The explosion in digital data volumes caused by the increasing number of social and mobile platforms means that, for the first time, businesses have access to hugely valuable resources that bring to light information about their customers’ behaviour and preferences. In fact, IDC estimates that data volume is growing at 50% per year. This so-called ‘Big Data’ promises businesses a better understanding of how customers behave to help improve relationships and better serve their clients, but it is still in its infancy – there is a long way to go before businesses can truly harness the opportunities such information presents and use it effectively.
The data generated by social networks and mobile devices is either semi-structured or unstructured, and is therefore more complex to process than traditional non-digital data. This means that there is pressure to determine how best to store, process and make sense of this data, in order to fully capitalise on the opportunity. With more and more businesses striving to grow their social and mobile presence through blogs, tweets and m-commerce transactions, this vast bank of data is growing all the time.
What’s the attraction?
Social media analytics are particularly useful for retail brands. For example, by monitoring Facebook and Twitter pages, it is possible to glean some very valuable insight into their customers’ buying experience. Many customers feel uncomfortable making comments in person about a retailer, but are happy to do so via social media channels. Closely monitoring these conversations and proactively using social media as a tool to develop a meaningful dialogue with customers helps businesses to effectively resolve customer issues and improve customer services. Ultimately, it enhances the customer experience and the reputation of the brand.
In financial services, using big data analytics could bring about huge benefits by offering early-warning fraud detection and identity threat management systems, giving clients peace of mind and generating impressive ROI. Arguably, big data analytics even played a role in the outcome of the US election. By setting up a feedback system and testing scenarios on potential voters, Team Obama was able to collect valuable profiling data. His camp then used this intelligence to target potential voters with more personalised information and messages to help make a connection with the individuals – a tactic which other politicians could learn from.
While the benefits are clear, how can businesses make the most of this opportunity, incorporate it successfully and achieve return on investment from such initiatives?
Globalisation and the emergence of the latest communications technologies have increased the demand to make decisions more quickly and with greater precision. Data captured from social media and mobile devices is time-sensitive in terms of using it to inform efficient decision making. This means businesses need to capture quality insights from this data and analyse it as quickly as possible to remain competitive. Those that continue to use old data models will lose out to more nimble, technologically-savvy rivals, who are already harvesting valuable insight from these vast digital data pools.
Integrating multiple platforms
While there are tools and technologies available for analysing this data, they originate from several different fields (such as mathematics, economics, statistics and computer science), so any organisation looking to use them needs to adopt a flexible, multidisciplinary approach. Analysing big data can be extremely complex. For example, for any given process several different tools and technologies might need to be employed to address specific problems. A mix of platforms is therefore necessary when processing structured and semi-structured data and all of them need to be integrated with one another to ensure the process runs smoothly.
Selecting the right tools and talent
In order to take full advantage of big data and maximise the benefits, businesses must take into account the quantity of data that is to be analysed, as well as selecting the most suitable analytics tools for each project. In addition, it is important to manage specialist talent carefully, by selecting representatives from both IT and business departments, who understand how all the information comes together and who can validate key insights and use these to implement new changes accordingly. As big data calls for an approach that is similar to data mining, the employees involved should have a good technical understanding and apply similar principles such as hypothesis testing before scaling up the projects.
Getting the balance right
By effectively using big data to reveal hidden business secrets ranging from customer preferences to performance drivers, the opportunities that big data presents are numerous. If used properly, big data can help senior leaders make strategic decisions based on customer-driven facts rather than gut instincts.
Ultimately, the ability for any organisation to thrive in the marketplace depends on its ability to adapt and exploit business and social data assets, and those that seize the opportunity that big data offers will stay ahead of the competition.