Thursday, April 18, 2024
No menu items!
HomeData IntegrationWhat are the business risks of poor data quality?

What are the business risks of poor data quality?

“You can have all of the fancy tools, but if [your] data quality is not good, you’re nowhere.”— Veda Bawo, Director of data governance, Raymond James

High-quality data is an essential resource for your organization. It provides accurate business insights, informs your strategic decision making, and aids regulatory compliance.

Poor data quality, on the other hand, can have detrimental effects.

6 risks of low-quality data

Data quality indicates how fit for purpose your data is. Think of it as the overall health of the data, and how easy it is to work with.

The top risks that poor data quality poses are:

Reduced efficiency
Missed leads
Lost revenue
Reputational damage
Inaccurate analyses
Lack of compliance

Let’s break these down in more detail.

1. Reduced efficiency

Many of your internal business processes rely on a steady stream of reliable data. If the data is incomplete or just flat-out wrong, then your teams will have to waste time manually correcting quality issues.

This doesn’t just make you inefficient, it can severely impact the speed at which your company makes decisions and hinders data transparency across departments. This is especially detrimental if your business has a data silo mentality.

2. Missed opportunities

Without high quality data to base your decisions on, your business will miss important opportunities. For example, poor data may mean you miss out on:

Market trends
Customer insights
Product improvements

Inaccurate data also prevents lead generation by making it harder to target your prospects. This leads to a lackluster sales pipeline. Overall, you’ll end up with fewer customers.

3. Lost revenue

Poor data quality is responsible for an average of $15 million per year in losses. And, that’ll only get worse as data environments become increasingly more complex.

Lost revenue can be a direct or indirect consequence of poor quality data. Regardless though, it’s bad for your business. You might have:

Inaccurate client information, which could lead to losing them as customers.
Incorrect personal information like addresses, meaning products ship to the wrong people.
Inaccurate product information, which may result in claims or complaints that cost the business to fix.

4. Reputational damage

Often a consequence that comes hand in hand with lost revenue, reputational damage is detrimental to business growth. Customers who have bad experiences or notice inaccuracies are sure to tell the world about them.

Poor data quality can lead to a damaged reputation in a number of ways, such as:

Incorrect customer billing information
Missing or inaccurate product specifications
Sending duplicate marketing emails to the same address by accident
Poor sensitive data management

Your customers will lose trust in you if you manage their data poorly. And, it could land you in some hot water if you’re not careful. (More on that later.)

5. Inaccurate analyses

If you’re using inaccurate or incomplete data to conduct your analysis, you’ll lead yourself down a dark path.

Businesses use past data to identify patterns and create forecasts. However, these insights are only as accurate as the data that shapes them. Missing fields, duplications and inconsistencies will cause you to waste your resources on analyses that you can’t trust.

6. Lack of compliance

Data compliance laws affect everyone, no matter where you work.

These data standards oblige organizations to protect any personal data they collect and ensure that the data owners can access, change or delete their data as they wish.

Breaching these data laws can result in hefty fines, sometimes upwards of 4 percent of annual global turnover.

How can you fix poor data quality issues? 3 tips for cleaner data

Improving your data quality can have a positive effect on your business and keep you from falling victim to any of these consequences.

Try these three tips for cleaning up your data:

1. Establish your data rules

Messy data is usually the result of a lack of standardized procedures and guidelines. Implementing universal data rules ensures everyone’s treating data with the same respect.

Some steps you can take include:

Using standardized naming conventions and consistent formats for dates, times and addresses etc. This prevents systems from misreading your entries and keeps duplication to a minimum.
Treating fields individually. This helps you identify the critical fields for data completeness and apply appropriate rules to ensure they’re filled out.
Identifying data ownership. This not only establishes who’s responsible for the data but can help you track changes and audit effectively.
6 data quality metrics you can’t afford to ignore

2. Regularly audit and clean

On the topic of auditing, make sure you regularly check the status of your data. Conducting a data audit sounds scary, but really it’s just a health check to make sure problems don’t go undetected. Data audits usually involve three simple steps:

Checking in with stakeholders
Understanding the location of all of your data
Evaluating data quality for any issues

A simple check is sometimes all you need to protect yourself from nasty consequences

3. Choose the right tool

To enhance your data quality, you need to use the right tools.

With a tool like CloverDX, you can discover and fix bad data fast, using a wide range of features including:

Bad data identification & correction
Rule definitions
Reports on data quality

Keep your data squeaky clean

Ninety-five percent of businesses have seen impacts related to poor data quality.

To operate at your best, you need high quality data. It helps you make informed decisions, increases efficiency and maximizes your profits. Champion data quality at your business and see the benefits it brings for yourself.

Why actionable error reports are so important to your data architecture
Read MoreCloverDX Blog on Data Integration



Please enter your comment!
Please enter your name here

Most Popular

Recent Comments