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How Data Fabric Supports Advanced Analytics and AI Initiatives

By Umesh Kumar·Published July 10, 2024·Updated July 15, 2024·Technology

Advanced Analytics and AI Initiatives

AI and machine learning have truly taken off in the past few months and will only become better and more ubiquitous as the years go on. The question for every business now isn’t whether or not to use these systems but which tools and when to get started.

Of course, getting a prime and functional AI system up and running just isn’t possible without a well-rounded approach to data. In this case, data fabric.

How a Data Fabric Solution Can Help Support Your Analytics and AI Initiatives

Data is everywhere, and with the sheer amount that we’ve collected in the past few decades alone, we can truly understand the very fabric of our business. This is true for all sizes of businesses, but those large, heavy-duty enterprises are the ones that will undoubtedly benefit the most from revamping their data.

AI and automation can streamline your workflows, help you find new, innovative insights, and so much more. What it cannot do, however, is pull recommendations from thin air. That’s why it’s crucial that your data is properly collected into a single source of truth and stored in a meaningful way with a data fabric. Without that structure, your AI and analytics tools will falter.

Just have a look at how data fabric can help your AI and analytics efforts: 

How to Implement a Data Fabric Solution

Before you can use AI or analytics to their fullest potential, you first need to get the most potential out of your data with a data fabric.

What is Data Fabric?

The Data fabric in this case, refers to existing toolsets that collect and consolidate your data from every stage of operations. This means taking the data from your factory floors, international distribution, and customer behavior. By centralizing data like this, you will immediately be able to gain better insights from your data and prepare your business for automated data management.

What’s more important in this case, of course, is that it puts together a single source of truth, as it were, which AI and other automated systems need to work properly and to their fullest.

Improve Your Data Infrastructure

Whether you also need to improve your data infrastructure depends on where your data is currently being stored.

Some businesses are happy to use cloud-based solutions. This is a perfectly viable option, as it stores your data on several servers in vastly different locations, making information redundant (meaning it’s safe if there’s a terrible accident like a fire in one data center).

If privacy is your utmost priority and you must house your data yourself, then you will need to build a redundant, robust data infrastructure system yourself. Not only do you need the computation power to handle the data, but you’ll also need plenty of legroom so that you can expand and handle the AI tools in-house.

Use Data Governance to Sort and Organize Data

Data fabric is a technological architecture solution that transfers information to one destination. Data governance is the list of rules, policies, and standards that are added to the information in order to organize it.

You need to create a solid data governance policy and use metadata to sort and store your data in a way that’s easiest for AI and analytical tools to use. From there, it’s all about maintaining data quality while keeping your information secure and making good use of your AI and analytical tools.