Data Quality: The Best Way to Regain Confidence in Your Data
But if companies want data to have a positive impact on revenue and business growth, then data quality processes need to be established. These processes will give more confidence to employees and decision-makers and empower them to lean on data when making business decisions.
- Improve data quality by cleaning it at the point of collection. This eliminates the need to clean data down the line.
- Quality data has seven main dimensions: accuracy, completeness, consistency, validity, uniqueness, integrity, and timeliness.
- The four processes to improve data quality are data profiling, data governance, data cleansing, and data standardization. These can be done manually but doing so opens the window to human error. A tool like Improvado automates and simplifies these processes.
- In addition to using quality dimensions for measuring data quality, add productivity and engagement metrics into the mix for a well-rounded data quality measurement process.
- Data quality helps make marketing and sales processes transparent and improves cross-functional collaboration.
What is Data Quality?
Data is the new oil. And much like oil which has no value when unrefined, data is worthless until it becomes something usable. Unfortunately, data is fragile and can be easily contaminated.
Data quality ensures this doesn't happen. It's the process that evaluates data, ensures it’s accurate and free from errors and shows the proper image of the insights that interest you and your business.
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What Defines Data Quality?
There are over 60 data quality dimensions. But, in practice, most data teams are concerned with seven.
This data quality dimension refers to the accuracy and correctness of data. The goal of accuracy is to produce error-free data that reflects what is actually happening.
This is generally considered the most important dimension of quality data.
When data includes all the information needed for its intended purpose, it is considered complete. Completeness can vary depending on the purpose of the collected data.
For example, let's say the goal of your collected data is to turn leads into sales. If the marketing team only collects names and emails—but the sales team needs phone numbers for demo calls—then the data you have is considered incomplete.
Data across different databases needs to be consistent to prevent data errors down the line.
If your email marketing software registers a change in a customer's email address, this change should also be reflected in the customer relationship management (CRM) software. Not doing so could result in problems with billing notifications.
Data validity refers to the consistency of data values as defined by the business.
For instance, a Europe-based business might format dates using the dd-mm-yyyy format (12 September 2022). But if someone adds an entry writing using the mm-dd-yyyy format (September 12, 2022), then this data is no longer valid.
Uniqueness means there's no data duplication or overlaps across any data sets.
Let’s say that a prospect signs up to a lead magnet as J. H. Watson. If they then write their name as John H. Watson when they buy your software, this should be entered as one person in your database.
This dimension refers to the preservation of data across its entire lifecycle as it moves through different systems and departments in your organization. It also means that there are processes in place to prevent data tampering.
Timeliness of data means the data is available whenever it is needed.
Yearly financial statements, for example, should be ready when accountants need them. If they’re not, it doesn’t meet the requirements of the data timeliness dimension.
Benefits of Quality Data
Quality data has a positive impact on an organization’s processes and its overall value as a business.
When quality data processes are in place—and this is communicated to decision-makers—the data gets used more and eventually becomes the foundation of business decisions and innovations.
It increases business profitability and revenue because decision-makers get insights quicker. And it also improves business performance as people are not wasting time correcting and reconciling data.
The Importance of Data Quality for Sales, Marketing, and Customer Management Teams
Data quality fosters alignment between different departments and their data while preventing any errors or inconsistencies.
This makes cross-department collaboration easier. There's transparency in all sales and marketing efforts, and everyone gets a macro and micro view of the customers and their journey throughout the lifecycle.
What's the worst that could happen when data processes are not in place?
We've heard of many businesses thrown into rough waters because of bad data.
For example, Samsung admits to losing $105 billion when an employee made a mistake because of poor data safety measures. Uber underpaid their drivers for many years because of an accounting mistake. The U.S. Postal Service spent about $1.5 billion on processing undeliverable emails.
And how about the many stories we hear of marketing and sales teams’ mishaps due to bad data? Some common ones include:
- Marketing team is sending emails that are incorrectly labeled—destroying brand trust, at the very least.
- The PPC team is targeting the wrong market segment—which ends up being very costly.
- The sales team is calling incorrect or non-existent phone numbers—affecting their efficiency.
- The customer service team has billed customers twice because of duplicate entries—resulting in irate customers.
All this makes it clear how bad data can put businesses in trouble.
That's why quality data has to be a priority for any business that uses data as a cornerstone for business decisions and activities. Quality data will give them a true picture of exactly what they’ve done, what could happen, and what they could do to increase revenue.
How to Measure Data Quality
At the moment, there isn’t an established standard for measuring data quality. Organizations must set their guidelines and establish baselines and expectations about data management and governance.
Generally, data quality dimensions are used as metrics. Each metric is assigned a weight and level of importance depending on the industry or the purpose of the dataset. For example, the financial industry places a higher value on validity while the pharmaceutical industry prioritizes accuracy.
Mikkel Dengsøe recommends going beyond measuring data quality and adding productivity and engagement metrics to the mix.
Productivity measures the efficiency of time spent on data management while engagement ensures that the data reports are available whenever the end user needs them.
How to Improve Data Quality
Firstly, everyone that works with data needs to take full responsibility for data quality. This includes the data creators (the ones who create the data) and the data users (the ones who use the data).
Data users should clearly communicate what type of data they need so that data creators can focus on providing data that meets those needs.
Once this is clear, you can proceed to improve data quality.
But where do you start?
To improve data quality, you should start at the root and allow only high-quality data to enter your database. This reduces, if not eliminates, the need for data quality checks down the line.
But this begs the question: What about the data you already have? How do you clean it?
Here are four data improvement processes to correct any quality issues with current data.
Data profiling is the first step to improving data quality. It's the process of reviewing and examining data to troubleshoot any errors, missing information, or redundancies.
When done manually, the process can be time-consuming and cost-intensive—not to mention prone to human error. However, data integration tools can speed up and improve the accuracy of the process.
People in the organization must be assigned specific roles whenever handling company data.
This is the role of data governance—the process of organizing and managing data so that rules are clear on who can access it, what actions they can take, and what methods they can use. This minimizes human error while giving enough access for people to do their jobs.
Data that no longer serves the business's goals needs to be removed through data cleansing—or it will contaminate your data. This process removes redundant, inaccurate, and incomplete data.
Data can come from many different sources. For example, for marketing and sales teams, data might come from your email software, Google Analytics, CRM tool, and ad platforms like Facebook and Google Ads.
Through data standardization, you can align all the data collected from these different places and prevent data disparity. This makes inter-department collaboration and insight-sharing smoother and faster.
An easy way to standardize data is to use automation tools like Improvado which extracts data from 300+ marketing and sales sources.
We live in a data-driven world. Businesses with quality data and know what to do with it enjoy many benefits. They’re the ones that can scale faster and leave all their competitors behind.
If you still don't have data quality management in place—now is the best time to invest in your data quality.