The importance of data quality management

Gunnar Steinn Magnusson18 Jan 23 • 8 min read

Blog > Manage

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they do collect is quality data – there’s little use in having vast pools of data if it’s inaccurate, incomplete, or out-of-date.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.


What is data quality management?

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).

The six traits of data quality

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?
  • Accurate: data is correct and up-to-date

  • Complete: no fields are missing

  • Consistent: the data is the same across different systems
  • Timely: it can be easily accessed
  • Valid: the data is in the right format
  • Uniqueness: there are no duplicate entries

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.

The problem of… bad data

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.

The problem of… missing data

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.

The problem of… duplicate data

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:

  • Multiple people entering the same data
    (e.g., two employees recording that they worked on project X)

  • Multiple systems recording the same data
    (e.g., an employee records their work schedule in both Salesforce and Google Calendar)

  • Multiple systems recording the same data with different values
    (e.g., an employee enters their birth year as 1990 on LinkedIn, but records it as 1991 on Facebook)

How to improve data quality

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:
  • Assess data – conduct a formal data assessment

  • Define data quality – decide what’s acceptable and what’s not
  • Correct errors at the source – don’t let bad data infiltrate other systems
  • Omit siloes – ensure there’s a central data repository
  • Provide access – make data available to a vast range of potential users

  • Implement common values – offer a defined list of options for data sets for users to choose from
  • Secure data – ensure stringent data access measures
  • Promote a data culture – get everyone involved and up-to-speed to encourage data quality success
  • Perform regular reviews – monitor what’s working well and see where improvements need to be made
  • Employ a data steward – this person will implement measures and training to facilitate improved data quality measures

Use a data quality management tool

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:
  • Data profiling and cleansing software. This type of tool helps identify problems in your source data by checking for inconsistencies, missing values, corruption, or bad formatting – you can use these to automate processes such as data cleansing or re-formatting.
  • MDM (Master Data Management) software. These tools manage the structure and content of critical business information across an organization’s enterprise.


    For example

    they can be used to define product catalogs that ensure products are consistently defined across all applications; track company hierarchies such as employee roles within a department; or create a centralized repository for customer records so that each department has access to accurate information about customers without having to rely on spreadsheets shared via email attachments sent back and forth between departments

Do you need a data quality management solution?

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:

  • Does your company suffer from inaccurate or missing information?
  • Is there a lack of consistency in the way you collect, store and use data?
  • Are there gaps in terms of what you can do with certain types of information at different stages in its lifecycle (for example, if you have a lot of customer information but no idea who they are)?

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.

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Gunnar Steinn MagnussonChief Executive Officer, exMon Software ehf.

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