taken care of, the data analysts will actually spend most of their time validating the data.
This research by Forrester shows: “Nearly one-third of analysts spend more than 40% of their time vetting and validating their analytics data before it can be used for strategic decision-making.”