As businesses are expanding, the data is becoming a vital part of every business operation. The quality of data that is collected and consumed during the business processes will determine the success and growth of the businesses in today’s as well as in the coming years ahead.
Data Quality in a simple sense is the measure of the condition of data based on various factors like accuracy, completeness, reliability ,consistency and whether the data is updated.
Measuring data quality levels can help businesses and organizations identify data errors and resolve it as soon as possible so that the efficiency of the organization is not affected.
Improved data quality leads to better decision- making reducing the risks and improved results.
Data quality management is an important component of the data management programs that aims to ensure data is formatted and used consistently.
Importance of Data Quality
Data is a trending topic in the business world these days. Businesses and organizations want to know about the valuable insights that is derived through the data. But data is useful only if it is of high quality. Bad data can cause businesses to pay a huge cost. For instance, the bad data costs the US economy $3.1 trillion every year which clearly indicates the indicates the importance of good data quality for any organization.
What is good data quality?
In determining good data quality, data accuracy is a key attribute. To avoid problems in operational systems and faulty results in analytical applications, the data used must be correct. Incorrect data needs to be identified and fixed to ensure that data analysts and end users are working with correct information.
Other dimensions of good data quality include data completeness, with data sets containing all the data elements, data consistency, where there are no conflicts between the data values in different systems.
What Factors Contribute to Data Quality
- Accuracy
Accuracy can be explained as to how well the data presents the real- world conditions it aims to describe. Incorrect data can lead to problems as it can make incorrect calculations.
Inaccurate data hinders the growth of businesses.
- Relevancy
The data which is gathered should be useful for the businesses goals. If the data collected is not relevant to the company’s goal it is of no use.
- Validity
Data is valid, only if it is in the correct format and complies with the requirement of the company.
- Completeness
Incomplete data leads to false insights and is of limited use as no proper information can be drawn from it. Complete data means there’s no gap in it and valuable information can be gathered which will prove to be beneficial for the businesses growth.
How to Determine Data Quality?
As a step forward, determining the data quality levels, organizations perform data asset inventories in which the relative accuracy and validity of data are measured. The other common way is to create a set of data quality rules and regulations based on business requirements. Such rules specify required quality levels in data sets which can be checked for accuracy and other data quality attributes. After the rules are in place, a data quality assessment is is conducted to measure the quality of data sets and identify the errors.
An example of such a system can be seen in the International Monetary Fund (IMF) which has specified assessment methodology known as the Data Quality Assessment Framework which focuses on the consistency, reliability, completeness, accuracy and other data quality attributes in the statistical data.
Data Quality Management tools and techniques
Data quality projects involve several steps which starts with outlining and measuring the impact that bad data has on any business operations. Next, defining the data quality rules, performance targets for improving data quality metrics are set. The data quality improvement processes are designed.
The processes include data cleansing to fix data errors, and enhance the data sets by adding missing values and updated data information.
Software tools specialized for data quality management can match records, validate new data, delete or duplicate files and offer remediation policies and identify personal data in data sets.
Software tools can also do data profiling to collect information about data sets. Collaboration and workflow enablement tools have also become common in providing shared views of corporate data repositories to data quality managers.
Benefits of Good Data Quality
- Informed Decision Making
Good data quality leads to better decision making for businesses. Good data reduces the risk and informed decision making results in the improvements of businesses and organizations.
- Easier Implementation of Data
When a company owns high quality data, the efficiency automatically increases. If the data is incomplete then a considerate amount of time goes in fixing the data to make it usable which takes longer time from the other important tasks.
- Increased Profitability
The better the data quality, the more is the profit for businesses. It can help the organization achieve more effective marketing campaigns and also making it more cost – effective.
- Improved Relationships with customers
For success in any industry, customer satisfaction plays an important role. Better data quality helps to improve customer relationships with the organization. Collecting data about customers choices helps to know them better and make data- driven decisions. Proper data management also helps to prevent duplication of the content given to the customers.
With Business Intelligence lies the Data Management which includes a broad spectrum of applications used throughout the entire BI system enhancing the businesses and organizations. Effective data management practices play a vital role in the success of any BI system. Good data management policies provides the foundation on which good Business Intelligence rests.
With data- driven decisions, BI facilities easy report generation, gathering valuable insights and imperative business solutions. Businesses Intelligence service companies offer services like establishing data governance, reporting and quality analytics to their clients for advanced business solutions.
Conclusion
In this data- driven age, it will be a foolishness to not make use of the data and insights generated by it. High quality data will ensure more efficiency as the choices made will be based on facts and data, thus helping in the expansion of the businesses.
