Monetising big data

Local company, Tharollo believes its Advanced Customer Data Analytics (Acda) solution has the ability to monetise big data by turning analysis into competitive advantage.

In an interview with ITWeb, Tharollo directors Glenda Wheeler and Carn Iverson revealed that Acda provides accurate insights into the customer base to guide business decisions that will increase revenue and profitability.

Using machine-learning algorithms and self-organising maps, Acda applies up to 50 statistical dimensions to generate reality-based insights into five customer-centric categories - revenue and profitability, behaviour, life cycle, migration and churn.

"By combining these models with our long-established project management and business analytical skills and by partnering locally with firms like IBM for data technology, we have structured an inclusive, commercially-focused approach to leveraging big data," says Iverson.

"We believe such an approach is essential if organisations are to awaken the decision-making knowledge that is typically in deep hibernation within their dig data."

"Amid all the chatter about big data, one question stands out - how will it make money?" Wheeler asks. "One sure-fire way to do that is with advanced data analytics that produce reality-based, actionable insights into big customer bases. This is to inform the strategic decision makers about how best to boost revenue and profitability," she adds.

According to Iverson, deploying the right type of skills is the main challenge to deriving real business benefits from big data. He explains that, in terms of big customer data, organisations need skills that generate reality-based insights into their customer base in order to accurately inform the strategic decisions that will increase revenue and profitability.

To exploit such opportunities, he explains, the top three technical "must-have" skills are data extraction; modelling and analysis; and knowledge management.

"But these technical abilities must then be complemented by project and change management skills. It may be stating the obvious, but successful big data projects - those that are completed on time, on budget and meet their business objectives - need to be structured and implemented in the most efficient and effective manner," Iverson explains.

On the other hand, Wheeler says, that there is an acute skills shortage in all these areas - data extraction and modelling, data analysis, project management and change management - is a major barrier to initiating big data projects. In terms of the shortage of statistical and analytical skills, she notes, a McKinsey big data commentary suggests that the hunt for such talent is taking place in the world's hottest market for advanced skills.

Making the connection between analytic talent and project-delivery skills is a major challenge locally, Wheeler says. "Organisations might be lucky to employ analytical and statistical modelling experts, but do they possess similar talent among their business analysts (BAs) in order to accurately define business requirements?

"And are their project managers (PMs) sufficiently capable of managing a big data project that may span many operating divisions across the enterprise? The shortage of skilled and experienced PMs and BAs in South Africa reinforces the barriers to delivering successful big data projects."

In particular, Wheeler notes, BAs will have to add an understanding of statistical analysis to their portfolio of skills. They need to know what insights have to be extracted from big data and how best to present those insights in a way that will influence the efficiency and effectiveness of decision-making processes, she adds.

"If organisations wish to fully-leverage the insights derived from big data analytics, they need to combine best-practice methodologies with pragmatic experience in the areas of project and risk management, business and data analysis, business architecture and requirements engineering."

She believes that the methodology applied to a big data project must reduce risk through a series of phases that are time-boxed as well as cost-boxed. "It should deliver rapid, actionable outcomes through accelerated achievement of business benefits. It must also ensure that milestones are always met within pre-defined timescales and budgets."

Wheeler points out that it's important for South African organisations to recognise that combined with advances in data processing, advanced data analytics has unleashed the latent knowledge that exists within vast data sets.

"In comparison to just five years ago, it is now cost effective to interrogate massive amounts of data very quickly and in a variety of ways in order to inform strategic decision-making. This can be summarised as the Four V's of big data - volume, velocity, variety and value."

Meanwhile, Iverson is of the view that advanced analytics illuminates all the realities within entire data sets.

"It certainly enables a much higher degree of customer-centricity because it reveals the mathematical realities in critical areas such as revenue and profitability, customer behaviour and life cycle, product migration and as a predictor of churn. Those insights are really at the nub of a focus on customers and they enlighten decisions regarding core functions such as product development and bundling, targeted-marketing activities and investment in infrastructure.

"In short, big data can consistently provide an organisation with actionable business intelligence. And that's a business advantage that should be leveraged by any organisation with a large customer base," Iverson concludes.