Data plays a significant role in redefining business operations. It plays an integral role in making major strategic changes in the organization to formulating strategies that can impact consumer behavior. Data is the DNA for all the major changes taking place in the organization. However, not every piece of information that is available to an organization is in the best interest. Only good quality data will serve the intended purpose. Here comes the role of the data quality framework.
Digging deeper into the data quality framework and its key aspects
As we have mentioned above, the data available within the system may have some flaws or errors; here, the data quality processes are deployed to filter the data and the authentic and useful data. The data quality processes continuously profile the data for errors and implement the different data quality tools to prevent errors from penetrating the system and impacting the overall operations.
It is also called a data quality lifecycle which is desired in a loop wherein the data is persistently monitored to catch faults and errors. Different data quality processes are leveraged to prioritize sequence and minimize the error before it enters the system and impacts its functioning. Quality data leads to:
- Enhanced productivity
- Better decision making
- Gaining a competitive advantage
- Enhancing the customer relations
- Easier data implementation
Why is high-quality data important?
Quality data is a pressing issue for most organizations. Despite having all the relevant data, the company cannot formulate the right strategies, and this is because of the quality of the data. Here comes the role of data quality tools and data quality management framework that helps the data science professionals filter out the data which is relevant to the organizational requirement.
One of the common concerns when it comes to quality data is duplication of data. Data scientists use data duplication software and data matching software which helps them remove the repeated data and filter out the quality data.
Key parameters to measure the data quality
This table highlights the different parameters that help the organization measure the data quality:
To calculate this, one has to count the number of empty fields within a given data set.`
|How to calculate
|The ratio of data to errors
|It means how many errors are there in the size of the data set available
Total number of errors/total number of items
|It shows the information missing from the data set
|Data transformation error rate
|It shows the errors that come when information is converted into a different format
|It is calculated by the number of times the data fails to convert successfully
|Unused data because of the faulty quality of data
|How much data has quality issues
|Email bounce rate
|Number of times the email bounces back because of the wrong address
|To calculate this:
The email bounced/ Total number of emails sent*100
|Data storage cost
|Cost to store the data
|Fees charged by the data storage provider
|Time is taken to get value from its information
|Define what value means to your firm, and then check how long it takes to achieve this pre-decided value
Stages of a Data Quality Framework
Now that you know about the different parameters that help you assess the quality of the data, it is important to get into the technical aspects of how does data quality framework works. Several data quality tools are available, and they work in different stages. The following section takes you to the four-staged data quality framework:
- Assessment- This stage involves assessing the quality of data that is in the organization’s interest. It also defines the parameters to which it can be measured. This step involves the following:
- Choosing the incoming data structure like marketing tools, CRMs, etc.
- Deciding the attributes important to complete the information like phone number, address, name, etc.
- Now define the data type, pattern, size, and format. For example, you should define that the phone number should contain 11 digits and follows this pattern (XXX)-XXX-XXXX.
- Deciding the data quality metrics
- Design- A data quality pipeline is designed using the data quality processes and architecture at this stage. The key work included in this are:
- Choosing the data quality process to clean the data and protect the data quality.
- Cleansing of the data to eliminate null values and transform the useful value into an acceptable format.
- Data governance rule to capture and implement role-based access.
- One must decide when this process will be executed, i.e., when the data is fed into the system or before data enters the database.
- Execution– you have designed the data quality pipeline. It is then executed on the existing and incoming data to process it.
- Monitor- Now, you can monitor and profile the data for its quality and also measure the quality metrics.
Once you have figured out the right tools and designed the process to filter the quality data, you have to finalize the time to trigger the cycle. Some organizations would want to complete a proactive approach wherein the data analysis report is generated weekly. After this, the following stages are executed:
- Updating the data quality definition
- Introducing the data quality metrics
- Redesigning the data quality pipeline
- Execution of data quality processes
Wrapping it up !!!
This was the basic information about the data quality framework and its implementation. Although there are many layers to implementing a quality framework, it is important to follow the basic steps to ensure that there is no data duplication. Quality data helps an organization formulate the right strategy that can help them gain a competitive edge in the market.
With the growing competition and complexities of consumer demand, organizations need to harp upon the information available and derive useful insights. With the use of data duplication tools and data standardization tools, it becomes easier for them to find the right information that is in the organization’s best interest.
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