“Let’s look and see first with a small amount of data at a lower level of detail, does it actually have the potential to give you new insight and if it has potential, then you’ve got some sort of ready reckoner marker to be able to say ‘well if I spend up to this and if I put this infrastructure in place, there is opportunity there’, then we can go forward,” said Clarke.
“That message is going down quite well with our customers because with a lot of people when you talk about analytics, it’s a difficult conversation because it’s such a nebulous subject. There’s a lot of organisational change happening within this space as well because analytics is really part of the business conversation and isn’t, or shouldn’t be, a purely technology conversation.”
According to Clarke, that’s where some of the issues that have plagued analytics have come from – many technology vendors treat the analytics question the wrong way.
Business insight
“Data analytics and big data have to start with ‘what’s the business insight you’re looking for’ and I think that’s key. There are a lot of new technologies coming out now but they’re focused on how to cost-effectively run analytics across massive amounts of data. That’s really what the technology has been focused on for a long time. But from an analytics perspective, there hasn’t been enough new algorithms created.”
Paddy Loughnane, lead data architect with Triangle thinks that data analytics is going through something of a rebirth, driven by the wide scale adoption of big data technologies.
“I think big data has energised the existing business of data analytics and brought it back into focus. Data analytics had nearly gone into legacy mode, it was something that was well established and almost old hat. It became very integrated into organisations and I think big data has re-energised that and brought a new focus onto it and is extending how people view data analytics and the sorts of things that it can deliver.”
The various uses that data analytics is put to depends on the industry and sector that any particular organisation is operating in. But Loughnane believes that it is companies with large numbers of customers who stand to benefit from it the most.
“For some organisations, particularly for the more mature ones, what we’re seeing is that a lot of it is being driven by how they deal with their customers. They’re getting much more information on how they deal with their customers and getting more timely information on what customers are actually doing and how they can respond back to their customers,” he said.
Fraud prevention
“Fraud is another big thing that we’re seeing a lot of interest in, particularly in the betting and the gaming industry because of the real time availability of streaming data. Betting organisations can actually detect patterns of fraudulent or potentially risky activity by analysing real time information.”
Other organisations are grappling with what big data could actually mean for them.
“There’s a lot of focus on big data in the market at the moment and there seems to be a feeling that it’s a bit of a silver bullet. If you build it, you know, you can deliver from it. It’s setting a lot of expectations, particularly at the higher levels of organisations. The thinking seems to be that ‘we have all this data’ so let’s just load it up,” said Loughnane.
But this is not the best way to go about the process of deriving business intelligence from data, particularly for companies at the earliest stages of a data analytics strategy.
“There is valuable insight to be had because many organisations have a lot of dormant information that they would never really have considered analysing. For example, there is a lot of valuable information in old document-based content management systems, in that sort of unstructured type of information. That is typically the kind of data that didn’t necessarily sit very well into very structured older analytics systems.”
Managing expectations
For Art Coughlan, cloud and enterprise business group lead for Microsoft Ireland, expectation management is a key issue when it comes to data analytics and big data deployments. The hype surrounding these technologies in recent years has been such that often senior management need to temper their expectations of what is possible.
“We find that expectations are being set very high. When we go talk to customers we typically find that they expect immense power and value in analytics and what we find is that they actually struggle with joining the perceived power that’s there with the business problem they’re trying to tackle. There can be a chasm between the headline benefit and how they apply that to the problems they face within their business,” he said.
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