Avoid big problems with Big Data in four smart steps
It’s a fact of business today that you need to rely on Big Data to run an energy-focused company as efficiently as possible. According to a December 2016 report by McKinsey & Company, Big Data’s potential keeps growing, and companies must integrate an effective analytics strategy to their corporate vision to make better and faster decisions.
Even when just focusing on one part of your company’s operation, such as your company’s fleet of vehicles, data can be overwhelming. In a given year, a fleet of just 300 vehicles can generate up to 1,200 transactional data points from maintenance alone with another 30,000 from fuel transactions and a massive 15 million from telematics.
The trick is to not get lost in the numbers.
In the fleet industry as well as the energy industry, more companies are entering the Big Data game in an effort to yield valuable insights about how their operation flows and what they can change to make it more efficient.
Here are four smart steps for creating a tactical plan to harness the power of Big Data.
- Define clear business objectives - As your company prepares to begin collecting and analyzing data, you must first define clear business objectives. What are your goals and what parts of your operation should you monitor to help you achieve those goals? Focusing on collecting the right data will allow you to make better and faster decisions that align with your strategic vision. Also consider the quality of the data. Many companies struggle with their ability to achieve their goals due to poor data quality.
- Set realistic, achievable goals - From the outset, make sure you set achievable goals. Organizations see great potential in the data and want to realize the full benefit of their investment. Ultimately, success hinges on the ability to evolve your Big Data strategy and not bite off more than you can chew when you are just getting started.
- Generate clear and specific reasons why higher costs are occurring - Why is fuel spend increasing? Why are maintenance costs higher in one division than another? Why should drivers stick to the assigned preventive maintenance schedule? As Big Data has evolved, fleets have shifted from trying to find meaning in an ocean of information to vehicle-focused data. The next generation of fleet management software is successfully deploying expertly designed statistical analyses that go beyond Big Data reports and are able to generate clear and specific reasons why higher costs are occurring. Today’s software makes it easier than ever to identify the outliers in terms of cost and speed up the ability to take corrective action.
- Determine what will likely happen next - Even more impactful than its ability to provide accurate insight, technology can make data genuinely impactful through predictive analysis – the ability to determine what will likely happen next based on the past. Predictive analysis solutions are empowering companies to do deep dives into their data easily, analyzing current maintenance data along with the vehicle’s history to determine how likely a vehicle is to fail. This opportunity to take the analysis a step further allows companies to more accurately predict and manage future costs. Companies are starting to use predictive data to inform which vehicles they purchase and when to replace older vehicles before maintenance costs grow.
Big Data can produce a potentially overwhelming amount of information that generalize what is going on in a fleet, but that usually results in more questions and more headaches. But by developing an effective analytics strategy, establishing goals, taking quick action and managing outliers, a fleet can quickly transform into an efficient and less costly operation.
Don Woods is the director of information technology at ARI in Mount Laurel, New Jersey, the world’s largest family-owned fleet management company, managing nearly 1.5 million vehicles in North America, the UK and Europe. Visit www.arifleet.com.
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