Billions upon billions of bytes of data are produced every single day. In our data-driven world, this continual stream of information is a form of insight that proves vital in modern business strategy. Yet, only some data a business interacts with is useful. Some data may need to be more relevant, complete, or accurate, making it fairly useless for a company.
Each year, businesses collect more information. In an attempt to gain more insight than other companies and forge a competitive advantage, enterprises are rapidly expanding their potential to collect, organize, and draw information from data sets. However, with more data available, companies face difficulties with a growing number of data sets withneed better data quality.
According to Gartner, low data quality can cost organizations upwards of $12.9 million each year. In this article, we’ll explore this figure in more detail, commenting on what data quality is and why it has such a grave impact on modern business operations.
Let’s dive right in.
What Is Data Quality?
Data quality is a general term that outlines how apt a data set is to perform a certain function. Within this definition, data quality also refers to the completeness and accuracy of the data itself. That said, even if your data is perfectly correct and fully complete if it doesn’t serve the purpose you’re looking for, then it is irrelevant and will have a low data quality.
Businesses need to reconfigure their data architecture around performance benchmarking, create strict data management policies, and ensure that everyone in their organization understands effective data policies in order to boost data quality.
When measuring data quality, businesses tend to look at the following qualities:
- Accuracy – If there are discrepancies between versions of datasets, it is likely that one of them is inaccurate.
- Completeness – Completeness refers to the overall percentage of data that is within a dataset. Missing values or gaps in the data will reduce the level of completeness.
- Timeliness – Data may be perfectly correct, but if it is from several years ago and outdated, then it will not offer your company much insight.
- Utility – Data that serves no purpose for your business has a low level of data quality for your company.
While data quality is typically an innate factor of the data you deal with, that doesn’t mean that companies are unable to improve data quality. On the contrary, there are several ways that businesses can improve data quality over time.
How Can My Business Improve Data Quality?
Businesses that only work with high-quality data are able to access better decision-making, enhance their customer-facing experiences, and boost their revenue. Across the board, better data leads to better operations, helping companies to thrive.
Yet, forging an organization that has a continual stream of high-quality data is far from a task that can be completed overnight. Businesses need to reconfigure their data architecture, create strict data management policies, and ensure that everyone in their organization understands effective data policies in order to boost data quality.
While these changes won’t happen overnight, there are a number of strategies that any business can implement to ensure the process occurs as quickly and smoothly as possible. Let’s explore each one in turn.
Pinpoint Your Data Leaders
When a business decides to enhance its data quality, the change typically comes from the top down. A CEO or CIO will have begun to see just how important data quality is, which will inspire them to create change in their organization. Beyond just desiring a change, these business leaders should appoint heads of data at every level.
A great data strategy starts with people who are in charge of ensuring that data quality is maintained throughout the entire data pipeline. For this to occur, business leaders should assign data owners, stewards, and teams of IT experts to oversee the process. These stewards will help enforce any data policies you create and will streamline any data-facing changes you make.
Automate Data Capture And Processing, Where Possible
The past decade has seen a number of automation pathways that help to improve the speed, accuracy, and efficiency of the average data pipeline. In 2024, businesses can turn to a whole host of tools and platforms that help to automate data quality strategy. By implementing automation in data collection and data processing, you can reduce the chance of errors occurring and eliminate any human error that could occur.
Equally, automating your data pipeline as much as possible will help you to align your business more closely with all required governance regulations that you must abide by. Across the board, automation helps to boost data quality, adhere to regulations, and increase the efficiency of data processing. Where possible, you should focus on automation in your data pipeline.
Due to some modern data protection laws, you won’t be able to automate absolutely every part of your data capture process, especially if you work in an industry such as healthcare or finance. However, for the vast majority of companies, automation will become a key strategy in your movement toward improving your data quality.
Modernize Data Infrastructure
One area of data quality that is infrequently discussed is consistency. When you hold your data in several different repositories, each one should reference the same information. If the same dataset displayed different figures in each department’s data repository, you would be working in the dark and would be unable to deduce which figures were accurate.
To reduce the chance of your business running into problems with consistency, we strongly recommend that you move toward a more modern style of data management. An effective way of doing this is to begin working with a centralized cloud data warehouse, which will provide access to all your employees and help to democratize data.
You can also pair data warehouses with specialized analytics databases to entice even more value from the data you store. For example, when comparing Bigquery vs Snowflake, two hybrid time series databases, there are numerous additional features you can implement.
By modernizing your data infrastructure, you’ll be able to engage with your data more fully, helping draw more from your high-quality information.
Final Thoughts
No matter how much data you source, if its quality isn’t of the highest quality, you’ll end up with inaccurate insights that could lead your company in the wrong direction. From creative decisions to operational improvements, high-quality data can help streamline every aspect of your business and help you move toward your goals.
By focusing on continually improving the average quality of your company data, you can create better insights across all areas of your company. Using the strategies we’ve outlined on this list will allow you to hit the ground running and push for the highest possible data quality in 2024.
Best of luck!
