According to a recent IDC report, the global big data technology and service market will grow at a 26.4% compound annual rate, to exceed $41 billion by 2018. This unprecedented growth is driven by a number of different use cases, but some of the best ways to deploy big data are relevant across industries. Here are our top picks for businesses wishing to deploy big data in 2016.
- Market intelligence
Most executives have accepted that customer insights can â€“ and should â€“ be collected at every stage of the marketingâ€“sales funnel. Although the large quantity of structured and unstructured information isnâ€™t going to manage, analyse, and protect itself, the outcome will be well worth the effort. As Sashi Reddi, the Vice President of CSCâ€™s Dig Data and Analytics group, suggests: â€œMaybe the issue isnâ€™t whether we can afford to implement big data, but rather, whether we can afford not to.â€
- Predictive analytics
Today, itâ€™s hard to imagine life before Spotifyâ€™s discovery tool and Amazonâ€™s advanced recommendation engine. When it comes to predicting customer demand, big data plays an integral role. The one thing weâ€™ve learnt is that consumer behaviour is anything but straightforward, which is why best predictions are born from a combination of different data sources. As Dr. Rado Kotorov explains: “Combining data provides new context and new use cases for the data. For example, combining social media data with transactional data can provide insight into purchases and thus lead to product innovation.”
- Regression Analysis
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- Customer Loyalty
In the world of retail marketing, big data is most commonly used for building and managing loyalty programs. Although loyalty is often understood in terms of traditional plastic bonus cards, thereâ€™s much more to the story than that. As Dunnhumbyâ€™s Client Director Oliver Harrison predicts: â€œBe it an app on your phone, your payment card or by tapping your NFC enabled phone, the card based approach will inevitably be eroded and diluted by allowing customers more control of how they interact with retailers in a physical store.â€
- Operational efficiency
While most B2C companies have concentrated their big data efforts on customer-facing applications, internal processes can also benefit from the deployment of advanced analytics. Contrary to popular belief, implementing a big data solution doesnâ€™t have to be expensive â€“ or difficult. As EMCâ€™s Marketing Science Labâ€™s Director Michael Foley recalls: â€œWe set up EMCâ€™s Marketing Science Lab, plus our BigÂ Data Analytics system in less than 6 months. Customers love knowing that harnessing bigÂ data doesnâ€™t have to be a giant upheaval, or takeÂ forever.â€
- Decision management
In 2012, HBRâ€™s McAfee and Brynjolfsson stated that: â€œBecause of big data, managers can measure, and hence know, radically more about their businesses, and directly translate that knowledge into improved decision making and performance.â€ Although relevant technologies have come a long way in four years, the same logic still applies. With big data, executives are able to take guesswork out of their decision-making process, while increasing efficiency and improving the quality of decisions.
- Problem solving
One of the most interesting big data projects in recent history comes from EMC, whose data analysts have attempted to uncover the science behind one of the worldâ€™s fastest motorcycle racers, John McGuinness. This unconventional use case is, in fact, a great example of a solution-centric big data application that starts from a simple question, in this case: what makes John McGuiness so fast? The key to answering questions like this lies in the ability to identify, measure and analyse all relevant variables. Foley explains that: â€œThis is the first time in motorcycle history anyone has gathered data from man, machine and the environment â€“ and in the harshest of conditions â€“ to uncover the secret behind what makes one unique rider the fastest man on two wheels.â€ Similarly, big data can be harnessed to answer virtually any business-critical questions.
- Data visualisation
Award-winning author and technology expert Phil Simon suggests that: â€œA new, data-oriented mind-set is permeating the business worldâ€, and along with that, data analysis is rapidly becoming democratised. Soon, non-technical professionals will also have to generate and understand insights from big data, which is when intuitive data visualisation tools will come in handy.
- Internet of Things
While the adoption rates of connected devices are on the rise, itâ€™s safe to argue that there will be much more data to be collected and analysed in the near future. Smart home appliances, health tech, wearables and vehicles will soon be the norm, which means great things for further innovation. In the words of ZDNetâ€™s Teena Maddox: â€œAs the IoT spreads across almost all industries it will trigger a massive influx of big data and spawn new methods for harvesting, analysing and using this information.â€
- Big Data as a Service
Early last year, Forbes contributor Bernard Marr introduced the term â€œBig Data as a Serviceâ€ to describe the process of outsourcing a variety of big data functions to the cloud. He suggests that: â€œOn top of upfront costs, storing and managing large quantities of information requires an ongoing investment of time and resources. When you use BDaaS, all of the techy â€œnuts and boltsâ€ are, in theory, out of sight and out of mind, leaving you free to concentrate on business issues.â€