For decades businesses have been using data in some way or another to improve their operations. For instance, an IT software could support small enterprises in their customer-facing processes. Alternatively, large corporations may possess complex systems that monitor and detect any changes in consumer sentiment towards brands.
Recently, many industry leaders, including McKinsey, IBM and SAS among others have released relevant studies on big data. It transpires that they are using similar terminology to describe big data as a “situation where the volume, velocity and variety of data exceed an organisation’s ability to use that data for accurate and timely decision-making” (SAS). These providers of business intelligence solutions have developed technical approaches to storing and managing enormous volumes of new data.
The handling and untangling of such data requires advanced and unique storage, management, analysis and visualisation technologies. The terms of “big data” and “analytics” are increasingly being used to describe data sets and analytical techniques in applications ranging from sensor to social media. Usually, big data analytics are dependent on extensive storage capacity and quick processing power requiring a flexible grid that can be reconfigured for different needs. For instance, streaming analytics process big data in real time during events to improve their outcome.
Insightful data could easily be retrieved from the Web, social media content and video data among other content. Notwithstanding, such data could be presented in different forms; ranging from recorded vocal content (e.g. call centre voice data) or it can even be genomic and proteomic data that is derived from biological research and medicine.
Big data is often used to describe the latest advances in technologies and architectures. Nowadays, big data and marketing information systems predict customer purchase decisions. This data could indicate which products or services customers buy, where and what they eat, where and when they go on vacation, how much they buy, and the like.
Giant retailers such as Tesco or Sainsbury every single day receive long-range weather forecasts to work 8-10 days ahead. Evidently, the weather affects the shopping behaviour of customers. For example, hot and cold weather can lead to the sales of certain products. It may appear that weather forecasting dictates store placement, ordering and supply (and demand) logistics for supermarket chains. Other retailers like Walmart and Kohl’s also use big data to tailor product selections and determine the timing of price markdowns.
Shipping companies, like U.P.S. are mining data on truck delivery times and traffic patterns in order to fine-tune their routing. This way the business will become more efficient and incur less operational costs. Therefore, big data extracts value by capturing, discovering and analysing very large volumes of data in an economic and expeditious way. This has inevitably led to a significant reduction in the cost of keeping data.
Big data can also be linked with production applications and timely operational processes that enable continuous improvements. Credit card companies are a good illustration of this dynamic as direct marketing groups at credit card companies create models to select the most likely customer prospects from a large data warehouse. Previously, the process of data extraction, preparation and analysis took weeks to prepare and organise. Eventually, these companies realised that there was a quicker way to carry out the same task. In fact, they created a “ready-to-market” database and system that allowed their marketers to analyse, select and issue offers in a single day. Therefore, this case indicates that businesses became much more effective (and efficient) in their processes through iterations and monitoring of websites and call-centre activities. They could also make personalised offers to customers in milliseconds as they kept tracking responses over time.
Organisations are increasingly realising the utility of data that could bring value through continuous improvements in their operations. This contribution indicated that relevant data needs to be captured, filtered and analysed. Big data is already swamping traditional networks, storage arrays and relational database platforms. The increased pervasiveness of digital and mobile activity, particularly from e-commerce and social media is leading to the dissemination of meaningful data – that is being created each and every second. Successful, online businesses can gain a competitive advantage if they are capable of gathering and crunching data.