Big data streams and the analytics systems that can make sense of them are becoming easier to deploy due to the advent of cloud services, better tools and more data scientists, but new challenges are emerging such as increasing data privacy regulation.
One constant with big data is that it just keeps getting bigger.
As PwC Australia Partner Rob Parker puts it, there’s a wave of data pounding on the doors of organisations these days and the surge is only going to get stronger in the future.
“We have got this data tsunami where everyone has realised that the amount of data we have generated in the last five years is just growing tremedously,” he said.
“We know we need to keep it, so the storage requirements are increasing exponentially. Trying to leverage all that data has been a challenge for organisations.
Mr Parker sees modern day cyber security systems as representative of how big data and analytics work together these days.
The massive streams of data hitting the outer ring of an organisation’s cyber security defence pose a big data analytics challenge.
“In terms of the security aspects, it actually becomes more and more challenging to improve the posture of your security because you have to sift through all that information.
“Arguably it was easier ten years ago –when the data volumes were not so massive – to find the bad guys in the system. Now there is just so much data, it’s easier to miss them”
“That’s where the analytics comes into play – being able to sift through that data and apply some intelligence to it,” says Mr Parker.
“It’s like a balancing act – ingesting all this data and finding a way to make it useful.”
That balancing act is becoming somewhat simpler compared to the early days when organisations had to roll their own systems from scratch as well as track down teams with then rare skill sets such as data scientists.
These days the toolsets for big data and analytics are more mature and there are a lot more data scientists than five years ago, although the job category remains in high demand.
“Data scientist is still one of the highest demand jobs there is in ICT today,” says Mr Parker.
Ready-to-run cloud services are also helping organisations with their big data and analytics projects.
“Being able to leverage the cloud makes a difference. To harness big data and analytics you need a lot of computers and storage, and having cloud environments now has allowed organisations to do that, particularly in the ASX top 20 company list and large-scale government departments.”
In a recent Gartner survey of the marketing analytics world, almost 75 per cent of respondents said consumer privacy concerns would form barriers to marketing analytics practices.
At the forefront of consumer privacy concerns is the European Union’s General Data Protection Regulation (GDPR), which became enforceable in May this year and touches entities that process EU citizen data anywhere in the world.
Mr Parker does not see the GDPR regulations as an inhibitor of big data and analytics projects, but rather a force that should help the global business community avoid privacy disasters.
“I don’t see GDPR as an inhibitor, I see GDPR in the EU as a great example of trying to get ahead of the curve in terms of technological growth before it becomes too difficult,” he says.
David Moore, Senior Technology Partner at PwC Australia sees lots of market need from inbound and outbound call centre analytics.
On the inbound side, call success rates are around the 40 per cent to 60 per cent mark but outbound call success rates are still low.
“People want to know some pretty basic stuff,” from their investments in data and analytics systems says Mr Moore.
“Things like unanswered rates, ring off rates, time on call, call completion, call satisfaction and a whole lot of things that are direct inputs off the call itself,” says Mr Moore.
“My anecdotal understanding of outbound call centres is that they are becoming a very ineffective means of communicating with potential consumers and markets because everyone is bugged by overseas call centres, unwanted calls and phishing operations.”
Legitimate outbound call centre operations are giving way to online self-service portals.
The analytics engine sitting behind these portals are getting smarter every day, says Mr Moore.
“When I hear of the opportunities that enterprises are creating out of this, they are definitely getting smarter. These engines are sensitive to size, colour, style, fit, age and the potential for on-selling co-products.”
Automated sales techniques such as brewing up combination offers on the fly, warning of low stock levels or creating spot deals are becoming more and more compelling for consumers.
Another sales analytics trend centres around time of day, particularly with food, says Mr Moore.
“Businesses know that when they have an inbound enquiry at certain times of the day, they have a highly likely success rate on food orders and co-dependant food orders – like pizza going with cola or ice creams.
“Analytics engines are becoming very attuned to these types of combinations, and in more complex relationships than that of pizza is to ice-cream or cola.”
Syndication and co-sharing customer data is another data analytics trend and customers signing up for login rights to an online sales site often agree, sometimes unknowingly, to agree to co-sharing which is mentioned in the sign-on fine print.
“The value in syndication means that five participants spending a twentieth each are getting one hundred percent of value out of this.”
“Some of these back-end services are in clouds, they are pretty easy to stand up, they have a standard solution stack, they have a big data engine and an analytics engine that can be experimented with through their own algorithms.
“This is all becoming finely tuned,” says Mr Moore.
With the advent of analytics systems and services to better manage and understand big data streams, and recent changes to the regulatory landscape, it is clear that businesses must equip themselves with the tools and talent to manage cyber risks, gain data insights and create value for their customers.
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