Main Big Data technologies use cases

Within DataBench, use cases represent the link between technical solutions and business goals and help the collection of data for BDA exploitation typology – the main use-case types. In deliverable D2.2 we have identified 35 use cases within the DataBench survey to assess the adoption and maturity of the use cases across different industries and to have a pragmatic and realistic view of the footprint of BDA adoption. Some use cases are adopted across industries; others are shared and common only to some industries; some are industry specific. The use cases hereby defined pertain to storing, transforming, and analysing data and harnessing Big Data technologies as a way of enabling organisations to extract value from data to achieve the main business goals.

This figure shows the ranking of use cases in terms of absolute numbers of respondents of the survey carried out in D2.2. The shadow bars in the figure  show the size of the respondent sample for the specific use case – those currently using or evaluating each one. The top three use cases are common to all industries – risk exposure assessment, new product development, and price optimisation. However, looking at responses by industry, we can observe that some industries do not consider all three to be the top three use cases, as they prioritise other activities. But they became highly relevant when considering the total sample. Broadening the analysis to the top five use cases, a mix of internally and externally oriented use cases is presented. In the top five, we observe automated customer service as one of the most deployed use cases. When considering only use cases currently in use (the light blue bar), optimising offers (which involves understanding and targeting customers) is more relevant and falls in the top five. 

Taking a closer look to the top five use cases, we find common patterns across industries.

· Risk Exposure Assessment: This first use case is extremely relevant across all industries, especially for organisations in the process of evaluating current processes, services, and products, but also for those evaluating the introduction of new products/services. When business processes are under evaluation, risk exposure assessment can come into play, too, providing information on opportunities and risks related to new processes.

· New Product Development: New product (and service) development progresses with the adoption of Big Data technology because it helps organisations to (re)shape products (and services) according to customer needs and interests. This also links with product and offer personalisation/customisation.

· Price Optimisation: Product and service price optimisation is a complex mechanism that can be undertaken only once a BDA platform is in place and functions well, as price/offer optimisation involves profiling and targeting specific customer segments and tailoring offers and prices accordingly.

· Regulatory Intelligence: Big Data solutions and technologies are helpful in setting and managing regulatory compliance strategies and in building a regulatory-savvy yet data-centric company. A Big Data platform (or solution) helps an organisation capitalise on potential and real value by ensuring the use of data and adherence to next-generation regulatory compliance. Big Data technologies help businesses update and modernise their compliance processes, making them more precise and effective, follow regulatory changes easily (both domestically and internationally), and improve decision making processes.

· Automated Customer Service: In automating customer services, organisations can optimise responses to customers in terms of both timing – from call reception to handling and forecasting service completion – and cost. This process not only improves customer satisfaction; it also lowers call handling costs and human errors and their related costs.

Big Data is considered across all industries as a pivotal solution in digital transformation and the achievement of digital business objectives. The volume, variety, and velocity of data from multiple sources (both internal and external) is increasing, and the opportunity to exploit this data to gain a better understanding of current performances and areas of possible improvement is clear and valid.

The exploitation of Big Data is helpful for a large number of use cases, embracing both internal and external processes, such as optimising conversion rates, detecting and avoiding risks, streamlining operations, and monitoring customer behaviour, among others.

Enhancing decision-making processes is an ongoing effort for many organisations, and Big Data analytics can contribute to achieving business goals and profitable results. Big Data analytics solutions vary by vertical market; organisations in each sector implement Big Data solutions for purposes and business models specific to that sector.