What Happens When Data Quality Goes Wrong And How To Fix It

From banking to pharmaceuticals and construction to every industry, corporate decisions are always driven by data. This determines a company’s long-term success. The quality of data a company can access determines their customer experiences, analytics, insights, retention, and revenue.

Despite understanding its value, 54% of all companies find data quality and completeness a challenge.

What Defines Data Quality.

Data cannot be qualified simply as good and bad. It can be assessed by many different dimensions. The first is how accurately the data reflects real-world values. Secondly, the data should contain all the necessary information and be presented in a form that can be easily understood. The data should be consistent throughout the organization’s systems. It should conform to standard formats and be present in a single unit, no records should be duplicated. Lastly, the data should be accessible as and when needed.

Effect of Poor Data Quality.

Data quality can impact a company in many different ways. An occasional error may not have very dramatic impacts but if all of your data is of poor quality, the consequences for your company could be significant.

Poor Decision Making.

The trouble with data is that it imparts a false sense of security. Companies base their decisions on the data available to them. Hence, a company’s decisions can be only as good as the data they are based on. Without high-quality data, you cannot make the right decisions for your company’s future.

For example, let’s look at a pharmaceutical company. They may have data that says there’s a spike in the number of people being diagnosed with a particular disease. This may make them invest in medication for that disease. However, if the data failed to mention that the people affected by the disease are in another country, the investment will be a failure. This can make the company suffer heavy losses in the long run.

Productivity.

Incomplete data can make day-to-day tasks quite difficult. For example, if a sales manager is working with bad data, he won’t be able to make simple forecasts but will have to first sift through the data and track down the numbers to validate the data first. This affects their efficiency and takes up time that could be spent on other tasks. According to one report, analysts can spend around 40% of their time validating data.

Incomplete data can also cause dilemmas in decision making. Sometimes, it may seem easier to simply make a quick edit and correct the data based on an assumption than to dig deep into its source. There’s a higher chance of this happening if the decision has a deadline associated with it. Unfortunately, this increases the risk of human error and can snowball into a larger problem later.

Thus poor data quality can affect your company’s day-to-day productivity and make employees less efficient. This, in turn, can increase operational costs.

Organizational Mistrust.

The frustration of having to vet and validate data before working with it can hurt employee morale. Why would employees who were hired for specific skill sets want to spend their day sifting through data? Bad data also lowers the chances of achieving the results needed. Thus tasks may need to be repeated for no fault of the employee’s. This can lead to dissatisfaction and frustration which in turn affects the employee’s faith in the company.

When data is inconsistent, it also makes teamwork harder. Different members of the team may base their decisions on different data. It will be hard for the team to evaluate which data is correct. Thus, it makes it difficult for a team to work in harmony towards a common objective.

Reputation.

The effects of bad data are not limited to within your organization. It can affect your clients as well. For service companies, billing errors, missed deliveries, misplaced orders, etc. can make their customers very frustrated. Today, when a customer is frustrated with the service being offered, he turns to the internet to vent. Most people look at reviews before making a decision to work with someone. Thus, negative customer reviews can be very costly for a company.

How to Fix This?

No business can risk working with bad data. To improve data quality, a company must be proactive. Some of the ways to do this are:

Invest in data quality software: An investment made towards improving data quality today can have benefits for years. In many cases, it is best to outsource data quality management to an expert. Any good data quality software can perform many tasks to audit the database. This includes deduplication, address validation, profiling, etc.

Improve data sources: It isn’t enough to fix existing errors and flaws, you should also be able to prevent poor quality data from entering your systems. For this, companies must review existing guidelines for data entry processes from time to time.

Encourage Collaboration: Improving data quality is not one person’s job alone. Encourage your team to get involved and contribute towards building good data. Collaboration between teams can make it easier to find a single source of truth.

Reward Excellence: To incentivize improving data quality, reward employees who make an impact. Even verbal recognition or small incentives can encourage others to follow suit.

Make data quality an ongoing priority: One of the aspects of evaluating data quality is its reflection of current values. It is no good to work with outdated data. Thus, maintaining high data quality needs to be an ongoing process. It should not be looked at as a one-time exercise.

The Bottom Line.

For a company to flourish, data is something it cannot compromise with. It has short-term and long-term implications for your company’s future. Investing in good quality data can help you make smarter decisions, increase productivity and act as a single source of truth.

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