The importance of data quality management in the modern enterprise

Estimates suggest that at least 2.5 quintillion bytes of data is produced every single day, and as new and innovative technologies are continually invented, that number is only expected to increase.

This presents a goldmine of opportunities for businesses but only if a) they have the right tools at their disposal, and b) they know how to use that data. First, businesses need to ensure that the data they do collect is quality data – there’s little use in having vast pools of data if it’s inaccurate, incomplete, or out-of-date.

This is why data quality management has become one of the most important aspects of any modern enterprise. The quality of data directly impacts how well an organization can run and therefore, how successful it will be in the long term.

We’ll take a deep dive into data quality management, why it matters, and what steps businesses can take to ensure data quality company-wide, and ultimately, encourage organizational success.

Data quality management is the set of processes, systems, and techniques that a business puts in place to ensure all data is accurate, complete, and always up-to-date. Data quality management sits under the data governance umbrella – these are the internal security standards and processes that a business implements to ensure data is gathered and stored properly and governs how that data is accessed and by who (you can find out more about data governance, with our article "Data Governance 101" here).

In essence, data quality management is all about maintaining high-quality data. If a business can’t inherently trust its data, it can have a detrimental impact on overall operations and performance. Data informs actionable insights for every business decision, big or small, if that data is incorrect, wrong decisions are made.

Data quality issues can happen at every stage of a company’s lifecycle and for various reasons, such as incomplete data, unstructured data, different data formats, duplicate data, or difficulty accessing data in the first place. Other problems such as a lack of resources and time, the right tools, and untrained staff can also significantly contribute to poor data quality. While some of these issues might be addressed at a single point in time (e.g., through regular audits) others may require continuous improvement over several years (e.g., implementing new technology).

Data quality refers to the degree of satisfaction of data consumers with respect to the quality of that information in a specific context – in other words, the goal is to ensure that data is fit for its intended use.

So, what steps should be taken to facilitate a successful data governance strategy?

If this isn’t the case for your business, don’t worry, there is a way to improve the quality of your data by implementing a robust data quality management strategy. This means managing both internal and external sources of information so it's always accurate, complete, and current.

Data quality is the fitness of data for its intended use. It's the degree to which data conforms to the expectations of its users. Bad data is any data that fails to meet these expectations, whether it's a simple typo or something more complex like missing or inaccurate values, duplicates, and inconsistencies in field formats or definitions.

Missing data is the bane of all data quality efforts – it’s like a virus that can disrupt your entire organization, and it’s everywhere.

Missing data can be caused by many things, such as hardware failure, human error, and even natural disasters. When you have missing values in your dataset, it means you don’t have enough information to make an accurate conclusion or decision. Perhaps some of your customers didn't provide their email addresses when they signed up for the newsletter, or, maybe there was a bug in the software code that crashed when someone entered ‘golf clubs’ into their search field.

Maybe half of your employees didn't respond to last year's anonymous employee survey because they were afraid of reprisal from management if they gave honest feedback (ouch). Alongside all these potential causes for missing data – and many others – consider how much money companies lose each year due to fraud.

The bottom line is that missing data impacts every aspect of an enterprise's operations, including customer service levels and profitability metrics. Supply chain cycles can be severely impacted – inventory levels become increasingly uncertain due to inaccurate shipping schedules or delivery times being missed because data wasn't updated correctly before orders were processed by the warehouse, which then had no way of knowing what goods would arrive next week versus next month.

Duplicate data can wreak havoc – it results in wasted time and effort and prevents a singular view of data.

Here are some common examples of how duplicates occur:

The benefits of improving data quality include improved business efficiencies, faster innovation cycles, better products and services, enhanced customer experience, and a more effective research and development process, to name a few.

However, it’s essential to note that data quality management is an ongoing process, not a one-time project or fix. It's an iterative and continuous practice that needs to be instilled into your enterprise's software development cycle. In fact, data quality management should be part of the data governance process by default.

There are several steps a business can take to improve data quality, including:

One simple way to improve data quality is by implementing a data quality management tool, to help you measure and improve the quality of your data.

There are two main types of tools available:

A high-quality data management plan ensures that your organization's data is accurate, relevant, and available when it's needed, but how do you know if you need one?

To understand how this impacts the bottom line of your company, here are some questions to consider:

Chances are if you’ve answered ‘yes’ to any of the above, you likely need the support of a data quality management tool. At exMon, we know that data quality management is an integral part of any company’s business and should be treated as such. It’s vital that you understand the problems that can occur from poor data quality, as well as how to fix them before they happen again.

Learn about the exMon data platform that offers everything you need to improve the quality of your data to facilitate better-informed decision-making.