The Impact of dirty data on your business

The overwhelming abundance of dirty data and how it can cost you

The cost implications of the non-stop pile-up of dirty data are far-reaching and, at times, unpredictable. Based on recent estimates, dirty data has cost U.S companies somewhere between $2.5 to $3 trillion every year. But that’s just an understatement.

When you consider the unnecessary storage costs for redundant data and the countless marketing campaigns that depend on inaccurate lead records, the picture gets a lot worse.

Let’s take a quick look at the impact dirty data can have on your business.

What is dirty data and where does it come from?

Any form of data that is inaccurate, inconsistent, incomplete, or contains errors is known as dirty data. About 60% of the dirty data prevalent on a global scale is attributed to human error which is what makes it such a difficult issue to resolve easily.

For instance, no matter how good of a data strategy you have, inaccurate information can always creep into your servers and data records in the form of duplicates with incorrectly spelled wrong dates and addresses. Sometimes, dates, account numbers, and other personal information is displayed in varying formats which makes it even harder to reconcile easily.

About 57% of the businesses heavily rely on consumer reports to be informed of data errors, which in itself is a less than ideal way to address or solve the issue.

A lack of proper interdepartmental communication and conflicting reports and records kept by different departments lead to even more data errors caused by these internal contradictions.

The overall impact of dirty data

A significant amount of resources are wasted on incomplete and outdated consumer data (up to 27% of the revenue in the US). Combine that with the loss in productivity and all the internal and external miscommunication and you’ll find that the cost that comes with storing dirty data is more than what most companies can afford.

Most data scientists and knowledge workers spend a lot of their work hours cleaning up and organizing databases. It is no wonder it causes a huge dip in productivity. Sifting through records manually is a tiring and time-consuming process and one that isn’t going to do the business any favors. It only tends to add to the already existing problem when manual workers themselves inevitably make mistakes of their own.

More than 60% of organizations are said to rely on marketing and prospect data, 40% of which is inaccurate. As a result, various business processes suffer. This includes lead generation, marketing and most importantly, customer relationships, all of which have an above 50% probable rate of being affected

How does dirty data cost your business?

1. Revenue losses in failed communication

Let’s say you run an organization. You obviously depend on your loyal consumer base to buy your products or services in order for you to generate substantial revenue. But what happens when the contact details of many of your clients are inaccurate? What happens when there’s no way for you to get in touch with many of your lead prospects?

You lose customers and, as a result, your annual revenue takes a hit.

The longer it takes for you to clean up your database, the more this problem costs you down the line.

According to Experian Quality data, 12% of a company’s revenue is wasted on inaccurate records. Surprisingly enough, this figure has stayed the same since 2007 which indicates a gross oversight on the part of many of the world’s standard business companies. It also points out how far-reaching the consequences of dirty data can be.

2. Futile marketing efforts

Every day, social media marketing teams are coming up with different ways to connect with an audience and encourage limited offer purchases. The activities involved in expanding the organization’s outreach take various forms, the most important of them all; email marketing, targeted messages, social media posts, paid promotions, and so much more.

When a significant portion of the consumer data that your business so heavily relies on turns out to be wrong and inconsistent, the combined efforts and time your team of employees put into those strategies becomes futile. Paying your employees for the hard work they’ve put in to achieve a projected revenue target that was barely attained can put your business at risk in the long run.

3. Poorly informed decision making

Business executives rely on a variety of analytical data in the form of statistics and metrics to aid them in making important decisions for the future of the company. These decisions are meant to help improve the organization’s competency and efficiency and propel it to the forefront of the business world. However, in order for this large-scale, data-driven decision making to be effective, the information used needs to be clean and accurate. Otherwise, the outcomes of these ill-advised strategies can dangerously affect the position of the business and leave it struggling to keep up with its competitors.

4. Poor customer relationships

Clients can become very frustrated and impatient with an organization that they frequently contact if they can’t keep track of their customer details. This can cause a strain on the client-business relationship. If a client has one too many of such bad experiences from your organization, they might lose confidence in your business and eventually withdraw their contract or unsubscribe from your email list. They might even refrain from doing business with you in the future. The less satisfied your clients are, and the more customers you lose, the more adversely this can affect your business.

How to deal with dirty data?

First of all, you need to stay up to date on the best practices in the industry when it comes to cleaning up data. The priority is to focus on clearing inaccurate entries and duplicate records while at the same time maintaining that the metadata (information that points to the location of these records and the corrections made) is integrated with the original data files.

Once you do end up clearing up all the inaccurate information from your database, you now need to focus on maintaining the valid records while regularly fact-checking them to see if they are consistent.

You also need to make sure that you keep track of all the changes and corrections that have been made to the data. In other words, you must preserve the lineage of the data so that it is more reliable.