Big Data fuels the big picture

Big Data fuels the big picture

Data. Such a small word with enormous implications. Data encompasses everything from formulas to coordinates, symbols to DNA, as well as artwork and stock prices. Data is in everything, everywhere, and growing at a staggering rate. One study projects that worldwide data will grow by 61% by 2025, most of which is unstructured—put simply, unorganized.

As humans are busy living their lives, they’re also producing massive amounts of data, like photos, GPS routes, search queries, data from smart devices, and even shopping habits. So it should come as no surprise that enterprise businesses are overwhelmed trying to wrangle that data, ideally in an efficient manner. The challenge looms larger every day, as another study found that up to 68% of most organizations’ data goes unused. Unused data is essentially untapped potential. All that unused data can leave an enterprise without context for what’s really happening inside their organization, where a data fabric architecture can help contextualize.

While enterprise businesses continue to accrue data, the sheer volume being produced means those businesses demand a powerful infrastructure to parse through it, migrate it, process it, understand it, and learn from it in order to strategize for the future. That’s where a data fabric can help.

So what is a Data Fabric? No, it’s not clothing for your metaverse avatar. Data Fabric is an architecture design concept. It is a blanket layer that sits across various data sources like cloud systems, on-premises systems, or SaaS applications and integrates these sources through automation. Within that, a data fabric helps to govern who has access to the right information, keeping it secure, and in the correct hands.

Luv Aggarwal, a data platform solution engineer at IBM, says, “Data fabric is not a product. It’s an architecture, it’s a way for an enterprise to set up their data landscape, so no matter what innovation comes out in a couple years, a data fabric will be able to capture that data.” The sheer volume of data projected businesses will have to contend with, warrants a system that also can provide insights for an end user, like a data scientist or an IT leader, for example.

There are three main pillars of a data fabric architecture; the first is access to data wherever it resides. The second pillar is governance or policies set in place for the data being accessed. And the third pillar is about exposing that data to the folks who need it, using natural language processing.

Consider this simplified scenario: Sheila enters a museum. She grabs a map with information about where to find impressionist art, helping Sheila access the right gallery. As Sheila walks the impressionist gallery, she reads the placards beside each piece of art, with more data about the artist, like the year it was produced and or materials used in the artwork. This space is a gallery, so certain policies apply– so she wouldn’t dare touch the artwork. Lastly, Sheila snaps a photo of the entire gallery, she’s able to assess the influence of one artist on another, as well as the motifs and ideas expressed throughout the style. All of which helps Sheila, the end user, better comprehend the data, or in this case the artwork, and can contextualize everything she’s just seen. Sheila may not be an enterprise, but her experience will help her navigate the art world a little bit better.

Data can be incredibly granular, and using a data fabric allows for a holistic and clearer assessment of any organization. Whether it’s a gallery of impressionist art or data points of a customer’s streaming habits, a business that sees the big picture can make better choices within its enterprise.

Taken together, data has the power to provide clear insights, helping business leaders strategize for the future of their companies. As the data in our world continues to grow, enterprises will need to be prepared with the right tools, like a data fabric, if they’re going to maximize the value of their data. As Luv reminds us an enterprise “can use that data to make predictions and changes about their experiences today, and having that kind of insight is going to be the real differentiator in the competitive marketplace of tomorrow.”

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