Whether they realise it or not, businesses generate lots of data. The amount of production, the number of employees, the supply coming through, almost anything quantifiable is data.

Modern data science has enabled the collection of even more reams of data. Through an array of sensors and tracking mechanisms, businesses can collect data from virtually every point of interest. In fact, the volume of data generated per day is expected to hit 463 exabytes globally by 2025. That is about 100 times the volume of the Internet today, or about 6,000 times as many books in the United States Library of Congress.

Sifting through all this data to make sense of it is not feasible for any company of any size. Though the emerging field of Big Data aims to make sense of such vast amounts of data, it needs to be broken into manageable sizes.

Chiew Kok Hin, Chief Executive Officer of AIMS Data Center in Malaysia says, “At the end of the day, it depends on the data that the specific company needs. If you know what you want, you can extract from it. If you don’t, then the data generated is trash. So, it is important for the company to learn what sort of data points are important to them. Working with people who provide data analytics can help with this.”

“For example, in a shopping centre, sometimes they provide free Wi-Fi access points. Each Wi-Fi access point can be used to generate a traffic heat map, showing areas with the greatest foot traffic. And when you log into Facebook, Twitter, or e-mail to use the free Wi-Fi, you are also giving them information like age, gender, et cetera. There are all sorts of ways to use this information, or use pattern recognition and analytics to make sense of this data,” adds Chiew.

Another problem that businesses may have is legacy data, where data generated since the business started is kept or archived haphazardly. This data can stretch back decades, and can sometimes be in an undigitised format.

Chiew recommends that this data is first digitised and uploaded onto the cloud for easier access. “With this, you are able to retrieve the data anytime you require. But this also allows data analytics to filter the data that you have, to make sense of it,” he explains.

But among SMEs, cloud and data analytics adoption is still low despite its proven benefits. This is especially due to cost and security concerns. More than that, inertia from ‘doing things as they always have been’ is prevalent among SMEs.

To address this, Chiew explains that a paradigm shift in thinking must occur, similar to how computers were viewed at the dawn of the IT age. “We have seen so many companies that are small in the beginning, but are able to scale because of the use of IT,” he explains.

Finally Chiew says that, “For us, we need to demonstrate the fact that our product is solid, to help this paradigm shift. Our message is simple: you do what you do best we do what we do best. We provide infrastructure like data centres, connectivity, some engineering services and so on. We will make sure that the scalability is there, the agility is there, and the flexibility is there. You don’t have to find your own resources, because we are there for you.”

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