Articles | March 7, 2024

What is Data Quality? Data Management Best Practices & More

What is data quality? Read the article and learn why it’s so important, the benefits of good quality data, and some tips and tricks to help you get started on the path to quality data immediately!

data quality

With 2.5 quintillion bytes worth of data generated each day, companies that optimize and leverage data-led insights stay ahead of the competitive curve. While most of us already use systems that store and structure data, the quality of that data simply isn’t good enough, with many companies reporting lost revenue, slow response times, and poor decision-making as the consequences of messy master data sets.

But, the good news is that data accuracy can easily be improved by implementing various best practice processes, systems, and governance steps. In this article, we look at all things data quality, including why it’s so important, the benefits of good quality data, and some tips and tricks to help you get started on the path to high-quality data immediately.

Let’s get started!

What is Data Quality and Data Quality Management?

Data quality refers to how well a dataset meets criteria for areas such as accuracy, completeness, and validity. As part of a broader data governance strategy, if your data quality is high, it gives your organization the confidence to rely on the data to measure performance, analyze productivity, and make business decisions.

To keep different data sets in good shape, many organizations deploy various data quality management tactics to maintain data standards. While we’ll look at these in more detail later on, data quality management uses a mix of people, processes, and governance steps to monitor data values and continually optimize data entry techniques for greater and greater quality.

Why is Data Quality and Data Integrity Important?

As AI-led big data systems, underpinned by data lakes and data warehouses, become increasingly popular for modern businesses, the saying ‘rubbish in, rubbish out’ is on the minds of many CTOs and CIOs. Poor quality data drives poor quality outcomes, so effective data quality management is essential to maintain data integrity, stay ahead of the game, and enable your business strategy. 

Fail to maintain good data quality standards, and you could find yourself:

  • Repeatedly making poor business decisions, driven by inaccurate enterprise data analysis
  • Missing opportunities to drive additional revenue 
  • Unable to respond to changes in your industry ahead of your competitors
  • Open to fines and data breaches, especially if you regularly handle customer data or medical patient data 

To bring this to life even further, in 2021, consulting firm Gartner found that bad data quality costs organizations an average of $12.9 million per year. That’s a lot of money to lose all because of inaccurate data and poor data quality control across your organization!

Also read:  Data mining methods 

The 4 Most Common Data Quality Issues

It’s easy for us to sit here and recommend you improve your data quality, but no one purposefully sets out to have poor-quality data – unfortunately, high-quality, consistent data is simply hard to achieve.

But, there are some common reasons businesses struggle with data quality problems. Let’s take a look at the most common data consistency issues.

  • Businesses use a range of different systems that each hold data in different formats and structures, creating inconsistent data standards.
  • Businesses take data from multiple data sources, each using its own data types, data pipelines, data formats, and business rules.
  • Businesses run many projects to move between different systems. Regular data migration can erode quality effort, making maintaining a trusted data set hard.
  • The legislation of particular countries or regions regularly changes, meaning data records constantly need updating, which opens the door to errors and inconsistency.
  • Staff simply aren’t educated on maintaining high data quality, leading to the quality standard eroding over time.

Even though many businesses use data analytics to drive business decisions, to maintain data quality, you need to overcome the common challenges that affect every aspect of data. But if you make a fresh start and manage to get new data in good shape, here are some of the benefits you can uncover.

The Benefits of Good Data Quality

Now that we know the consequences of bad data quality, it’s time to look on the bright side. Improving the quality of data stores across your business will allow you to level up your business operations by:

  • Improving productivity through automated business rules
  • Enhancing the service you can provide to your customers, driving increased revenue opportunities
  • Reducing costs thanks to faster decision-making and less duplication
  • Creating consistency across processes and systems
  • Easily demonstrating compliance thanks to embedded quality assurance frameworks 
  • And many more!

Data quality improvement will benefit every corner of your business, so it pays to invest time and effort into a data quality framework that you can implement across the business. To help you begin to address data problems in your business, let’s look at some data management best practices. 

The 6 Data Quality Dimensions – Best Practices to Improve Data Quality

Before embarking on your own data quality improvement process, it’s worth stepping back to look at the fundamentals of a good data governance program.

This begins with the six data quality dimensions. Each views data quality through a different lens, helping you consider all angles to ensure your data is fit for purpose. Let’s take a look at each in turn. 

  • Completeness: Completeness measures the amount of data records that are complete and ready to be used. Partially complete data may lead to unreliable results that are not fully representative of the analysis you’re trying to complete.
  • Uniqueness: Data sets have a habit of duplicating themselves, so uniqueness measures the percentage of duplicate data in your sets. To avoid misrepresentation, tactics such as data cleansing and data quality rules help ensure that records are unique and individual within your sets.
  • Validity:When it comes to using the data in real time, the validity of data measures how much of the data meets the required format for a particular business rule. Often, this refers to data being in the right format, pattern, or range to make it applicable to a given situation.
  • Timeliness: In an ever-digital world, data must be available at the click of a button. Real-time data processing is a demand of many businesses, so when managing data, you have to ensure it can be generated, received, and manipulated at exactly the time it’s needed.
  • Accuracy: In a world where many systems use the same sets of data, accuracy refers to how reliable the data is versus the agreed ‘source of truth’.
  • Consistency: Building on accuracy, while one system often leads the way to ensure reliable data, other systems will conduct data quality checks to measure data quality against the master source. This is what consistency is all about, ensuring that, as well as using other data quality metrics to ensure data quality at an enterprise level using rules, audits, and data quality assessments.
  • Fitness for purpose: Finally, effective data is only effective if it helps enable the business to do its job. While the accuracy of the data could be 100% if no one needs it, it’s a waste of time, effort, and money.

The dimensions of data quality are the underpinning foundations of great data governance. They should be applied no matter the size and shape of your data sets and no matter where the data is located. 

data quality

Here’s How to Get Started with Data Governance – 6 Data Quality Tools, Techniques, and Processes

Now that we know data quality is important for business performance and the dimensions to build trust in data, it’s time to build out your own data quality assessment framework. This will help you continuously root out inaccurate or inconsistent data and make positive strides to fix it for the benefit of your entire organization.

Let’s take a look at the 6-step process.

#1 – Hire Dedicated Data Stewards

If you want to get serious about data quality, it all starts with getting the right people on board who are dedicated to improving the state of data in your organization. Many businesses start by hiring a Data Steward who’s ultimately responsible for leading the governance approach to data quality and improving the accuracy of data in your organization.

While the best results come from hiring dedicated stewards and data managers, it could also be a side-of-desk activity for data scientists, architects, or information security professionals. This will help you get off to a good start if you don’t have the budget to take on a new headcount. 

Either way, once the roles are assigned, invest in adequate training and awareness of data quality to ensure they have all the knowledge they need to start promoting data quality best practices.

#2 – Agree on a Data Profiling Strategy

With the right people in place, it’s time to start building your data profiling strategy. Data profiling is the process of examining, analyzing, and creating a summary of the data you hold. Put simply, this is the process you’ll go through to assess the quality of your data. 

Within your strategy, you’ll want to define things such as: 

  • How often you’ll analyze data based in different locations
  • The techniques/lenses you’ll use to identify data
  • How you’ll measure the data accuracy
  • The level of data quality that’s acceptable for your business
  • How you’ll report data quality issues to other departments, such as IT, Risk, and Compliance

Once you have a clear view of your strategy, it’s time to put your data quality measures into action.

#3 – Go Hunting for Data Quality Problems

Now, it’s time for action as you hunt for data quality problems within your organization. You’ll need to plan exactly how you’ll do this, arranging things like data access requests, access to systems and tools, and time with relevant stakeholders to understand how data is used in a particular area. 

It’s best to have a clear data governance process to accompany this. This will help you keep track of what’s happening when and ensure you have oversight and support if you encounter any problems.

Once you’ve completed your assessment, you’ll likely generate an outcome report to share with stakeholders. This will clearly show the level of data quality in that particular area, including an objective pass/fail assessment and recommendations for improvement. 

#4 – Build an Expert Data-Fixing Taskforce

With a list of recommendations, it’s time to get the right people together to fix the problems. Depending on the size and scope of your data quality team, this may be done yourself or passed onto the business area that owns the data to take action themselves. 

The latter aligns with a master data management approach, whereby business and IT professionals work together to ensure data quality remains high across all applications and data sets. Given that poor data quality can result in various negative consequences, it makes sense for everyone to pull together to ensure the data is fit to serve the business operations. 

#5 – Audit the Process with Quality Assurance Activities

Like all good compliance activities, you need to add a third-party review to ensure your data governance framework is adhered to. Whether you team up with a formal auditing team or set up your own local quality assurance, you have to keep your data stewards, business owners, and application teams accountable for data quality best practices. 

Many organizations track a schedule of data quality reviews, ensuring each is completed on time, to a good standard, and that any follow-on actions are completed to a high standard. This helps keep bad data at bay, ensuring that every component of the overall data quality governance model is effective. 

#6 – Track It All With a Data Quality Management Tool

Where many businesses start and fail with data quality is by trying to do it all manually. Especially when dealing with large data sets, a data quality solution or tool can help automate a lot of manual work, increasing the speed and accuracy of your analysis activities. 

Popular tools such as OpenRefine, Talend, and Cloudingo are perfect for executing your data profiling strategy, helping you identify, analyze, clean, and re-format data to boost quality and provide confidence to business leaders when making decisions. 

While these tools require investment and setup, they’ll help take your data quality capabilities to the next level as you standardize and assure your organization’s entire enterprise data set. 

Businesses succeed and fail based on their data quality

In a world where data is at the heart of everything we do, if your data quality is low, it can leave you at a real disadvantage. If you want to avoid risk, poor decision-making, and slow performance, you need to invest in your ability to maintain high levels of data quality. 

The tips in this guide are a great place to start, but for the best results across all of your data needs, we suggest partnering with a technology expert. At Inetum, our Smart Data offering helps organizations worldwide define, implement, and achieve their data strategies, including initial visioning, change management, and long-term data governance. 

If you’d like to join the hundreds of customers who benefit from our expertise, knowledge, and partnership, reach out today to understand how we can help level up your enterprise data quality. 

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