How Can Banks Derive Value from Analytics in a Mobile-First World?
Due to the sheer number of mobile devices in use, big data can tap mobile data analytics to better understand trends across vast populations and sub-segments of users. When analyzed effectively, this data can help banks gain access to businesscritical ins
Rapidly evolving technology has led to far reaching consequences for the world of business and the banking sector is no exception. Due to change in customer usage patterns and operations cost, even banks have started discouraging in- person interactions. The dawn of the Internet gave rise to net banking which made life convenient. Today, the arrival of sophisticated smartphones in the market and the mobile banking capability has completely transformed the way people are engaged in banking transactions.
The conceptualization of mobile banking started when banks adjusted the net banking applications, originally designed for desktops/laptops to fit mobile screens. Some of the emerging economies like India have successfully skipped some stages of digital evolution and have straight away adopted mobile services.
Today’s banking web pages are sensitive to devices. Mobile apps developed by banks can be easily downloaded to ensure more responsive visualization experience for the user and they in turn provide valuable personal information about the user to banks.
Explosive data growth
In addition to offering users a great visual experience, smartphones also generate data through mobile apps. At present, there are over 2 bn smartphone users around the world. This number is going to increase over the next few years and so is the data generation. The use of social media also gives a great opportunity to consolidate the data with the existing information that is captured.
The volume of mobile data and the speed at which it is created is only going to increase with the global population growth, mobile device usage rate, and the use of social media. Behind those exceedingly large volumes of data is the real business value. The actual business insights could be derived through a proper analytics program that has the ability, direction, and capability to understand the business and the relevant information. The question is: How can you make sense of all this data in order to make it actionable, and avoid the challenges it presents?
Data Analytics and Insights
Collection of large datasets does not really mean better business insights. The collected data needs to be analyzed on various business models to derive optimal value. Often this leads to consider big data as ‘dream’ solution for all the needs. But does big data really create value or is it something that needs to be analyzed thoroughly before embarking on the journey? The ideal approach would be to identify the right use case for big data that can provide the required business insights. This approach would help banks to embark on the big data journey that help them achieve the business objectives and RoI.
Most large banks now have a wellorganized enterprise-wide data warehouse that integrates the existing banking applications to cater to the operational and analytical needs. However, with the introduction of unstructured data from apps, third-party data sources like weather, economic indicators and social media, it becomes increasingly difficult to utilize the existing technologies. The cost of storing and processing voluminous amount of structured, semi-structured, and unstructured data has also increased exponentially. The existing technologies may find it tough to keep up with the real-time streaming and analyses.
This is where technologies like big data can play a greater role in augmenting the existing enterprise data warehouse landscape to gain an in-depth understanding. With cloud services rapidly entering the market, companies can make use of services like Amazon Web Services, Microsoft Azure, etc, to minimize their initial investment to derive proof of value before embarking on a bigger exercise.
Due to the sheer number of mobile devices in use, big data can tap mobile data analytics to better understand trends across vast populations and subsegments of users. This will help improve engagement tactics and optimize the delivery of services. When analyzed effectively, this data can provide insights on user sentiment, behavior, and even physical movement patterns.
Mobile device data becomes particularly useful for analytics purposes when coupled with outside data sources, such as weather and economic data, which allows the correlation of macrolevel trends to targeted sub-segments of users.
Analyzing mobile device data is just a part of the equation, however big data practitioners should also leverage the mobile devices to deliver relevant products and services to users based on learnings from analysis of mobile device data, which should also include non-mobile device data sources for additional context. The analysis of behavioral patterns including search and location contexts require the use of big streaming technology to trigger appropriate actions in near-real time.
In the years to come, it would be interesting to see how the digital technologies would take over the way we think, act, and respond.
Identify the right use case for big data to embark on the big data journey that helps banks achieve the business objectives and RoI.