Kenneth Kuek of InterSystems in conversation. Source: Tech Wire Asia.

Business Intelligence thought leaders: Ken Kuek of InterSystems

When the technology press’s hyperbole machine is in full swing, many journalists are guilty of waxing lyrical about torrents or deluges of information: data that’s available to today’s organizations via business intelligence platforms. Finding the value in this previously untapped resource has taken up many column inches, so before these pages become guilty of the same journalistic misdemeanors, it’s worth looking to the data industry’s leading figures to perhaps bring both the data sector and many publications’ enthusiasm down a notch or three.

We spoke recently with Kenneth Kuek, the Business Development Director of InterSystems, just before the big tech event of the APAC, the Big Data World expo, that took place in Singapore this year, where there were business intellignece platforms a-plenty on show.

“Now people are smart. They think that ‘Oh, I don’t need that amount of data; let’s choose data that [we] are able to interact, to better make use of it, and of course, use machine learning and AI to achieve better outcomes.'”

InterSystems’ specializations are in two areas where data mining for insight has been more advanced to date: the financial and medical sectors, with a strong client roster, especially in the former: JP Morgan, Credit Suisse, and HSBC. But InterSystems’ IRIS platform capability also fits well with the medical sector, where patient and pathological information is rapidly becoming digitized. “In healthcare, we are actually able to produce analytics, not only in the application layer but also in the data layer. We’re able to wrangle the data, for example, for the researchers’ analytics, [and] for the healthcare worker to understand, to digest data, and produce detailed reports,” Ken said.

Smart Business Intelligence Platforms

The mention of AI or machine learning often goes unchallenged by some data platforms’ potential audiences, but Kenneth was quick to point out that the technology is not a magic wand that can be waved over wildly different data sets to produce ground-breaking results. “So, a lot of people are trying to sell AI [or] machine learning services. But it is not that straightforward. Yeah, it’s not, ‘I have two terabytes of data and I’m just going to throw [that] into your analytic system, and I’m going to get the result that I want.’ To understand the output, or the needed outcome, is most important.”

A company like InterSystems that offers AI-powered analysis as-a-service naturally values data science and the rigorous processes required to extract meaningful results from very different data sources. And the value placed on data professionals is reflected onto InterSystems’ customers.

“We still need data scientists to come in to provide the parameters, depending on what data sources are wanted. […] We render the data in order to make [it] cleaner and very easy for the data scientists to apply the parameters and output to the reports the user expects. So it’s not that you engage [directly] in the system; you subscribe to our IRIS data platform. And, we still need professionals like data scientists to draw the perimeters: this is something very important.”

The Need for Data Science Jobs

Kenneth told us that boardroom decisions to engage in data-based projects are increasingly common, but often, the wrong choice of solution type may follow. In the short term, the use of self-built machine learning data systems will increase, assembled from the various fragmented open-source libraries and methodologies available. But in the longer term, for faster and generally more valuable results, “I think a mature data platform like the IRIS system will still have a very, very strong foothold.”

In the same way that many organizations prefer to pay others to maintain and run compute, storage, and networking in the form of cloud provisions, Ken sees InterSystems as the AI in the cloud – more scalable, more reliable, less resource-intensive, and producing better bang-for-the-buck. Ken is realistic in that AIaaS isn’t (yet) as simple as spinning up an S3 storage bucket. That’s refreshing amid the hyperbole, where seemingly every product, however mundane, is badged with ‘machine learning for better results.’ IRIS is keeping its feet on the ground (although it’s located in the clouds).