Data Quality — 5 metrics to measure data quality in your company
How to measure data quality in your company in an effective way? Discover 5 easy metrics of the quality of data.
Data quality management in business
In companies, data sets are stored in CRM, ERP, billing systems, data warehouses or on separate external drives and servers. As a result, there are usually many different types of data, often without a common, structured form, which makes it difficult to compare. A lot of data is also out of date and incomplete.
It’s no secret that having and using quality data is a key factor not only for the company’s success in the market, but also for its overall functioning. After all, data in the enterprise is used on a daily basis to create new products, perform market analyzes, reach new customers, and to make transactions. However, in order to make good, accurate decisions based on information, it’s necessary to take care of data quality on an ongoing basis.
That’s why more and more companies operating in B2B decide nowadays to improve data quality by for example automating the customer’s onboarding — company information of the contractor is then not filled by humans via a registration form, but company information API that auto refills all records directly from the official business register. What is more, some organizations cooperate with technology companies to perform regular data cleansing (for ex. we provide such data software services at Transparent Data).
But, before taking any concrete action, each company should be able to assess its data quality. It’s the first step to start professional data management.
Below, we present 5 popular data quality metrics, which help to measure what % of all data sets are to “fix”.
5 data quality metrics
Good quality data is, above all, reliable, up-to-date, relevant, accurate and interpretable. Developing your own system of measuring data quality isn’t an easy task. It’s definitely a task that requires a lot of time. There is no single data quality management system that would completely suit every company in any industry. Fortunately, companies can implement well-developed data quality metrics.
To be more precise, there are 5 data quality metrics that are globally used to calculate what percentage of stored data is poor-quality data:
- Duplicates: which data is duplicated and which not? Calculate what percentage of all data in your company datasets are duplicates.
- Up-to-date: is it possible that your data isn’t up-to-date, i.e. doesn’t present real-time information? Check how many percent of all records are this type of data. Think about what you can do to download real-time data. You can read more about real-time data HERE.
- Completeness: Your data may be considered incomplete if some records are empty (for ex. In a B2B database you miss current address or phone numbers. In this case, also calculate the percentage of data records that have no fields completed.
- Common formats: How consistent is your data regarding the formats in which you store it? Do you keep all your data in PDF documents? Or maybe CSV? Check that you aren’t storing data in the form of scans that cannot be parsed. Calculate what percentage of your company’s data is stored in these unfriendly formats.
- Accuracy: Does your data contain errors such as missing diacritics marks or misspelled street names, house numbers, etc.? HERE is a concrete case study that may help understand the problem. Check what percentage of data you store contains errors.
In what order to measure data quality?
When analyzing data quality, firstly you should take a look at key data — data that are responsible for approximately 80% of decisions made in the company. The results you will receive from using the above data quality metrics should help you estimate the size of the problem of poor data quality and facilitate taking appropriate corrective actions. An example operation after detecting duplicates can be simply deleting duplicate records from the database. The same may apply to incomplete data if it isn’t possible to complete the missing data.
In order to efficiently manage the quality of data on a regular basis, you would need to indicate concrete people or teams that are responsible for maintaining the right formats and accucary.
How poor data quality can affect your business?
Poor data quality can harm the company in a number of ways:
- Negative customer experiences — failure to deliver the order to the correct address, errors in invoices, incorrect personalization of newsletters — it all affects the trust of customers.
- High data maintenance costs — duplicated data simply takes up more space in the cloud or on servers, which means that the cost of data storage is higher.
- Loss of the opportunity to gain new customers due to typos in e-mail addresses or phone numbers.
- Decrease in work efficiency due to the need to verify data (validity of telephone numbers, addresses) — in this case, lower work efficiency goes hand in hand with wasting human resources (which generates additional costs) and delays in the implementation of tasks.
- Lowering the reliability of analytics — incorrect and outdated data may have negative effects, ranging from the loss of the client to whom you present an offer with an incorrect information, to the company’s bankruptcy as a result of investments in activities based on outdated data.
- Problems with compliance — the so-called dirty data significantly impedes all processes in the company, but in the case of compliance or due diligence in VAT, this impact may also have serious legal and financial consequences.
Data quality management: Why is it worth taking care of data quality in the company?
High-quality data (clean data) is the data that you can trust. This value does not need to be explained in particular — every manager or employee who has wondered whether a given analysis or report actually presents reliable data knows it perfectly well. Guessing what is true and what is not is not conducive to doing any business.
Unfortunately, it is commonly estimated that 25% of data is outdated every year. What is more, according to the Experian report from 2021, contaminated data can reduce company’s revenues by up to 20–30%. Improving data quality should therefore be a key principle for maintaining a company’s operational efficiency.
That’s why, begin with using those 5 data quality metrics. Every journey has to start somewhere.
