Data means information which can be in the form of number facts, symbols, description of employees’ names, addresses, costs, and many more.
Data has become an important part of business processes. It can be collected through different surveys, online tracking, social media monitoring, transactional data tracking, online marketing analytics and collecting registration data. Organizations use data to analyze customer feedback, make informed decisions, identify promising business opportunities, and achieve improved customer retention and experience. If an organization has higher Data Quality, then they can make better choices in marketing, sales, and customer service.
Data Quality – How Do You Measure It?
Data Quality (DQ) is one of the important categories of the data management process. DQ is extremely important in the organization because it provides them with the ability to make better and more customer-oriented decisions. Data quality can be maintained by ensuring that its collection is performed accurately and systematically, making its analysis an easier and refined process.
But the question that comes next is – what determines the quality of data?
As we have briefly mentioned in the overview titled “Getting to know Data Quality Tools”, the measurement of DQ depends on certain factors. Some of those are –
Believability means that data in a dataset is accurate and many users believe this factor. There are other attributes that can be associated with the process of creating data sets such as, efficiency, effectiveness, duration, complexity, robustness, the need for capacity and costs.
It means that a piece of information does not contradict another piece of information in a different source or system. If any set of data contradict itself then the data is not trustworthy. There can be problems for the organization where it can cost money and reputational damage. For example, if the student’s birth date in the school registry is dated May 7, 2001, but in his/her birth certificate it dated as of November 10, 2002, then the piece of information is not contradicting itself with the other information.
Accuracy means whether the information is correct whereas, inaccurate data can cause critical issues. For example, if there is an error in a customer’s bank account because an unknown user accessed without his/her knowledge.
Completeness determines how comprehensive the information is and on the other hand, an incomplete piece of information is a waste of time. For example, when sending an official mail to the client it is necessary to check if the name or address is correct otherwise it will go to waste.
Timeliness means that the piece of information is up to date. If the information is collected in the past hour, then it is timely. If that information is not up to date or timely then it can lead to wrong decision making, cost of time and money to the organization.
Consistency understands whether data constantly appears in the same format. Consistency has two major benefits, first is that the data is compatible with previous data, and the other is that the data is familiar.
Accessibility means that the easy access of the data to the data consumers. The data needs to be up to date and can be easily retrievable to the customers.
Value addition –
This factor means that the overall value addition of the data is particularly important for the consumers and organizations. Value addition addresses some of the qualitative issues of DQ. The value in data gives you a competitive edge and data adds value to your operations.
Relevance is an important factor of data quality characteristics; it means that if the information is needed or not. There must be a reason behind the collected piece of information. If you collect irrelevant information, then it will be a total waste of money and time.
Data Quality Tools
It is nearly impossible for a person or team to handle large amounts of data in the organization without missing out on any details. To resolve this, data quality tools are used by organizations to improve their data resources. Three of these tools have been described below –
IBM InfoSphere Information Server for Data Quality:
IBM offers this data quality tool to deliver automated data monitoring and personalized cleansing in batches. This solution searches for the DQ flaws and forms a remediation plan according to the user’s designated business achievements. With this tool, companies can make their own DQ rules. The tool has main features such as data profiling, cleansing, matching, validation, data integration, DQ assessment, data classification, and so on.
Informatica Data Quality:
To achieve data quality management, Informatica Data Quality uses metadata and machine learning technology. It automates data quality management for machine learning and artificial intelligence. The important features of Informatica data quality. The important features of Informatica Data Quality are data profiling, data integration, DQ transformation that includes standardization, matching, enrichment, validation, and data integration. It also manages exceptions and automation of DQ critical tasks with the CLAIRE engine which uses machine learning and other AI techniques.
Trillium DQ is a tool that is a flexible and scalable data quality platform for different use cases, and it monitors and manages DQ. It provides self-service capabilities for data stewards, business analysis, and batch data quality which can measure real-time and big data as well. This platform supports several initiatives, such as data governance, migration, master data management, single customer view, eCommerce, fraud detection, and many more. It has main features such as data profiling, data linking, pre-built reports and scorecards, and so on.
The Flawless Data Experience Achieved
Organizations view data as an important part of digital transformation activities. If data is important then the quality of data is also important for the organization because it leads to great decision making in the favor of the organization. As in a domino effect, ensuring your data is usable dictates how your future processes that rely on data will pan out.
Data quality tools offer automation, support, and protection of data with their unique capabilities. This reduces the challenges faced by organizations and gives them access to industrial data. With enhanced visibility, higher quality and increased ease of access, processes that are dependent on data become more effective.