The modern business world has organizations collecting customer data through several channels and maintaining contact records across various databases. But simply having access to this data is not enough as it is also essential to proactively the validity and reliability of data before using it for operations. The best business decisions are backed by up-to-date data of the highest quality, making it necessary to be self-conscious about data. And having a data quality testing strategy in place is one of the best ways to achieve that.
What Does Data Quality Mean?
Access to high-quality data is significant in many ways, one of them being that the data is always ready to be used for its intended purposes. These include improved analysis and reporting or an innovative marketing strategy. Most business decisions today are very data-driven and the effectiveness of these decisions is only as good as the data that drives them. And this iterates the importance of data quality as it can enrich, profile, standardize, and validate the data to unlock its full potential.
Importance of A Data Quality Testing Strategy
To achieve consistent and reliable customer data, businesses need to constantly manage their data quality, the lack of which can lead to missing the chance to build customer loyalty and trust. It also negatively impacts other vital aspects such as understanding customer purchase preferences and effectively staying in touch with them or the market. Improved data quality also assists in processes such as the applications of robotic process automation in finance and as well as understanding the relevance of issues such as data integration in data mining.
According to an InsideView Alignment Report of 2020, more than 70% of revenue leaders highly prioritized data management. Despite that, a study by the Harvard Business Review shows completely contrasting numbers. It estimates that only 3% of companies’ data quality meets the basic standards desired. The fact of the matter is that a major gap exists between the needs of companies concerning data quality and what they do to achieve it.
Key Steps To Data Quality Testing Strategy
Data is considered to be of high quality when it satisfies the requirements of its intended use for clients, decision-makers, downstream applications, and processes. Data quality is an important metric that drives the value of data and, in turn, aspects of the business outcome such as compliance with regulations, customer satisfaction, or accuracy of decision making. The list below outlines 5 main criteria for measuring data quality:
- Accuracy: Accuracy of the data being input into an operation is very important thus the data must be correct for whatever information you’re storing it for.
- Relevancy: The data that is collected for the purpose needs to be relevant and useful for the specific purpose it is needed for.
- Completeness: This data also needs to be complete and error-free. The result of the division should not have any missing values or incorrect records.
- Timeliness: It is also important and goes without saying that the data has to be relevant and not outdated.
- Consistency: It is also essential to keep the data and associated reports in a way that makes them easy to cross-reference with comparable ensures.
There are various steps to employing a proper data quality testing strategy. Listed below are a few key ones that follow the five main criteria for measuring data quality:
Defining Specific Data Quality Metrics
It is important to define and specify the metrics against which data needs to be tested to understand the targets to achieve and the areas of improvement. One way to do this is to understand or analyze the various ways an organization uses data and the problems that higher-quality can solve. The important metrics of data quality keep varying based on the focus area for different businesses and so they cannot be defined within one set for different organizations with different purposes.
Conducting Tests to Find a Baseline
Driving data quality improvement throughout an organization is not possible without defining a baseline and identifying gaps in the data that need to be improved. A good example of this can be specific tools that allow logistics companies easily validate delivery addresses before outbound packages leave their facilities. Thus, making sure that the data quality is upgraded through tests that determine the baseline of their logistical needs.
After organizations have determined what business areas they need to improve their data to meet goals, they need to start addressing specific data quality issues. Businesses need to follow up on these issues by trying out various solutions available that can help in solving the issues and improving data quality. This is necessary to improve the overall practicality of the data and optimize it for their usage.
Assessing results against the data quality solutions that have been tried is an essential step for businesses. This is because running such tests against initial metrics enables understanding positive improvements that were observed or identifying areas where further refinements are needed. And the results from these processes determine how the data quality solutions need to be adjusted.
Data quality can mean something different from one organization to the next. But as long as you are defining criteria that make sense for your business and testing against those, you can be sure you’ll be able to find ways to drive improvement.
Data validation is also important for security reasons. Here are a few data validation techniques that are key to improving the quality of your data
Why Choose Intone Data Integrator?
Data quality can mean different for different organizations, but as long they adhere to a data quality testing strategy that can define the criteria that make sense for their business, there is always a possibility for improvement.
The growth in demand for data and modern business models make access to quality data an absolute necessity. Intone Data Integrator is a top-of-the-line data integration solution that has been tried and tested by industry leaders and experts alike. We offer,
- Generates knowledge graph for all data integrations done
- 600+ Data, and Application and device connectors
- A graphical no-code low-code platform.
- Distributed In-memory operations that give 10X speed in data operations.
- Attribute level lineage capturing at every data integration map
- Data encryption at every stage
- Centralized password and connection management
- Real-time, streaming & batch processing of data
- Supports unlimited heterogeneous data source combinations
- Eye-catching monitoring module that gives real-time updates