AI and Data Integrity have the potential to power up trusted business decisions to a great extent

Undoubtedly, AI and Machine learning have taken over those IT organizations that are seeking competitive advantage, through digital transformation. Both AI and Machine Learning play a critical role when it comes to data integrity. More than 75% of the organizations across the globe, are prioritizing AI and Machine learning over traditional IT practices. Traditional IT practices have failed to handle the enormous volume of complex data available to organizations today. In order to analyse the huge amount of data, organizations have to adapt faster means, and this is where AI and Machine Learning prove to be beneficial. Business decisions in every organization surely will power up if, AI helps to manage the huge amount of data, to improve the process of data integrity.

What is Data Integrity?

Companies need trusted data and not big data. Data Integrity will look at the whole life cycle of the data generated, stored, accessed, and applied to accomplish specific business tasks. The agenda of a good data integrity program is to ensure that the data available is absolutely accurate and error-free. A strong data integrity program will commence with the understanding of what data we are aiming to track, how it enters our systems and how it is stored.

AI in Data Integrity

As per reports, by 2025 on a global scale, people are expected to generate 463 exabytes of data each day. It is impossible for companies to work on the entire data through traditional AI practices. Fortunately, AI can make this task possible, as it works faster than humans and can work constantly, without any breaks. When it comes to working on enormous data, to improve data integrity, AI can automate quality checks and make recommendations.  Some experts believe that AI algorithms have the potential to analyse data sets that would take years for humans to implement or longer.

How Can Data Integrity and AI Power Up Business Decisions?

Every organization stores critical data assets, such as customer and employee records, and these data are stored in hard-to-access data stores. Every traditional IT structure of an organization comprises a variety of enterprise applications and their related databases, multiple data centers, and some new data, which gets generated in the cloud. These mentioned factors can lead to data cribs and the organizations fail to ensure the accuracy of the data. Organizations that are still following the traditional IT practices for data integrity are struggling with data cribs to a great extent. According to a study, more than 45% of the newly created data records have at least one error. Hence, the business leaders who require quality data to base their business decisions, are at risk. Here comes AI as a savior. Understanding the changes in the data by using AI and machine learning, alert data drifts, and recommended quality rules, can help to improve the quality of the data as well as automate the data pipelines to reduce the manual workload. This kind of automation will help to increase data visibility and data observability. Demographics being one of the important third-party data also aids organizations to eradicate data bias by providing a comprehensive view of customers. Though, both AI and machine learning models are benefitted from enriching data with third-party data enrichment models, it is important to understand that both AI and machine learning are equally important for creating them. Organizations trust AI as it can deliver maximum and accurate data at a limited period of time. Data integrity is crucial as, without data integrity, one cannot trust the data and also cannot trust the business decisions based on that data.