Data is everywhere and organizations are amassing mountains of data. As new data and new types of data are generated every day, managing it is becoming increasingly complex. Improving data quality and data management is the key to making the right decisions and maintaining a competitive advantage.
Industry experts point out data management is the key to effective decision making and business efficiency. IT giant IBM defines four components or four V’s of big data
- Volume – There will be lot of data
- Velocity – Data is continuously coming up in a quick manner
- Variety – Data is available in different forms
- Veracity – Quality of data
A study conducted by Andrew McAfee, a principal research scientist at the MIT Center for Digital Business found that organizations driven most by data-based decision making had 4% higher productivity rates and 6% higher profits. Google is an excellent example of a company that relies on fact-based decision-making and where every employee is focused on what data can offer. Every decision that Google makes is based on data, analysis of data and scientific experimentation.
Data Quality is Crucial
Data-driven decision making depends on data quality. Data quality refers to the dependability of data for decision making. There are many processes involved such as in ensuring data quality from data capture and data cleansing to data processing and data mining. Business decisions need to be supported with data that can be verified and the success of the data-driven approach depends upon the quality of the data gathered and the effective analysis and interpretation of the information.
Tips for Data Quality Management
Data Quality Assessment: An independent analysis of the current state of data should be conducted to identify errors, inconsistencies, or duplicates. This can help in developing strategies and governance policies to address specific data management requirements.
Consider Data as a Corporate Asset: Data should be considered a corporate asset with financial value. Bad data should be weeded out as it will not only damage good data but also make it difficult to obtain clear business insights. An effective data quality management solution is necessary to identify inaccurate or corrupt data, and to prevent it from getting in to the database.
Combine Data Management and Business Intelligence: Business intelligence (BI) solutions help organizations in sorting out data sets that can be targeted for managing data quality.
