August 31, 2020 | Divya Khare
Data is equal to knowledge. Data management is equally important for an organization as much as preserving the data for future reference. It is, as the name suggests the practice of collecting, keeping, and using the stored data securely in a cost-effective manner. The objective of data management is to help organizations optimize their use of data within the vault of policies and regulations. This in return helps the decision-makers to take prompt action towards maximizing the productivity of an organization.
Study on Data Management
Experian conducted a survey to look at the global trends in data management. As stated in the report, 98% of companies use data to improve the customer experience. In the same study by Experian, 30% of the poor data quality impacts the ability to deliver excellent customer experience.
The study also revealed that 89% of businesses report that they struggle with data management. This affray leads to a shortfall of primary data. A data strategy can be built only if the businesses identify their goals and project them in the right direction. These goals are vital to formulating a productive data strategy for AI and Analytics.
What is DataOps?
Gartner defines DataOps as “a collaborative data manager practice, really focused on improving communication, integration, and automation of data flow between managers and consumers of data within an organization,” explained Nick Heudecker, an analyst at Gartner and lead author of Gartner’s Innovation Insight piece on DataOps.
DataOps is generally used to bridge the gap between the development pipeline and the stakeholders eliminating all kinds of miscommunication. It is still maturing and finding its ways in the business world.
Because of the enormous amount of data that is produced by the organizations, performing data analytics requires complete automation. This includes tasks like testing of the validity, checking the behavior of the data pipeline, and detecting anomalies. The foundation concept of DataOps emerged stating that “analytics is code,” which refers to the summarising of everything, from automation to ETL routines, to analysis of the routines.
Power of DataOps
DataOps is empowering enterprises to modify their data management and data analytics processes. While DataOps is implementing techniques to overcome data management failures, it has also focused on applications with small databases by executing smart DataOps strategies such as cloning, predictive analysis, automation, and much more. The principles of DataOps are enabling businesses to act on their huge production datasets in several that could not even be imagined a few years ago
Advantages of DataOps
Pluto7 is a tech-enabled company helping the companies seek an out of box framework in order to make their businesses follow the new trends of technologies. However, DataOps is effectively increasing data consumption along with following the security and governance policies. DataOps is not just a tool but a collaborative effort to ensure data quality that supports AI/machine learning and also ensure enterprise data is risk secured. Click here to know more about how Pluto7 is implementing DataOps.
If you are looking for some help on your data front, feel free to write us at firstname.lastname@example.org