In 2018, Experian's Global Data Management Benchmark Report indicated that 95% of C-level executives believe that data is an integral part of their business strategy. There's no doubt that data is a significant asset of every company. But is all data just as valuable? The short answer is no.

While it might seem like collecting data is half the battle, the real challenge is maintaining high standards of data quality throughout its entire lifecycle.

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Data Quality

In this post, we'll cover the definition and importance of data quality as well as which tools are available to help with data quality management and analysis.

How does quality data empower good business decisions?

Let's take a step back and review an example of how quality data can empower the best business decisions.

1. You have data, but it's not usable yet.

At this point, you just have values in a database or an Excel sheet. This raw data doesn't have practical use. For instance, you have thousands of email addresses from your customers and their topics of interest in a CSV.

2. You transform data into information.

You take that data to a tool where you can visualize it clearly in the right context. For example, an emailing list inside your marketing app. Now you can filter those email addresses according to their interests.

3. You obtain knowledge.

You analyze the information you've gathered and gain important insights from it. You might learn, for example, that 80% of your customers want to be contacted via email to get information about CRMs.

4. You make an informed decision.

With that knowledge, you can make a data-driven decision, such as deciding to create a newsletter with content about CRMs. When you have quality data, you have the necessary knowledge to make the right decisions for your business.

Characteristics of Data Quality

Since data comes in all shapes and sizes, it's not always easy to determine its quality. However, there are some characteristics typically attributed to high-quality data.

Looking for these characteristics in your own data will give you a notion of your data quality:

1. Accuracy

Is your data correct? And does it reflect the real-world situation you are looking at?

To guarantee accuracy and precision, you need to constantly optimize your data management strategy. Data accuracy is closely related to data integrity.

2. Completeness

Is your data comprehensive? Incomplete information might be unusable.

Though it's not advisable to collect more than the strictly necessary, make sure your must-have values are mandatory when storing new entries in your database. Otherwise, you'll end up with names without last names, or incomplete phone numbers you can't use.

3. Relevance

Is this the data you need? Let's face it, not all the data you collect is going to be a game-changer. But if there's a reason why you are collecting data and the values you obtained can serve that purpose, then you have quality data.

For example, if you ask your customers what their birth year is when they're signing up for a trial with your product, but their age is not actually useful information to you, it's data without a purpose. Therefore, even if it's correct, data is not effective.

4. Consistency

Does your data contradict other sources? High-quality data shouldn't contradict the data stored in other databases. Otherwise, you would have to assume one of them is wrong — but which one?

When there are inconsistencies between databases, it's a hassle to determine accuracy. Instead, ensure there's one source of truth when it comes to your data — whether that means getting everyone on the same data software or integrating your data tool with your CRM.

This way, everyone within your organization can access your data via a single tool, no matter where they are or when they need access.

5. Accessibility

Is the information accessible to the right people? Similar to the previous point we just covered, many companies interact with customers, prospects, partners, and employees via different applications.

As a result, data is scattered throughout different tools, and if there's no software integration in place, you have a data silos problem.

Data silos are among the main causes of poor data quality. Even with accurate, consistent, and relevant data, if the team who should be leveraging that information doesn't have access to it, it's not serving its purpose. To guarantee accessibility, integrate your business systems.

6. Timeliness

Is your data up-to-date? Data is constantly changing, and the problem with outdated data is that it may not be representative of the current situation. It's great to keep track of historical data, but with a clear sense of time.

Ensure you're keeping your data records but you'll also want real-time data and reports so you're aware of any changes as they're happening. This way you can either capitalize on those changes or work to mitigate any issues as needed.

Data Quality Analysis

Data quality analysis is how you ensure your data is... well, high quality.

In other words, it allows you to make sure your data is: accurate, relevant, up-to-date, and suited for its intended use and application.

Data quality analysis is often part of the process of data quality management.

Data Quality Management

Data quality management is the process of ensuring your team has access to high-quality data — it entails pulling insights about the health of your data in order to improve upon that health. This leads to the application of accurate data and the creation of larger data sets.

Data Quality Metrics

Data quality metrics are how you actually determine the quality of your data — they're the unique measurements that you put in place to analyze your data.

Data quality metrics are what actually determine the level of your data's accuracy, relevancy, application, etc. — as a result, you'll know how high (or low) quality your data is.

Data Quality Management Best Practices

Here are some data quality management best practices to keep in mind while analyzing the quality of your data.

  • Determine your team's most important metrics.
  • Get data quality buy-in across your business so everyone understands its importance.
  • Perform data quality audits regularly.
  • Invest in the necessary resources for data reporting, analysis, and quality training.
  • Dive into the reasons for any data quality failures or notable successes that your team experiences.
  • Use a data quality management tool (read more below on data quality tools — these will help with data quality management).
  • Ensure there's a single source of truth across your organization when it comes to your data (whether in your CRM, sales software, etc.).

Data Quality Tools

Here are some powerful data quality tools to help you accomplish everything we mentioned above and more.

1.Operations Hub

hubspot operations hub data quality analysis and management tool

Operations Hub lets you easily sync customer data and automate business processes. Your team will stay aligned with a clean, connected source of truth for customer data, and your business will be empowered to adapt to the ever-changing needs of your customers.

Operations Hub automates the process of data quality analysis — rather than programmable automation (a.k.a. choose-your-own-adventure), the data quality actions in HubSpot are pre-made and out-of-the-box.

HubSpot's Ops Hub includes three unique programmable automation features: 1) custom coded workflow actions, 2) custom coded bot actions, and 3) webhooks in workflows. Speaking of workflows, you can use them to automate and solve common data issues. For instance, you might set up a workflow that capitalizes the first name property whenever a new contact fills out a demo form.

Pro Tip: Use HubSpot Operations Hub to easily sync customer data and automate business processes so your team stays aligned on all customer data via a single platform. 

2. Insycle

Insycle data quality analysis toolInsycle — a HubSpot App Partner and integration — is a complete customer data management solution. It helps you manage, automate, and maintain your customer data. Insycle improves efficiency, reporting accuracy, and team alignment.

3. Dedupely

Dedupely data quality software

Dedupely finds and merges duplicate data automatically, saving you time and headaches and improving confidence and alignment across your company.

4. SAS

sas data quality analysisSAS is an enterprise software suite with products that manage, improve, integrate, and govern your data. One of its best-reviewed products is SAS Data Management — it's designed to manage data integration and cleansing. The tool also provides powerful ways to implement data governance.

SAS also offers SAS Data Quality as a solution to address data quality issues without the need to move your data.

5. Talend Open Studio

talend open studio data quality analysis softwareTalend Open Studio is part of an open-source suite ideal for mid-market businesses. The drag-and-drop builder makes it flexible and easy to use. Talend comes with several features meant for helping you solve integration problems. 

6. OpenRefine

openrefine data quality analysis toolOpenRefine (formerly Google Refine) is a free, open-source tool for businesses of all sizes — it's meant for managing and cleaning data.

OpenRefine focuses on transforming and reformatting disparate data to standardize it. This software allows you to add countless extensions and plugins so you can work with multiple data sources and formats.

7. Datawarehouse.io (Ultimate Data Export)

Screen Shot 2021-05-30 at 12.15.20 PMDatawarehouse.io (Ultimate Data Export) data quality tool for hubspot

The Datawarehouse.io, also known as Ultimate Data Export, is a data warehousing middleware solution for your HubSpot data — by syncing the software, your HubSpot platform and data will be integrated without the need for code.

Seamlessly export all of your HubSpot data (e.g. tickets, products, emails, and web analytics) to Excel as well as integrate your data with business intelligence tools, like Tableau.

8. Ataccama

Ataccama data quality tool

Ataccama is a data management and governance platform with tools for data quality, data management, data catalog, reference data management, data integration, and data profiling.

The tool's data analysis and management features provide insight into the quality of your data. They also help you validate your data, improve upon it, filter out any low-quality or incorrect data, and monitor quality over time.

Get a Comprehensive Overview of Data Quality

Guaranteeing data quality is not always easy, but the time and effort you put into it pay off in the long-term success of your business. It allows team leaders to make informed and data-driven decisions.

Not everyone can be a data expert, but there are some key concepts, techniques, and tools that make it possible for every professional to improve their data quality.

Editor's note: This post was originally published in October 2020 and has been updated for comprehensiveness.

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Originally published Jul 21, 2021 1:15:00 PM, updated July 21 2021