Artificial Intelligence (AI) and Machine Learning (ML) are transforming the way enterprises manage data. Previously, companies struggled to access and use data as a result of relying heavily on manual and inefficient data governance methods. But with the advent of AI and ML, businesses are harnessing the power of automated data governance methods and are able to access, process, and manage all their data at a striking pace and accuracy.
One such automated governance method is the use of a data fabric - a single, unified architecture, featuring an integrated set of technologies and services able to deliver enriched, meaningful, and reliable data in real time.
These impressive capabilities are delivered by AI, which uses ML techniques and algorithms to streamline and simplify information retrieval and analysis. Businesses can rely on AI to discover and integrate data from multiple sources into a centralised view known as a virtualisation layer, where it can be automatically or semi-automatically mapped on to a business ontology. Enriched with actual business meaning, the data can then be accessed, reviewed, and indeed used by anyone.
A marketing manager, for instance, could access data via an internal marketplace and then use intuitive self-service tools to pull reports and other information without the need to liaise with IT or data departments. This prevents valuable data from being hidden away in silos and saves the time and resources that would have otherwise been spent by various departments retrieving, sharing, and interpreting data from across any one business.
On the security front, ML algorithms can recognise sensitive data and ensure that it is masked or encrypted by automatically applying actionable data governance policies. Customer banking details, for example, could be changed into an unreadable format, meaning that only authorised personnel with the relevant access level can view these details. Relying on ML here guarantees that proper security controls are applied to data, which can help businesses improve compliance with tightening data laws and avoid staggering GDPR fines such as the EUR 1.2B Meta is currently challenging. In addition, ML techniques can be used to record the lifecycle of datasets, tracking things like transformation, storage, and disposal. And this information can then be used to demonstrate compliance with data regulations.
These technologies are also helping to improve the quality of data. Validation and duplication rules can be automatically applied to datasets to ensure accuracy and integrity. For example, ML algorithms can be trained to check that every sale recorded on a database has a valid product code and transaction amount, and also to delete any duplicate sale to ensure that redundant entries do not reduce the accuracy of the overall database. Utilising the powers of AI in this way is both faster and less error-prone than human effort, thereby helping businesses make quicker and better-informed decisions.
What’s more, generative AI is quickly racing to the forefront of the data market in each and every industry. If you’ve used a generative AI chatbot, you’ll understand its power, both in daily life and in business. These AI tools can examine patterns and commonalities in datasets to produce texts, images, and answers to questions, and make it look as if the results come from a human rather than a machine. For enterprises, this isn’t a tech trend - it’s a game changer.
With generative AI, businesses can outsource manual tasks, boost team productivity, and nurture a competitive edge. The finance industry, for example, stands to benefit from the likes of task automation, enhanced fraud detection, and more personalised services. In terms of improving the customer experience, AI-driven conversation tools are able to answer complex
queries in the user’s natural language, such as ‘Can I increase my monthly mortgage repayments?’ or ‘What is the best investment plan I can safely afford?’ These revolutionary capabilities are transforming operational systems and processes from the ground up.
However, it is important to recognise that generative AI tools are only as good as the quality of the data that they have access to. This is why employing an effective automated data governance method is indispensable to anyone who wants to get a handle on their data and capitalise on the recent boom in generative AI tools. With this in mind, businesses should first set the table with a data fabric architecture to ensure that their generative AI tools are fed reliable and robust data, and therefore produce reliable and robust insights. Without the undergirding of an effective data governance method, the potential of generative AI will remain dormant or partially discovered at best.
AI and ML are driving data management to new heights, absorbing complex and lengthy tasks typically completed by data experts. This has dramatically reduced the expertise required to access and use data at scale, empowering less tech-savvy employees and businesses alike. Not only that, but thanks to AI and ML, businesses that incorporate a data fabric product also reap the benefits of being able to navigate complex data sets at minimal cost and maximum speed, while enjoying enhanced data security and quality. But perhaps most crucially, businesses that adopt automated data governance methods, enabled and empowered by AI and ML, lay the groundwork for the efficacy of existing and future generative AI innovations.