The introduction of AI or artificial intelligence has been revolutionary when it comes to advancement for businesses and consumers. Interestingly, it has also given rise to pressing and crucial discoveries. But, in order to make advanced analytics & AI work efficiently, we need to consider the privacy factor in today’s digital age. AI is posing serious issues for those who do not want their privacy invaded, and in fact to such a level that it has even been said to predict pregnancy prior to one’s knowledge. This is where data governance steps in, especially where machine learning and artificial intelligence are being adopted in almost all spheres of our lives. 

Understanding Data Governance

Data governance can be described as the process that involves developing internal ‘data standards’ and then enacting those rules to govern who gets data access and in what way is that data utilized for business operations and analytical applications. With the help of an effective data governance-based program, you are guaranteed data that is accessible, reliable, consistent, and compliant with regulations and rules applicable to data protection. 

Significance of Data Governance

The goal of data governance and management is to promote confident decisions backed by concrete data resources. Developing policies to define data duties, delegates, and ownership are what data governance primarily focuses on. The aim is to build an amalgamated knowledge of every data silo available in today’s system through the creation of standard data formats and definitions. 

Data management, storage, and management within an organization get to be governed by protocols, policies, and procedures that are created as well as maintained with the help of data governance-based tools. The framework of data governance within an organization can be used for frequently explaining and recording who, what, how, and why related to data. If the data within an organization is restricted and arranged on the basis of predetermined standards and rules, it is possible to improve accessibility and consistency in the process of data management. 

Ideas for Data Governance in the Era of Artificial Intelligence and Machine Learning

For effective AI data management, the following considerations should be given priority: 

• Focus on Data Quality – Good quality of data is crucial for both ML and AI models because it helps in producing reliable and accurate results. Data governance must include processes for data validation, cleansing, and standardization so that quality data is made available for inference and training. 
• Increase Data Security and Privacy – As the use of ML and AI keeps increasing, it has become crucial to protect sensitive and personal data. Data governance must be used for addressing privacy concerns through the implementation of procedures and policies for ensuring compliance with regulations and protecting data from unauthorized breaches, misuse, and access.
• The Ethics Factor – The algorithms of machine learning and artificial intelligence may impact society and individuals in ways we cannot imagine. With the help of data governance, ethical considerations can be incorporated like accountability, fairness, and transparency. 
• Data Sharing And Access – This is one aspect in which data governance can help with efficiently, especially by defining guidelines for sharing and accessing data within and outside organizations. 

In Conclusion

Data governance and management in the era of artificial intelligence and machine learning needs to be a holistic but comprehensive approach for effective AI data management that encompasses the different aspects of ethics, quality, compliance, collaboration, and privacy. Through the adoption of robust practices of data governance, it is possible for organizations to harness the power of artificial intelligence and machine learning without overturning ethical standards or privacy protection. 

Resources: https://www.losbanoslocal.com/reston-va-20190-united-states/government/maximus

 

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