The past decade has witnessed exponential growth in the volume and complexity of data. The growth of Social media, Telecom services, and introduction of new services like 3G/4G/LTE, IPTV, Mobile Apps, etc. have resulted in an enormous amount of data – both structured and unstructured being generated every day. This has, in turn affected the IT strategy of organizations mandating the need for newer technologies and better ways to deal with their most valuable asset - data.
For any organization, its data warehouse remains the key component of the IT infrastructure serving varied business requirements including Operations, Business Intelligence, Master Data Management, Information Governance, and Analytics through data. With key decisions being made based on the reports from these systems, the underlying data behind these decisions need to be 100% reliable. The reality, however, is quite different. There are inconsistencies, redundancies and anomalies in data as stored by organizations through its current systems and practices. These anomalies cascade further into the reports and can have potentially disastrous impact on business decisions and outlook. In fact, as one of the Analyst reports points out:
- Poor data quality is a primary reason for 40% of all business initiatives failing to achieve their targeted benefits
- Data quality effects overall labor productivity by as much as a 20%.
- As more business processes become automated, data quality becomes the rate limiting factor for overall process quality
Organizations are fast realizing the importance of Data Quality and are actively taking measures to correct and improve their Data Quality Management practices. These measures are often a multi-pronged exercise – including changes and improvements required in approaches, organizational models, techniques, and technologies used to store, measure, and use data for the purpose of business processes across the organization.
So, how do we improve data? Before we try to address this challenge, it is important to bear in mind that improving data is not about fixing the data quality issues anymore. It is about empowering users to derive value out of data.
In our latest whitepaper “Improving Data Quality – A Beginner’s Guide”, we delve deeper into the various measures and practices that can be employed by organizations to have an effective data quality management practice. Download the whitepaper here to learn more about improving the quality of your data, and making your business more efficient.