Big data ROI is a mark of maturity on a company’s big data journey. It happens when your big data projects morph from one-offs that are meant to satisfy a single business unit into something much larger.
Small big data project victories are noticed—and profitable. Visionary company leaders begin to extrapolate how big data could transform other business units or even their entire company. As investments grow larger, the big data ROI conversations and calculations start to happen. But how exactly do you measure your return?
ROI calculations typically describe hard benefits—profits, sales, or savings. After all, you must see financial benefits from any project you onboard. However, before those hard benefits can be realized, you must also consider the role of soft benefits, the nonfinancial or intangible benefits derived from your ROI journey.
When it comes to big data ROI, the skills your team gains from early projects are likely to be the first returns you realize. On the path to data maturity, gaining skills is a measure your company should take seriously. No matter the scenario or use case, having the skills to master big data tasks is a major step toward enabling your future ROI measures.
Through knowledge gains, your internal teams become experienced enough to take on data initiatives and run them themselves or in partnership with external resources. This drives internal productivity, which ultimately fuels the big data projects that deliver the hard ROI you’re looking for.
If you’re like most companies, you started with small-scale big data projects. These projects typically have low initial outlays and deliver quick ROI. For example, a company low on the data maturity scale may invest in Apache Hadoop or other big data infrastructure that makes the storage of large data sets easy or their enterprise data warehouse (EDW) more efficient. The storage capability and the efficiencies over other storage options make the ROI almost immediate.
However, as a company progresses along the data maturity scale, it often leads to projects that focus on analytics, data mining, or real-time data decisions. These require more of an investment, and calculating ROI becomes more difficult.
The number of companies reaching this level of investment is growing at a fast pace. The annual NewVantage Partners Big Data Executive Survey found that 26.8 percent of the companies surveyed expected to invest $50 million or more on big data projects in 2017. When the level of investment approaches millions of dollars, ROI measures become critical.
The NewVantage survey also revealed that the top two reasons firms invest in big data are to develop greater insights into their business and customers (37 percent) and to gain advantages of speed—faster time to answer, time to decision, and speed to market (29.7 percent).
The goal of reaching big data maturity is reshaping the internal decision-making culture at your business. Business decisions will be based on data, not hunches. Companies making this shift are more productive and measurably profitable in the long term versus competitors that don’t take advantage of data-based insights. Here are three companies on the road to ROI:
Rowan Companies provides offshore contract drilling services. A regulation set to take effect in 2019 requires real-time safety monitoring. With fleets spread across the globe, they needed a reliable way to secure this information. Their strategy centered on Internet of Things (IoT) tags. Rowan implemented open source and analytics platform solutions on its first drill-ship in less than 90 days, using 3,200 tags. Rowan plans to expand to 25 rigs within six months. When complete, the solution will use 10,000 tags and 150 kilobytes of bandwidth. The company can now remotely monitor critical conditions. With predictive analytics and maintenance forecasting, they expect to reduce downtime and alleviate future troubleshooting trips.
Centrica supplies energy services to 28 million customers in the UK, Ireland, and North America. They evaluated their infrastructure to rethink how they could increase customer satisfaction using smart meter data. They employed big data solutions to reshape their analysis of data. Through data aggregation, they now provide accurate smart energy reports to customers, giving them better understanding of their energy use, consumption peaks, and how their money is spent. Smart metering based on data analysis reshaped how Centrica monitors energy use. Data is easily collected, sorted, and analyzed every 30 minutes for the most reliable and accurate reporting.
Mercy is a U.S. healthcare system that runs 35 hospitals and 400 clinics. Using Epic, their data team made a big push for one patient, one record. No matter how a patient engaged with Mercy, they wanted their updated patient information in front of clinicians and staff. Based on the success of this initiative, they wanted to do more advanced analytics that integrated Epic data with external, third-party data sources.
Using an open source platform, Mercy was able to implement a way to do that in real time. Some of their researchers were working on a project in their on-premises database platforms. For a single query that analyzed a patient population of 19,000, the research team estimated a query runtime of two weeks. Shifting that data to the new, Hadoop-based platform, the query ran in half a day.
Big data is a journey that follows a unique path inside every company. While determining your big data ROI may initially be difficult, take comfort in knowing that it will deliver financial benefits in the long run.
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