Even if the term “big data” is overused to encompass more about the digital world than it should, there is no denying it’s relevance to today’s enterprises.
Companies have been forced to re-examine their data management strategies to make the most of the vast troves of data – both structured and unstructured – at their disposal.
Many have implemented data infrastructure solutions to process the many terabytes of data that now course through multiple channels. This approach is necessary as it is clear that within these vast troves of data from connected digital devices are invaluable insights to drive marketing, pricing and other key business decisions. In short, no company can afford to ignore the potential of big data, and how that data is processed and analyzed can be a strategic advantage for those that do it well.
Some of the trends and strategies that we have seen unfold over the last few years are likely to strengthen in the coming year. These include:
- The drive to digitize: Digitization lies at the core of business data architecture today. Without complete digitization, companies will experience gaps in different parts of their frameworks and sub-optimal results from data analytics. Companies that have been seeking to go completely digital when it comes to their processes will be more motivated than ever to go after this goal.
- Mapping internal and external sources: Recently, the biggest shift in business mindset with regard to data lies in the recognition by companies that data from internal systems – ERP, CRM, and more – is not enough. Companies seeking insights on a wide range of business and market variables have to look beyond data generated by internal processes and transactions. An effective data management strategy needs to blend internal data with those from external sources such as social media and syndicated channels. And since the data from these sources comes in variable formats – text, numerical, graphical – companies have to have a flexible and versatile framework to accommodate and process these.
- Juxtaposing data insights with industry knowledge: In focusing extensively on data solutions and the insights they yield, companies and their leaders run the risk of ignoring their own intuition and knowledge regarding the business and the industry. Data analysis without the right contextual considerations can result in flawed numbers or insights. Most analytics models are less than perfect, meaning companies must rely on experienced analysts and workers to expose potential issues with resulting data.
- Growing demand for visualization: Companies are experiencing a daily deluge of data that needs to be processed immediately for it to be usable. In this environment, end-users need access to instant and real-time analytics but in an easily interpreted package. In this respect, data visualization is gaining ground within enterprises that seek to broaden consumption of analytics and assist users with making insights relevant to their roles.
- Driving ROI from analytics in every part of the business: With IT still trying to shift from a cost center to a business driver, CIOs are under pressure to find ROI in everything. Yet one of the frustrations for IT is the lack of scale—there are too many requests for data analysts to handle in-house, and they only keep increasing. Analytics platforms for marketing can help a company reduce strain on IT and improve ROI from its analytics tools because they don’t need to be pushed out through IT, and line-of-business workers and managers can begin working with information to improve ROI in every department, including marketing and sales.
Data analytics is an industry agnostic function but the need for it is most evident in the e-commerce space where online marketers can leverage the results of click-through analyses and similar activities to streamline their retailing strategies. In a broader sense, however, data analytics is relevant for any company with a strong digital footprint and keen customer focus. Financial firms and banks can leverage it to tighten their risk management and fraud prevention measures, while utility companies can use their vast quantities of meter usage data to develop variable pricing plans, among other things.
In the B2C space, data analytics enables marketers to enhance brand perception and customer loyalty in a number of ways. It allows them to more clearly understand the attitudes, motivation and challenges of consumers in order to craft better shopping experiences for them. Call centers and other offline channels can utilize advanced analytics in order to provide enhanced customer support or leverage opportunities for cross-selling and upselling. An integrated model of brand loyalty demands that it be measured, not just in terms of customer behavior (repeat visits and purchase) but also in terms of attitudes towards the brand. Analytics can help businesses on both counts.
Similarly, on the B2B front, data analytics is a means to improved customer retention. By segmenting their customer base, these businesses can develop distinct strategies for high value customers. Early identification of churn risk among critical customers allows companies to step in with pre-emptive attention and support.
Clearly, the possibilities are endless for enterprises seeking to transform themselves digitally and take charge of their data. At the outset, this is a process that involves investment in the right data infrastructure and technology as well as a partner who can provide the analytical capabilities and tools to cut across a multivariate data framework spanning internal systems, social media, mobile platforms and more. But, in the long run, these investments will pay off in the form of ongoing insights that are timely, accurate, and actionable.