Data-driven businesses, especially large-scale enterprises, have a problem. Too much data can end up floating in the void, present but not used. Visible but not actionable. If you aren’t organizing and storing data in such a way that you can use it, then your business has effectively become a hoarder – and it will end up costing you.
The good news is that the right data fabric solution can help you better manage, track, and store your data. As a result, your information is primed and ready to meet the ever-changing customer and client needs.
Of course, with the current economic uncertainty, the cost of building a data fabric solution can be daunting. The good news is that you can drive down operational costs and boost efficiency with this solution; just use this guide.
Data Fabrics: An Introduction
Wondering what is a data fabric? It may be a new term, and that’s okay. The key thing to note here is that it’s far from a new concept. Using the user and operational data that your business generates even while you sleep to pluck out better, more informed insights and strategies has been the go-to advice for decades.
Data fabric, as an infrastructure, simply makes it easier. It’s a combination of data architecture and dedicated software solutions that work to pull together and centralize, connect, and then, of course, govern data across systems. This means it collects and stores data from different locations and departments into one central spot.
The goal of a data fabric is to create one true source of information. With a sole destination for all data from your company, analytics and automation are much more achievable.
Why You Need a Data Fabric Solution
We are a data-rich society, and that is becoming a problem. According to Statista, in 2021, we produced 79 zettabytes of data. This number is only increasing, especially due to the arrival of generative AI, which has cut down the production time of content considerably.
Making sense of that much data is not easy – nor is it cheap. That’s why so many businesses have fallen short of their data analytical goals.
Hope, however, is not lost. Thanks to data fabric infrastructure, you can benefit in these key ways:
Cut Out Redundant Files
One of the biggest wastes of money is storing information several times over – and this can easily happen when you have several sources of data. If each of your international offices has its own collection of data, overlap is bound to happen.
By centralizing your international data sets into one single source, you can cut out those redundant files. This streamlines the information you have, cuts back on storage costs, and also makes it easier for every team to access better insights.
Remove Outdated Information
Old files are another big storage issue, and the more areas, servers, and systems you have, the harder it can be to weed them all out without accidentally deleting key information. That’s why centralizing your data can help. With all information located in one single source, especially if that source is organized and prepped with a robust data fabric infrastructure, you can quickly find and then remove old and outdated versions of files. This once again reduces the cost and effort needed to maintain your data.
Improve Data Analytics
Without those unnecessary files clogging up your system, and with all your available information collected in a single destination, you can finally start to streamline your data analytic process. This is essential when it comes to boosting efficiency and reducing the cost of your heavy-duty enterprise, particularly if you operate on a worldwide scale.
Not only does cleaning up your data boost the efficiency of analytical tools, but it also makes it far easier and more accessible for employees. After all, with 70% of employees expected to work with data by 2025, as opposed to 40% back in 2018, the need for accessible (but secure) data has never been more apparent.
How to Get Started Building a Robust Data Fabric Environment
First things first: data fabric is not a single tool. It’s not a solution you can invest in once and then call it a day. Instead, it’s a system and approach; it’s a full-scale strategy. Yes, this means you will want to use tools and software. Yes, it means you can outsource experts or even infrastructure.
The only caveat is that you need to go into it with the goal of centralizing your data and then making it work harder for you. You can do just this by following these steps:
1. Explore Providers
Can you design and build your own data fabric solution? Yes. This approach is specifically called a data mesh, but that’s a later-stage approach. Developing all the tools you need in-house takes time and trial and error, so you’re best working with third-party providers at the start.
2. Work in Phases
Things are only going to get messy if you try to tackle the entirety of your data all at once. Instead, work in phases. A good way to choose which department to invest in first is to consider which team would best benefit from a data fabric solution. This could be marketing, operations, or even your IT departments.
3. Create Uniform Metadata
One step that you must agree on and train all teams on is metadata. Metadata is data about your data, and using the same approach across offices and departments is how you will be able to search and use this incoming data most effectively. Make sure that terms, keywords, and your strategy match up so your data can be accessed universally.
4. Invest in Machine Learning and AI systems
AI today is, for the most part, advanced machine learning (ML), and using machine learning is the key to optimizing your data fabric and extracting the largest amount of value out of it. With the right machine learning (otherwise known as AI) systems, employees, managers, and even executive staff can easily extract actionable insights in seconds.
Final Thoughts Before You Get Started
If you don’t know where to begin, always get a professional to guide you through the process. This could be someone who works for a robust solutions provider that’s helping you implement their tools in your business. The largest companies, however, would do best with a custom solution, so bringing in an expert in the field is a must to drive up efficiency while dropping operational costs.
