How to Derive Real Business Value from Your Big Data Investment

Today, small and medium businesses have a lot to rejoice as big data technologies are within their reach, even for those with small budgets. Big data has many promises, one of the greatest being its ability to offer invaluable insights on customers’ behavior, their unique needs and what products and services they desire from companies, retailers, and many other institutions. Collecting customer information using big data is no longer a challenge. However, many businesses still struggle with analyzing this data to derive actionable insights from it. Even when their IT departments are finally able to decipher the analytics part, another challenge arises: converting the analytics into measurable business outcomes that will create value, and by extension, more revenue, as this is the ultimate goal for most businesses.big data

Based on findings from a recent study by Forrester Research, over 75 percent of enterprise architects have embraced big data, but less than 35 percent of them have succeeded in deriving actual business-worthy value from it.  When viewed from a single business perspective, it is easy to downplay such outcomes, but when it happens to many businesses, the economy is bound to feel the pinch. A recent report by PWC revealed that in 2015, the Australian economy lost $48 billion worth of business opportunities because many enterprises in the country were unable to leverage on the full capabilities of big data. This trend begs the big question: how can an enterprise derive real business value from its big data investment? Read further to learn some of practical strategies technology experts and successful business leaders have to offer.

  1. Mobility for Boosting Efficiency and Real-Time Relevance

In 2015, Dell conducted its second annual Global Technology Index (GTAI 2015) survey, which involved 2,900 IT and business leaders in medium enterprises (containing 100-4,999) employees. The study revealed that companies investing in these big four technologies-cloud, security, mobility, and big data-are experiencing revenue growth rates of up to 53 percent. Focusing on the latter two technologies of the four, Terry Kline, CIO of International Truck, the commercial truck brand for Navistar, notes that access to quality data in real-time boosts both speed and efficiency of business processes. In addition, nothing beats mobile devices in delivering highly accurate and quality data at the time and place it is needed.

Mobile devices are pivotal in deriving maximum and relevant insights from big data because of their dual capacity to collect valuable data and transmit it to the relevant connected systems, and deliver data to personnel, customers, and other concerned parties on the ground. One common and popular application of mobility in aiding big data is collection of precise location data. This is, perhaps, the greatest edge that mobile software development has over web platforms. If a business is able to effectively collect and transmit location data in real-time, it can use the same to tailor advertisements that are target-specific, and deliver them to the targets, in real-time. The Dell study further showed that businesses that have embraced mobility have increased overall efficiency by 39 percent, which includes a business process improvement of 21 percent, and another 21 percent reduction in paperwork.

  1. Embracing an Open Data Platform

According to Kline, adopting an open data architecture will enable many enterprises to analyze data from multiple BI systems, regardless of its owner or vendor. Embracing an open data platform brings out a clearer picture of the data being analyzed. It boosts data relevance. In early 2015, major big data industry players and a handful of telecommunication giants converged in a San Francisco conference, where they expressed their interest in creating an Open Data Platform (“ODP”). The proposed system will be built on the Apache Hadoop open source software and its goal is to boost the ability of businesses to create and implement data driven applications.

Ford, one of the largest carmakers in the world, has adopted a Smart Mobility plan that will help further the company’s goal of delivering “the next level in connectivity, mobility, autonomous vehicles, the customer experience and big data”. MoDe:Flex, one of the innovations based on this plan, is an eBike introduced by Ford in 2015. The smart bike uses the MoDe:Link app to connect to a rider’s smartphone. The app then collects a ton of real-time data regarding traffic congestions, the weather, parking costs, time, and public transportation. It also does route planning and collects health and fitness data from the rider. In other quarters, modern day automobiles are equipped with electronic systems that collect every sort of data from the vehicle itself and from the driver’s driving habits. While all these types of data go a long way in helping manufactures improve the driving experiences of their customers, the comprehensive view of driver and rider data is achieved through an open architecture system.

  1. Refining Prioritization through Predictive Analytics

When businesses succeed in integrating mobility, open architecture systems, and predictive analytics, seamlessly, they are able to derive even more insights from big data. Predictive analysis, when performed on the right data and at the right time, is a very powerful insight generator. The aviation industry probably offers the best demonstration of predictive analytics at work. American Airlines, for instance, discovered early enough (in the 1980s) that it was sitting on a treasure of historical customer data that it could use to predict what its customers may need in future.  About 30 years down the line, and armed with an arsenal of the latest big data technologies, the airline can perform the unimaginable with existing customer data.

It will feed all sorts of data-demographic data, geographic location data, travel and search preferences, customer loyalty, purchase history, travel schedules, accommodations preferences, to name a few-and feed them into a BI system that outputs highly prioritized products that address the unique needs of millions of customers using the airline, in milliseconds. Nowadays, all you need to do is perform a simple search on your OTA (Online Travel Agency) portal for a flight jetting into London from Texas on a Thursday night, and leaving London early Monday of the following week.

After clicking “search”, predictive analytics takes over. Your favorite airline will most likely offer you an exclusive offer, and provide suggestions for the optimal route for this particular trip. It will follow it up with suggestions on some hotel suites you would love because it already knows the kind of accommodation you prefer, and the places you would love to see in London. If an airline brand detects that, you are a new customer, what better way to win you over than by offering an attractive travel discount? Airlines and many other businesses are able to achieve such high levels of customer engagement and service prioritization by applying predictive analytics to both first, and third party data.


Terry Kline and many other leaders in different industries, have observed unique growth patterns in revenue, productivity and improved efficiency in those businesses that are able to combine mobility, open data platforms and predictive analytics. As these technologies are not necessarily new and unattainable, enterprises may only need to make slight adjustments in their approach to big data, and they can begin to convert it into measurable business outcomes, through these three strategies.

Post Comment