IT Brief New Zealand - Technology news for CIOs & IT decision-makers
Story image

Data and analytics: Too difficult to understand?

Thu, 1st Dec 2016
FYI, this story is more than a year old

Data and analytics can derive significant value for some organisations, however, the concept may be too difficult for some to grasp.

That's according to Teradata, who says some businesses struggle with the concept of Big Data because they don't think of themselves as businesses who have a lot of data, or because they don't have the tools to process and analyse the data they collect.

Alec Gardner, general manager, Advanced Analytics, ANZ, Teradata, says that while data analysis has been around for decades, applying big data analytics brings speed and efficiency that was never previously possible.

“Irrespective of the size of the data being captured and stored, organisations can use big data analytics to work faster, and stay agile, delivering a competitive edge they didn't have before,” Gardner says.

He says there are three key areas of opportunity for organisations looking to embrace big data analytics:

1. Extending analytic capabilities There are many genres of analytics available that can help extend an organisation's analytical ability. Three of the most common are graph analysis, path analysis, and text analysis.

Graph analysis helps businesses to extend the breadth and coverage of existing product affinity analysis, by helping them quickly analyse all product combinations and see which products bridge different categories.

Path analysis involves analysing time-ordered sequences of actions and interactions leading to an ‘event of interest'.

Text analysis can be used complementary to graph and path analysis, to highlight customer sentiment and areas of on-site search for optimisation.

“An event of interest can be anything, like a purchase, application or account closure,” says Gardner.

“This analysis can show the common paths between channels which in turn can show a business which avenues are best for connecting with potential customers,” he explains.

2. Exploiting new data sources According to Gardner, new data sources are previously untapped sources, like a company's website or mobile application, that can be accessed to provide detailed data.

The data can be analysed to identify changes to optimise the experience. Gardner says this data already exists but many organisations are not capturing it at a granular level to support analytics.

“The key to using new data sources is to remove any technological or departmental silos,” he says.

“Data delivers the greatest insights when it is combined, i.e., combining point-of-sale (POS) data, with customer-specific information, social media entries and profiles, to develop a more holistic view of the customer and their expectations,” he explains.

“Armed with this information, businesses are more capable of delivering a differentiated, value-add experience.

3. Enabling the right-time actions “The value of analytics is only achieved by actions being taken,” Gardner says.

“The time-frame from insight to action can vary. The key is to ensure that the insight is available at the right time, like an individualised or company-specific offer when the customer next makes contact,” he explains.

“By using big data analytics to extend an organisation's analytic capabilities, find new data sources and take action on insights in the right time-frame, businesses can realise more areas of opportunity to develop their competitive edge.

Follow us on: