It’s no surprise to anyone that the amount of data generated on a daily basis is staggering. In fact, the latest statistic says that 90 percent of the world’s data has been generated in just the past two years. From customer transactions, Web-browsing data trails, social network posts and, increasingly, machine-embedded sensors, it can be absolutely overwhelming. And this is occurring on a global level across all types of industries.
While North America is surging ahead in terms of current big data investment, Europe, the Middle East and Africa aren’t too far behind followed by APAC and Latin America. Globally, Financial Services is taking the lead in adopting progressive big data analytics strategies, followed by Technology, Telecommunications and Retail.
Leading the pack in top five use cases for big data analytics is customer analytics with 48 percent of companies using big data to unlock insights from customer behavior data.
With big data you can combine structured and unstructured data and customer interaction data to generate the insights needed to drive customer acquisition and loyalty. For example, you can use insights about the customer acquisition journey to design campaigns that improve conversion rates. Or you can identify points of failure along the customer acquisition path – or identify the behavior of customers at risk of churn to proactively intervene and prevent losses. And you can better understand high-value customer behavior beyond profile segmentation (for example, what other companies they shop from, so you can make your advertisements even more targeted).
Companies are fast realizing benefits in other areas too, including operational analytics, which now constitutes 21 percent of use cases. Big data analytics allows you to quickly combine structured data such as CRM, ERP, mainframe, geo location and public data and combine them with unstructured data such as sensor and server data, machine and Web logs. And then, using the right analytical tools, you can use this data to detect outliers; run time series and root cause analyses; and parse, transform and visualize data. For example, you can use customer and device usage across networks to identify high-value usage. Or you can integrate and analyze historic machine data and failure patterns to predict and improve mean time-to-failure – or ERP purchase data and supplier data to optimize supply chain operations. And you can use sensor and machine data to identify and resolve network bottlenecks. The possibilities are endless
Big data also allows you to find new revenue streams with new data-driven products and services. By combining, integrating and analyzing all of your data – regardless of source, type, size, or format you can quickly and affordably scale to huge volumes of data and analyze them for insights. (Traditional EDWs are simply too slow and costly for most companies to impact product innovation.) At the same time, you can quickly run sophisticated analytics that can’t be performed using a typical EDW – for example, clustering, click path analysis, and advanced data mining.
What does this all mean? It means that globally, competition is at an all-time high. Companies are turning to big data strategies as a means to gain an edge over their competition. They are better understanding and reaching their customers, developing new revenue streams and dramatically improving operational efficiencies. In our new data economy, good business decisions are now data driven.
So what does this all look like? We developed an infographic to show how big data is fast becoming a global competitive weapon for enterprises.