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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.

(Sergey Nivens/Shutterstock)

Besides improving quality of data processed, proper data governance strategies include ensuring the reliability of data source, smooth data integration, holistic understanding of the clients 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.

AIs 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. Data act laws like the GDPR and Data Protection act, also ensure that data of individuals are safe and protected.

(metamorworks/Shutterstock)

Data privileges and access can be collaborative between the user and owner through a wide variety of tools, processes to enable faster workflow management.

Adequately holding training programs, sessions and knowledge resources to upskill the workforce management on recent data security and governance trends. They should also be trained on the emerging technologies like cloud platforms, big data, machine learning, and artificial intelligence. Clear distinction between different data roles and responsibilities like data stewards, data owner, head of data management should be done through a RACI matrix (i.e. Responsible, Accountable, Consulted, Informed). This will ensure organizations to remain competitive in the data market.

In order to deal with data methodically, tools form the most critical resource to invest on. It forms the basis of the policies and processes and aids the human workforce. Tools ensure measures of integrity and security right from data mining to data profiling. It enables better decision making, operational efficiency, understanding data lineage, improved data compliance and increased revenues. Given the varied and complex data platforms, making use of Representational State Transfer (REST) APIs enables a uniform data view across the organization.

Leveraging the correct technology and algorithm on large scale datasets help in efficient analysis of real time streaming data to provide instant feedbacks. This is the most common use case scenario in banking transactions or modern-day wallets and UPIs. Technologies like cognitive and automation can be used to enable best security practices across all data mediums.

Since most organizations are transitioning from on premise legacy systems to the cloud environments, cloud data governance is the next big thing to focus on. The next generation cloud platforms have well incorporated aspects of data security but a well chalked out data governance strategy is of utmost importance for securely migrating data to the cloud and later when it is stored there as well.

Some challenges that can arise are data sovereignty-that fixed state or country for data storage, but data decentralization (a key feature of cloud platforms) fixes it.

Adoption of data laws across countries has changed the scope of data governance. Line of business want to be known as more accountable and trustworthy with the data they are processing to run their business.

Hence, data governance should now be an operational need rather than a set of policies; as better management of data lead to better insights which in turn impact revenues and profits as well as objectives of customers and stakeholders.

About the author: Anu Chowdhury is an analytics consultant at Brillio with a history of working in the information technology and services industry, mainly skilled in Data Warehousing and Business Intelligence.

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Making it Real: Effective Data Governance in the Age of AI - Datanami