Making it Real: Effective Data Governance in the Age of AI
Customer trust is not only gained with delightful service offerings but also by ensuring that their data is safe. This is one of the key factors why organizations across the globe are now considering data security, compliance, and governance as a key business objective.
Data governance means laying down set of consistent rules and processes to ensure the quality and integrity of data throughout the business lifecycle. A data governance framework is a pre-requisite for any organization to convert data into assets and meet their strategic goals.
Today, businesses are in a race to achieve the most effective business solutions by use of data analytics, investing extensively in AI based solutions to extract maximum value from the data behemoth and enhance productivity.
Apart from improving the data quality, reliability and accuracy to make efficient business decisions, organizations also hold the responsibility of the data security and privacy of its customers given the rising awareness on their data rights. Data governance thus becomes an important aspect to be looked into which implies using the data correctly and responsibly within well-defined boundaries of standards and policies.
Besides improving quality of data processed, proper data governance strategies include ensuring the reliability of data source, smooth data integration, holistic understanding of the client’s needs, meeting the government regulations, and boosting data management on the whole while simultaneously catering to compliance, security, and legal issues.
Every year more and more data is added to the already abysmal pool of data, thereby making data handling humanly impossible or time consuming.
AI’s unique capability of learning from past experiences and adapting accordingly presents a potential of it being employed to data governance strategies such as; AI systems are employed to ensure data privacy and security, for unlike humans these algorithm based models can tirelessly monitor data and prevent cyber-attacks or security breaches. It also prevents access to the confidential data by third parties by making sure its interception by the right user. During data processing, it analyses behavioral data which form the digital records
In the times of data deluge and rapid transitions to the cloud and wide scale implementation of AI/ML, the need of the hour is an effective data governance framework for the next generation platforms with minimal risks and maximum returns. Thus, operational efficiency of an organizational can be improved by incorporating the already existing factors. It comes down to understanding how people, process, policies, technologies, and tools fit together.
In the age of cognitive technology and machine learning, most processes like metadata management, data security and data operations can be automated through a wide scope of options. Some of them include User Identity Access management, data permissions, Two step verification.
