PLUGGING DIGITAL LEAKS
Big data is a big conversation. It’s also leaving behind a big mess. Data are gathering in pools and lakes and as we dip our toes into these murky waters, we see a sign that says, ‘Here be dragons…’ TAMSIN OXFORD
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LAUREN MULLIGAN
he student standing in the corner tapping updates onto her Instagram profile. The tutor sending a quick WhatsApp to his wife, ‘Sorry, I’m going to be late’. The accountant uploading documents to the company intranet. Marketing releasing the monthly newsletters. Each individual adding another byte to the data lakes pooling in virtual space, filled with structured and semistructured data that teases insight and value but never quite seems to deliver.
OCEANS OF INFO
This data is supposedly capable of helping decision makers gain granular insight into their business yet the nature of data is constantly changing in both how it is captured, why it is analysed, and what value it can deliver. It’s an evolution from hastily scribbled notes about the good, the bad and the organisational ugly into digital archives that have swollen with information that has no context or relevance and yet whisper about possibility. “The computer revolution made it economical for data to be stored in increasingly complex and ever expanding data storage solutions,” says Phumlani Khoza, Associate Lecturer, School of Computer Science and Applied Mathematics and leader of Scilinx Research, a business solutions design and research laboratory. “The problem is that data hasn’t been strategically recorded in such a way as to deliver a specific economic value, or considered in light of ‘If we do X with the data, then we will achieve Y’. Instead we now have tons of data and no clear vision or idea on what to do with it or how to get it to share its most valuable secrets.”
POTENTIAL IN THE POOL
Khoza teamed up with 10 other researchers to develop Scilinx Research with the goal of advancing the operational capabilities of organisations through a hybrid structure that targets the generation and application of value-creating research insights. In short, brilliant minds applying themselves to the data conundrum, working to pull out its potential from the mess that relentless data collection has left behind. The goal is to create
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intelligent networks that define the next generation of analytics and how data relationships are interpreted across multiple data platforms and sources. At the peak of the big data hype, people were trotting in with fancy algorithms and mathematical constructs supposedly designed to whisk out insights from within these lakes of data. Yet what they saw, what they found, didn’t make much sense. The problem wasn’t the data but the questions that people were asking. Pipelines built to carry data insights into stressed executive offices literally leaked insights from every conduit but they lacked relevance. Where was the insight that would help the business make a decision that would positively impact bottom line or customer engagement?
DATA CONUNDRUM
“Businesses were told that if they built these data centres and gained access to all this computational capacity that they could extract economic value from this data,” says Khoza. “But when it came time to do this extraction, it couldn’t be done. The off-theshelf solutions were incapable of dealing with the heterogeneity [differences] of the data. These collections of data across email, social media, and operations, that were different dependent on the organisation, were impossible to unify into single solutions. You cannot interpret the data-powered insights from a supply chain company against one that operates in financial services.” What happened next? Companies started to invest into the potential abilities of emergent technologies such as machine learning (ML) and artificial intelligence (AI) – technologies capable of deep diving into the data and scouring the murky depths for even the tiniest grain of relevant insight. These technologies are essentially the pen needed to connect digital dots. Yet they too slip at one hurdle – context. Is the data generated for the marketing department being interpreted by a data scientist who understands what marketing needs?
SCIENCE-LINK SOLUTIONS
“Scilinx combines machine learning and science to find out what is happening,” says Tresia Holtzhausen, a member of the Scilinx Research team and a lecturer at Nelson Mandela University. “Everything around you is a system and these
