Mining data for intelligence: Analytics for success

Data is everywhere. In this connected world we live in, every action of ours is sure to create data. Data is generated by users on the Internet, by machinery connected to a company network, as well as by the interaction between businesses, partners and  other stakeholders over the Internet or directly.

As a result, analytics has become a high priority for companies. This explosion of data with its variety, velocity, and volume has also had a huge impact on business decision-making. The bigger problem is much of this data is unstructured and making good business decisions often requires analysis across multiple sources and types of data.

Business sectors that have a high amount of data generated from customer interaction, market activity and fluctuations, and inter-organisation communication are especially conscious of the virtual treasure that their data can uncover through analytics. A prime example of a sector that applies advanced analytics on their data is the banking, financial services and insurance (BFSI) sector.

More than half of midsize companies are planning to increase their IT budgets over the next 12 to 18 months, according to an IBM global study of more than 2,000 such companies in more than 20 countries. As a result, these companies are investing in a wide range of priorities, including analytics, cloud computing, collaboration, mobility and customer relationship solutions.

Banking on BFSI in

Sri Lanka

The BFSI sector in Sri Lanka is among the leaders in data generation and analytics, and, in the period of economic growth over the last two financial years, has remained well-capitalised and on a growth curve. The sector has also made great strides towards offering a more inclusive financial offering by widening the reach of services through additional branches.

In Sri Lanka, this sector is at the forefront of effective technology adoption, due to competitive pressure, regulatory requirements, and the creation and management of huge quantities of data. This need to draw conclusions from large data sets means that an organisation in the BFSI sector has to have an effective analytics mechanism in place, which necessarily means an IT infrastructure that is capable of storing, analysing, and managing this data.

Dealing with data

Connectivity is at the heart of analytics, and while most businesses are connected to the extent that the data is transferred between units efficiently, the need for analytics means that this data transfer has to also become effective.

There is the need to automate the transfer of data so that relevant subsets are not only transferred to the correct recipient, but that the whole data set is analysed to identify trends, pressure points, and growth areas.

Until recently, analytics was seen as a priority of large enterprises, which, by their very nature, generate huge amounts of data internally. It was not seen as a priority for medium and small companies, since the amount of data generated or dealt with typically would not justify a full-fledged effort in data analytics.

With technological advancements like cloud storage and computing removing the need for high investments in physical IT infrastructure, businesses of all sizes can run analytics to uncover intelligence without the need for dedicated software, hardware or talent. The subscription-based access to services on the cloud is especially beneficial to the smaller players in the BFSI space, who can still unlock all the competitive and service-oriented benefits of analytics. The advances in security for cloud-stored data have also ensured that this data is not threatened.

Big data with big partners

Organisations in the BFSI sector in Sri Lanka have the benefit of being able to talk to globally-experienced big data analytics experts like IBM, who can hand-hold them through the service and infrastructure part of the roll-out.

The combination of the data deluge and clever software algorithms opens the door to new business opportunities. Today, even small companies are armed with the software and hardware platforms that can efficiently and effectively perform these three types of analytics on enormous volumes of data – Big Data. Big Data is defined by the four Vs: Volume (terabytes, petabytes, or more), Velocity (streaming data), Variety (structured variables in a database, versus unstructured text, voice, or video), and Veracity (the degree to which data is accurate and can be trusted).

With the explosion of unstructured data on social media, companies are rushing to analyse this type of Big Data to better understand customers’ views, preferences, and behaviours.

(The writer is the Country General Manager for IBM in Sri Lanka.)