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Big payoffs seen in using Big Data

As the volume of data explodes, businesses need to assign serious resources to make sense and gain value out of an asset that they are building every day.

Dr. György Bőgel: Accumulated data is an asset. (Photo: Mátyás Pődör)

Executive, financial and information officers at the ‘C-suite’ level are soon to get a new companion. Apart from CEOs, CFOs and CIOs, recruitment firms now need to prepare for a constant headhunt for CDOs, Chief Data Officers. Since up to 90% of all data in the world was created in the past two years, it is little wonder that whilst the CDO position virtually didn’t exist back in 2011, by 2015, 9% of corporations employ one, according to a new study by the Economist Intelligence Unit (EIU).

Data strategy has become a top corporate priority and now there is a clear correlation between good data management and financial success. But companies’ attitudes have also changed towards data. They are less focused on its quantity or the speed of its accessibility. The value it can provide for their business counts a lot more now.

As an important center for computer science, Hungary has its share of Big Data experts, and more companies here are understanding the value of their own troves of information. They realize that the reams of data they collect can be analyzed in different ways to understand their markets and their customers – and to gain other types of knowledge that can serve their businesses.

“Company executives must realize that accumulated data makes up an integral part of their assets,” Dr. György Bőgel, professor of management at CEU Business School tells the Budapest Business Journal. “It needs to be looked after, somebody must be put in charge of it and an expected yield needs to be assigned to it. You need to know where to find it, what to use it for, what answers it may give and who can provide expert assistance in relation to it. No smart enterprise can exist without it today.”

As Bőgel points out, senior managers are generalists and they are supposed to have some basic general knowledge about every area the company deals with. “But I’m afraid many CEOs lack this knowledge,” he observes.

According to IBM, around 2.5 quintillion bytes of data is created worldwide every single day, which is equivalent to a storage space of 2.5 million hard discs of one TB each. “The volume of data is ballooning. There is enough processing capacity around; data scientists, however, are a rare breed,” Bőgel says. In fact, even today relatively few businesses rely on Big Data solutions and tools, and this also applies to countries more developed than Hungary.

On the other hand, Big Data provides an opportunity to build smart systems such as cities, health care, agriculture and so on. Building them requires well-defined steps: Problems must be translated into data, which must then be stored, transmitted and analyzed. In the end, recommended actions are worked out and implemented.

“Elements of such Big Data ecosystems, as I call them, like companies, experts or schools, deal with one or several of those activities in one or more industries,” explains Bőgel, who has written several books on the subject. “The ecosystem is functioning if all of its elements exist, they are all highly developed and smooth communication prevails between them.”

A message made just for you

Using Big Data in everyday marketing can be particularly challenging and, as Tímea Kádár (pictured), head of the online marketing department at Aegon Insurance notes, it helps to have a good data scientist on board. “He or she keeps an eye on the targets first, on what we actually try to get out of something. Then the way to get there must be planned. Data accumulation cannot be an end in itself,” she says.

Kádár mentions a recent campaign where Aegon surveyed people’s perception of having private pension plans. “Big Data can definitely help fine-tune such campaigns, especially when setting priorities,” she says. Where respondents labeled risk-aversion as critical, communication had to be made in such a way as to include the fact that Aegon’s pension insurance offered guaranteed yield. Different messages were drafted for people with different saving patterns, risk-taking attitudes or for those who would primarily expect the state to provide pensions. “This is how you can draw up main target groups and address everybody with the argument that is most important to them. How much more effective is that than simply repeating three slogans?”

E-commerce expert Csaba Zajdó (pictured) confirms that data-centric, real time personalizing methods are becoming widespread in the case of webshops. “Nearly all of them are based on the analysis of visitors’ behavior and they try to draw conclusions from past preferences, for example what products they have viewed and bought before, or from real-time behavior, like how much time they spend on a given page or how far they scroll down it.”

There is a wide range of techniques to choose from when it comes to tailoring the buying process. “The so-called recommending systems attempt to guess buyers’ taste and recommend them products that they are likely to feel positive about. The intention to leave a webshop can be projected with software such as the one our startup, OptiMonk uses that detects real-time behavior by mouse movements. But personalization based on location or weather is nothing new, either. In the latter case, different offers are shown if the sun is shining or if it is raining,” Zajdó explains.

The biggest obstacle is represented by the lack of data. For webshops with small traffic flows or for new customers, recommendations cannot give exact predictions. “The more data is available, the better projections are provided by this kind of software,” Zajdó highlights.

As Professor Bőgel notes, the opportunities inherent in available data are tempting, but enterprises must ask themselves certain ‘how-to’ questions. “And Big Data can give excellent answers to questions such as how to make e-commerce more successful, cut maintenance costs, streamline the supply chain or where product innovation is needed. It would be a shame to leave Big Data unused. But we are only at a learning stage.”