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 to maintain high standards of data quality throughout its entire lifecycle.
To make it more difficult, around 50% of companies don't seem to agree on who is responsible for managing data. The task is usually spread out across operations teams, decision-makers, and professionals of different departments managing data on a daily basis.
If you were chosen to manage data within your team, you need to know how to measure and ensure the quality of your data, as well as the tools available to help you out in this task.
What Is Data Quality?
Data quality measures to what extent data serves its intended purpose. In most cases, the purpose of collecting data is to have information to make decisions. So, when we talk about high-quality data, we are referring to the type of data that results in quality information and eventually leads to data-driven decisions.
Let's take a step back. How does quality data empower good business decisions? Here's the chronology:
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 a practical use. For instance, you have thousands of email addresses from your customers and their topics of interest in a CSV.
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.
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.
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.
In a scenario where your data has poor quality, you'll have the wrong information, you'll lack knowledge, and therefore, you'll make poor business decisions. But how can you tell if you are working with quality data or not?
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:
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. Overall, the best way to minimize mistakes in your data is by avoiding manual data entry.
Is your data comprehensive? Incomplete information might be unusable. Though it's not advisable to collect more than the strictly necessary, make sure that the must-have values are mandatory to store new entries in your database. Otherwise, you'll end up with names without last names, or incomplete phone numbers you can't use.
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 their birth year to start a trial, but their age is not useful information for you, it's just data without a purpose. Therefore, even if it's correct, data is not effective
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. Luckily, there are integration solutions that allow you to choose which piece of software "wins" in case of a conflict.
Is the information accessible to the right people? Most companies interact with customers, prospects, partners, and employees through 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, you must have your systems integrated.
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. For real-time reporting, you need to make sure your data is constantly being updated.
How to Ensure High-Quality Data
Securing data quality is not a one-time thing. It's part of a continuous process in which people, technology, and strategy have to be aligned.
As your business grows, the challenges around data management will become more complex. That's why a solid foundation, focused on preventing future issues, is the key to ensure data quality.
Here are some concepts you'll need to keep in mind from the first moment you implement a data management strategy:
Data Governance refers to the set of company policies and rules that set the standards when it comes to data management. These policies should be known and applied by everyone managing data. This will be the starting point to obtain high-quality data.
Data Profiling has to do with the people in charge. Data management rarely relies on a single team. Though we tend to attribute the technical aspects of it to the IT team, data is obtained and managed across the entire company. That's why, ideally, there has to be people responsible for data quality in every area of a business.
Data Maintenance should be a continuous effort that includes periodic data cleansing processes, prevention, detection, and repairing of data. Data maintenance is the way to safeguard its integrity.
Data Integration connects the different systems you are using. This is the way to make sure your data stays up-to-date and accessible. Additionally, if you've chosen data synchronization as a way to integrate, your data will also be consistent between apps, and matching entries between databases will be enriched.
Let's say, for instance, that you have the same person's contact information in two different apps: in the first app you have the name and email address, and in the second one you have the name and phone number. Two-way data synchronization will allow you to have all these values available on both apps.
If you are working with a complex data structure where the quality of your data is vital to run your business operations, there are other disciplines and concepts you might want to learn about, such as data matching, master data management, and data quality reporting.
Data Quality Tools
Are you having trouble ensuring any of the characteristics of data quality we talked about? Don't worry, there's an app for everything. Actually, there are so many apps out there that sometimes the most difficult part is choosing the right one. A good place to start your research is by visiting review sites like G2.
To give you an idea of the reach and diversity of the tools available to improve your data quality, here's a handpicked selection that addresses a variety of businesses sizes and operational needs:
SAS is a software suite with different products to manage, improve, integrate, and govern data. One of its best-reviewed products is SAS Data Management, designed to manage data integration and cleansing. It also provides powerful ways to implement data governance. Additionally, SAS also offers SAS Data Quality as a solution to address data quality issues without the need to move your data. The main audience for SAS software is enterprises.
Talend Open Studio is part of an open-source suite. Next to many features to solve integration problems, it's a flexible and easy-to-use tool. Mid-market businesses save a lot of time with Talend's drag-and-drop options.
OpenRefine (formerly Google Refine) is a free open source tool for managing and cleaning data. It 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 lots of data sources and formats. It's used by businesses of all sizes.
PieSync offers an integration solution specifically for customer data. Having isolated contact data is one of the most common threats to data quality. Since PieSync works two ways and in real-time, it helps data managers ensure customer data consistency, completeness, accuracy, and accessibility with a very easy setup.
Guaranteeing data quality is not always easy, but the time and effort you put into it pays 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.
Originally published Nov 6, 2020 12:41:30 PM, updated November 06 2020