JOHN OREDO delves into the various ways organisations can make use of big data in their possession and derive value from it.
According to Hal Varian, a Chief Economist at Google, to make money, an organisation has to predict two things – what is going to happen and what people think is going to happen. But the proliferation of huge data sets emanating from the ever increasing number of digital interactions will not only allow organisations to predict market behaviour through fore-casting but also to get real time data to describe current activities through now-casting. The last section of Varian’s statement regarding fore-casting and also now-casting what people think about an organisation and its products and services underscores the fact that organisations must be able to capture the conversations about it, its value proposition and delivery. With the emergence of digital ecosystems like social media, mobile telephony, CCTV records, governments and organisations are now able to integrate data from a variety of sources enabling them to know what you do, where you go, who your friends are and what your preferences are. All these data collected from disparate sources in the form of texts, audio, images and video that can be stored, retrieved, analysed and interpreted for the purposes of forecasting and now-casting is what is referred to as big data.
What is big data?
Big data can be understood in terms of a technology and an industry. Big data is seen as large quantities (volume) of data coming from different sources (variety) at very high speeds (velocity) with varying degrees of credibility and reliability (veracity). The real challenge for organisations today is not the availability of data but in deriving useful knowledge from the data. This need has spawned the big data industry. The big data industry is comprised of all those firms involved in the production, analysis and use of big data. The big data industry has a value chain which involves consumers whose information is captured through a myriad of digital devices and applications. The firms that receive this data may then pass it to data aggregators. The data aggregators, also known as data brokers will then consolidate and profile the data. This information in the hands of data aggregators may then be sold to government agencies, researchers, polling companies and even ad companies. But how then can organisations use the opportunities afforded by big data to generate value?
Making good of the data
The strategic potential of big data is not in its characteristics but the affordances of these characteristics. For any firm to remain afloat, it should have relevant data for sensing the business ecosystem for new business opportunities and also to seek data that is used as input to its strategy analysis and formulation. With big data, it is now clear that organisations will be relying on unstructured, haphazard, heterogeneous and trans-semiotic data for strategic information.
Big data has the potential to enable organisations to create new growth opportunities and entirely new categories of business models. In an article by Barbara Wixom and Jeanne Ross in the Janurary, 2017 issue of MITSloan Management Review, the authors suggest three ways in which organisations can monetize the data in their possession instead of hoarding it.
The first idea is that organisations can use the data they hold to improve their business processes and decisions. With data driven decisions, organisations are able to deliver better services and address customer demands effectively. For example, a supermarket can use the data captured through shopping smartcards to profile their customers in terms of their shopping habits. With these data, the supermarket can send customer specific promotional messages or understand what items should always be in stock at certain periods of the month.
Secondly, organisations can use data and analytics to provide rich information around their products and services- what the authors call wrapping information around products. Wrapping is a creative exercise through which companies find the challenges customers have in using their products and services and then use data and analytics to provide them with solutions. Users of credit cards are always worried about fraud. The banks have since wrapped information around this service by providing real time text message indicating when a transaction is performed on a card complete with details of the amount, vendor and a call service in case of doubt.
Lastly, organisations can make money by selling or even bartering the hordes of data in their repositories. This can be done by setting up a business unit that aggregates and classifies the data in a manner that it can be immediately consumed by those who need it in the new and existing markets. For many firms, big data is generating value both through the storage of information whose potential grows by virtue of its volume and from the insights attained by examining it.
Big data therefore adds the analytics dimension to an organisation’s capability. Big data analytics refer to the process of using various applications to examine large data sets to discover hidden patterns, market trends, unknown correlations, customer preferences and answers to any other strategy question a business may be interested in.
To derive value from big data may not be straightforward due to its unstructured nature and constant updatability. In order to monetize big data, organisations must update their legacy database systems that are largely premised on deliberately collected structured data. The organisations must also examine their absorptive capacity for big data analytics. Organisation planning to extract value from big data need to plan, set relevant objectives and build data analytics skills. Otherwise they will be shooting in the dark and hoping to hit the bull’s eye. Data analytics or data science is at the kernel of data revolution. It is data analytics that make information to “come alive”. Data scientists are supposed to marry the technical aspects of data with the ability to create business strategies. As the possibilities of big data continue to evolve, data science and the role of data scientist will play a major role in digital business strategies. In fact, an article by Davenport and Patil appearing in the October, 2012 issue of the Harvard Business review referred to data science as the sexiest job in the 21st century.
Dr. John Otieno Oredo is a lecturer and researcher in digital business strategies and holds a PhD in Strategic Information Systems.