Know: How Data Quality Builds Data Integrity

In the world that we are living in today, data is everything. From the smallest enterprises/businesses to the largest ones, data has something for everyone.

When it comes to data, two parameters become important: quality and integrity. There are many ways in which data quality directly influences data integrity by ensuring data relevance, data completeness, data consistency, data uniqueness, etc.

An article by Forbes reported that nearly 10% of businesses, especially the small ones, believe that high-quality data analysis helped them forge better relationships with both their customers and business partners.

Stay glued to our post to learn more about the relation between data quality and data integrity.

What is Data Integrity?

To put it simply, data integrity is concerned with the following three factors: accuracy, consistency, and the degree of completeness. Additionally, data integrity is also involved with ensuring aspects like data security and safety.

Data integrity is important, especially in the wake of the rising instances of data theft. Data integrity ultimately boils down to how reliable your data is. And a record number of businesses are taking the matter of data integrity very seriously.

A survey found that a whopping 92% of companies are wary of the serious damage that inaccurate and false data can inflict on their standing and image in the market. This statistic also exemplifies why the interrelation between data quality and data integrity cannot be ignored.

How Data Quality Helps in Building Data Integrity

In this portion, we have rounded up the various ways in which data quality can directly influence, better, and improve the integrity of your data. To see what they are, dive right into our list!

1. The All-Bases-Covered Dimension

As already mentioned before, the degree of data completeness is a crucial aspect of data integrity. Let’s suppose that you’re the owner of a supermarket.

One of your many routine tasks would involve maintaining and regularly updating a record or inventory of the various products in your supermarket. When it comes to inventories, there are multiple fields of data involved, including the product’s name, brand, the number of units sold, and so on.

If any of these fields are incomplete, they can prove to be time-consuming in the long run. When you adopt the right data quality practices, you will find that data ‘completeness’ is achieved automatically, thereby leading to enhanced data integrity.

This aspect becomes especially important if your business or company is involved in selling ethical data to other firms. of those buying data from others say that the overall completeness or accuracy of the data sold matters greatly.

Therefore, by bringing an all-bases-covered approach to your data, data quality helps pave the way to improved data integrity.

2. All Signs Point to Relevance

It goes without saying that irrelevant data is as good as a bat’s eyesight during the daytime. To ensure the relevancy of data, the equation between data quality and data integrity plays a key role. Here’s an example for a better perspective.

You are planning to run a marketing campaign specifically targeted at children and the youth population. In this context, the demographics related to the 35-45 age group will be of little use to you.

Data quality deals with data relevance. So, when you work towards improving the quality of your data, relevance is an aspect that is guaranteed to be covered. Relevant data can also help in reducing cases of data redundancy.

There’s practically no limit to what you can achieve with relevant data – data optimization, stronger customer and client relationships, a more streamlined and specific approach to work, and so on.

3. Consistency Wears the Crown

It should come as little surprise. Research by Gartner reported an alarming finding: Poor or average data quality could cost businesses up to $9.7 million annually. Often, you will find that data inconsistency is the villain.

Data inconsistency arises when the data maintained across multiple databases fall out of sync. Let’s assume that you keep your company or firm’s data across four different databases. If you change the data in one database, you need to follow it up by making corresponding changes across the other three databases. If you fail to do so, it will lead to data inconsistency.

By following standard data quality practices like data consolidation, normalization of data, and automation of repetitive tasks, you can improve your data’s consistency by a significant margin.

Another way of ensuring data consistency is by making it a point to update your databases regularly. You can do this by outsourcing the task to businesses that provide data cleansing services.

Thus, you can see how data quality and data integrity are essentially inseparable.

4. Timing is Everything

How often you update your data will go a long way in determining its accuracy, consistency, and relevance. So, it is imperative that you perform your data entry and updating processes regularly.

With today’s fast-paced, constantly changing business dynamics, even updating data daily is not sufficient. At times, you might need to update your data by the minute. When you adopt top-notch data quality practices, you will be able to integrate data on a timely basis.

According to David Loshin, it is in this context that the concept of data currency becomes important. Loshin believes that currency is the yardstick to evaluate the extent of your data updating practices.

Timely updates become possible when data quality and data integrity join forces, thereby giving the ‘currency’ a massive boost.

5. Outstanding Data Needs to Stand Out

One of the hallmarks of quality data is its ability to provide new, cutting-edge information to businesses and firms. This aspect also ties in with the trustworthiness of data. To achieve this, you need to avoid certain things, like data duplication and repetition.

To prevent data duplication, you can conduct regular data cleansing drives to identify the duplicates and then remove them from the database.

There’s more to duplicate data than what meets the eye. Get this: In the US, the incidence of duplicate data is as high as 20-30%, which in turn costs businesses an astounding $611 billion each year.

By ensuring data quality, you can level up the data’s integrity by several notches while also presenting unique, value-packed data ready for use.

Summing Up

A 2017 Brazilian Case Study in the Pharmaceutical Industry found that many pharmacies that compromised data integrity received the FDA’s ‘Warning Letters,’ reprimanding them for the “lack of sufficient corrective actions.”

Data integrity is absolutely essential, and our post walked you through the various ways in which data quality helps build data integrity.

Invest in data quality and data integrity, and let the results speak for themselves.