Value Creation from Big Data Analytics

According to McKinsey, Big Data Analytics can create value for the customers and the organizations in five ways (Manyika et al. 2011):

  • • Can increase transparency, making data more easily accessible to relevant stakeholders;
  • • Create and store more transactional data in digital form. In this way, organizations can collect accurate, detailed performance data in real time or near real time. This would enable proof of concept (POC) to identify needs, improve performance, but especially be able to offer new products and services adding value to the customer;
  • • Can provide organizations the tools to improve customer segmentation and then better develop and tailor products, services, processes, and promotions to each specific segment (in the limit, to each specific customer, in a one-to-one relationship);
  • • Include advanced analytics to provide actionable customer insights that minimize risks and improve decision-making;
  • • Be useful for organizations looking to create new business models and improve products/services, processes, organizations, and business models.

Harnessing and Harvesting Big Data Analytics for Digital Financial Services

Big Data Analytics platforms do not replace existing traditional data management and analytics platforms. They simply complement, extend, and improve upon existing environments and capabilities. Big Data Analytics consists of two processes: harnessing, which involves the collection, extraction, transformation, loading, administration, and management of Big Data Analytics; and harvesting, which involves the skills and solutions required to apply science to the data in order to derive actionable and meaningful insights from this to drive actions.

The harvesting and harnessing processes are complementary. They are two sides of a Big Data Analytics initiative (Hussain and Prieto 2016).

Harnessing Big Data Analytics

At the most basic level, the harnessing process consists of

  • • the collection of data;
  • • the extraction, transformation, and loading of data;
  • • the management of data; and
  • • the setting up of an ecosystem that can not only create Big Data Analytics but sustain it as well.

In the past, the data harnessing process was much easier than it is

today. The benefits of using these data were more limited. Today, the

complexity arises from

  • • a combination of additional sources of data, such as social media;
  • • the complex technology that exists today to give financial institutions access to those data as well as the ability to analyze them;
  • • the diversity of data. Gartner estimates that between 80% and 90% of all data produced today are unstructured.[1] Today, financial institutions can tap into a treasure trove of unstructured data of all varieties: text, audio, video, adjustor notes, click streams, and log files, for instance, and combine them with other structured types, such as currency exchanges, stock exchange performances, demographics and geographic data, and so on.

  • [1] Lohit, N, (2013), Big data, Bigger Facts, July 5., accessed 04 October 2013.
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