20 % Loss of sales due to poor data quality

We recently shared two articles around the importance of data in any organization: 7 Questions to Estimate Your Company's Data Maturity and the article Sound Data Governance leads to more sales and fewer costs | Mentor and Men.

Now we cut to Data Quality. Data Quality is the cornerstone of data management and the key for any company looking to position itself as a leader.

As consultants in digital business transformation, we put Data at the top of the agenda in every implementation project. Experience tells us that while the majority of companies keep a lot of data, they pay far too little attention to its quality. And for many organizations, that leads to huge revenue losses that organizations are often not even aware of.

What is quality data?

In general, data is considered high quality when it is

appropriate for their intended use, for decision-making and planning
an accurate representation of the reality to which they refer
internally consistent, especially with many different data sources

Opinions about quality may already differ. If that is the case, then it is good to have Data Governance in place (see Put Data Governance High on Strategic Agenda). Therein lies the guidance on how to find agreement on definitions and standards for data quality. Possibly this leads to cleaning up, deleting or relabeling data.

Quality properties of data

There are eight criteria that determine the quality of data.

Completeness: Is all the necessary information present?
Validity: Is your data prevalent and valid within your company's business, within your industry (e.g., food sector)? What does the law (e.g. GDPR) say about that data and does it comply with it?
Availability: Is my data 100% ready and "always on" when I want to use it?
Uniqueness: Are there (multiple) duplicates in my data?
Durability: Does data stay the same for a long time, or is it volatile?
Consistency: Is the data structure clear and is it always respected?
Integrity: Is the data structure always respected?
Accuracy: Does the data match reality today?

How do defects in your data quality arise?

As consultants in digital business transformation, we make data quality a top priority in every project. Experience teaches us that this is where the shoe pinches quite often, for the following reasons:

Incorrect manual input of data, whether intentional or not, e.g. wrong names, addresses or abbreviations (*)
Poor automated processes that are insufficiently vigilant for bad data
Migration of data to new systems
When merging or splitting data files: consider mergers or holdings that consolidate or just separate subsidiaries, or family changes (newly formed?)
Lack of data verification systems although they often seem obvious, e.g. for verification of company number or structure of phone number
Lack of reference files, think correlation zip code-municipality, street registers or API Swift/BIC code

Data is a moving target

Data changes more than you think.

The numbers show that you are never done with Data Quality. Data Quality is a never ending story because data is constantly changing.

Every organization must be well aware of this.

Impact of substandard data quality

Poor data is pernicious for any organization. Especially for companies that rely heavily on data, and today this is true for more and more organizations. Think about how much time and money goes into correcting mistakes caused by bad data: wrong invoices, packages delivered to wrong addresses, product recalls due to wrong information on the packaging. Dwell for a moment on delivering a new mattress in the heart of Brussels with a mobile elevator on the third floor ... and the driver turns out to have the wrong delivery address on file. Annoyance, frustration, huge loss of time and money by driver, customer service and customer. Or again, who gets happy about a mailing on which his r or her name have been misspelled?

Are these misses due to bad data recognizable?

Customers receive wrong documents or no documents at all

Frustration, confusion, lost time and additional costs in all departments of the company
Dispatching deliveries to incorrect addresses
Irritation at the end customer
Customer drops out and goes to the competition

The importance of the quality of data that organizations maintain cannot be overstated. It's not about quantity. It's about quality. This article aims to make professionals aware of this importance.

This is where things go thoroughly wrong in a lot of companies. In a subsequent article, we will share our expertise on how to get started to stop poor data quality. How can you become an industry leader thanks to strong, high-quality data?

Do you recognize this misery with data quality? Contact us. We can help. Be sure to read