The most important characteristic for any organization is not data, it is quality data. In a study, IBM estimated that bad data costs the U.S. economy $3.1 trillion per year. Such costs are incurred when your employees spend time in cleaning data or rectifying the errors caused by bad data. Apart from financial costs, bad data becomes a source of dissatisfaction and discontent between you and your customers, partners, and other business relationships.
This clearly explains the importance of embracing data quality in your organization. But what exactly is it? Let’s take a look.
What is data quality?
The definition of data quality depends on how it’s used at your company. From a business perspective, data quality enables enterprise intelligence by allowing you to perform successful statistical analysis, draw predictions, and make critical decisions.
Whatever purpose your data fulfills, you can always ensure its quality by meeting the following six rules:
HOW WELL DOES YOUR DATA DEPICT REALITY?
To ensure data quality, your data should always speak and reflect reality. When data is captured, mostly it is integrated across different systems and third-party applications. During this time, it is transformed and converted to different formats for standardization.
Many times, these operations change the real data and it no longer depicts the actual values. To avoid such situations, you need to implement data entry, transformation, and standardization rules to make sure that there are no incorrect records in your database, as they can lead to flawed decision making.
IS YOUR DATA AS COMPREHENSIVE AS YOU NEED IT TO BE?
Completeness relates to the presence of all necessary data attributes. Before capturing data, make sure you have identified the data required to fulfill your organizational business operations. Afterwards, make sure that the required data is being captured and entered into your systems appropriately.
Incomplete data mostly stems from insufficient analysis of your data requirements. Companies usually do not realize what data they need, and so they end up introducing required attributes later in their data lifecycle, which causes a lot of data records to be empty and incomplete.
DO DISPARATE DATA STORES HAVE THE SAME MATCHING DATA RECORDS?
Consistency means that the same data values are present for the same records at different data stores. Organizations mostly use multiple data management applications, for managing employee, customer, and financial data, among others. If these disparate data stores are portraying different data values, then across the entire organization, your teams will be using different data information to make decisions. Such differences between multiple versions of the same data can cause your own teams’ decisions to conflict with one another, leading to inconsistency at the operational level.
DOES YOUR DATA EXIST IN THE RIGHT FORMAT, DATA TYPE, AND WITHIN ACCEPTABLE RANGE?
It is not only important what data is captured – but how it is captured as well. Data validity is about storing data in the right type and format, as well as within the right range. This implies that data values follow the required validation pattern.
For example, if the attribute is an email, the data value should contain an ‘@’ symbol; if it’s a date, then it should follow a specified date format; and so on. It is important for data to conform to these standards so that it can be deemed valid. Invalid data – even if accurate or complete – cannot be processed, matched, or transformed.
IS YOUR DATA ACCEPTABLY UP TO DATE?
Timeliness deals with the time it takes for real-time data to be available for use and processing within your data applications. Is there any lag between when an event occurs and the time it takes for its data to be available for use? If your data integration framework is complex and time consuming, it could be that your current snapshots of data are weeks, or even months old, leading you to present and base critical decisions on outdated data.
Your data is only valuable if it is relevant. The older it is, the less relevant it becomes.
IS YOUR DATA FREE OF DUPLICATE RECORDS?
Along with the above-mentioned rules, data quality ensures data uniqueness, which means that there are no duplicate records present in your datasets. Duplicates can lead you to make wrong assumptions about your data.
To fix data duplication, unique identifiers are selected and the entire database is merged based on those identifiers. But it is not always that easy. Oftentimes, healthcare organizations remove personally identifiable information (PII) to guard patient confidentiality. In such cases, you may not have attributes in your data that can be used for exact matches. This is where you may need to perform some complex computing to create unique data definitions for comparing and matching records. For this purpose, common and proprietary algorithms are used such as fuzzy matching and soundex.
Organizations are increasingly becoming data-reliant, yet, critical decisions based on data often lead to incorrect or flawed assumptions. These six data quality dimensions are a great place to start but they should also be used to identify the root of the problem.
Why aren’t your data processes generating accurate, complete, consistent, valid, timely, and unique data? Start looking for such gaps in your processes and rectify your mistakes from the source.
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