Though the digital revolution came into light during the late 50s, its impact on our daily lives became more evident in the last decade or so. The main reason behind this is the extensive implementation of mobile devices and easier access to the Internet. This implementation has made the path clear for connected devices.

Nowadays, people use many devices on a daily basis and effortlessly change between them while doing different activities. With the above and several other factors like decreased attention span due to information overexposure and accessibility to multiple options to choose any product or service has given rise to a situation where effectively engaging and retaining a customer has become completely vital in making or breaking a business.

So How Would One Do The Right Decisions To Improve His Business?

The term ‘big data’ was first started by Roger Mougalas from O’Reilly Media back in 2005. It indicated to large sets of data which are virtually unfeasible to handle using conventional business intelligence technology. Today, it is supposed that 2.5 quintillion bytes of data are being produced every day and this number will increase more with continuously developing technologies and faster computing speeds.

In 2011 McKinsey published a report titled “Big Data: The next frontier for innovation, competition, and productivity” and the title alone specifies that data is definitely the new one. Countries from all over the world and companies of different sizes are vigorously using big data projects because all this data is capable to improve business operations.

It has been seen that big data analytics is attaining a lot of significance currently. Its amalgamation with businesses helps in adding importance at every probable stage of a sales/user journey because of the 360-degree insights which are able to derive from all the channels (mobile, web, physical locations, social media, etc.) and devices involved. These insights can be utilized to pull, remodel, or even bring innovative proposals that will recover and optimize business models, products, and services.

Big Data In Banking And Finance

Big data can be the key driver for improvement in the banking and finance sector and will assist banks to advance their performance. As per the most recent update (as of 2018) of the ‘Worldwide Semiannual Big Data and Analytics Spending Guide” from IDC, worldwide revenues for big data and business analytics (BDA) solutions is likely to reach $260 billion in 2022 with a compound annual growth rate of 11.9% over the 2017-2022 forecast period.

The banking, discrete manufacturing, process manufacturing, professional services, and federal/central government industries are making the largest venture in big data and business analytics solutions all through the forecast. This inclination confirms that business initiatives driven by actionable insights granted from big data analytics will get success.

Another McKinsey report called “Analytics in banking: Time to realize the value” emphasizes that in some regions, around 65 percent of customers now interact with their banks via multiple digital channels. This often gives rise to overlapping interactions with a particular customer across multiple channels and formats.

Banks now apprehend that it has become essential to bring consistent, relevant, and eventually, delightful banking experiences flawlessly all through numerous channels to hold and maintain customers. In this sector, big data analytics plays a big role in approving the customer’s movements and beliefs in real-time and sending customized offerings at all stages of the user journey.

The Efficient Aspects of Big Data

The 3 defining aspects of big data or more readily known as the 3Vs – the volume of data, variety in the types of data being collected, and velocity in which data is being processed. For banks, this implies that they can control the findings from big data analysis to study spending patterns and spot main channels on which transactions are taking place to appropriately cross-sell products and services to customers which are fragmented based on different user qualities. These findings could also be used for risk appraisal, observance and reporting, customer feedback management, and granting or refusing a loan among others.

IBM says that the current retail and institutional banking customers are more authorized than ever before. Their improved handiness to information indicates that they can judge products in few seconds and make decisions immediately, and mobile apps have fostered this process.

Analytics for app practice is also actively serving banks decide which aspects of their apps are most attractive for users, which bring improvements and updates to address the findings. Today, banks can increase their performance and develop customer preservation by leaps and bounds with bringing personalized, real-time, and value generating offerings

Lloyds and Pindrop – An Example of Technology Progression

For example, Lloyds Banking Group has partnered with a U.S. artificial intelligence (AI) start-up ‘Pindrop’ to use its technology to identify fake phone calls in 2016. Pindrop is a technology which takes into account large amounts of data to know the difference between a legitimate and fraudulent call. It also helps to identify several features of a voice from a phone call such as location, background noise, number history, etc., that can help in creating an audio fingerprint which consequently can be used to emphasize unusual activity and recognize the probable scam.

Now, it is apparent that big data analytics’ role in banks and other businesses will get advanced more with the progression of technology. A large amount of data being analyzed has the possibility to finally change the way we live and carry out our lives. Furthermore, big data analytics will provide a new step in ensuring truly personalized and safe banking experiences and operations.