More and more of us are ecoming familiar with the Big Data challenge. Digital technology has made it possible to collect more data than we ever previously imagined.
 - Photo courtesy of ranjithsiji via Pixabay.

More and more of us are ecoming familiar with the Big Data challenge. Digital technology has made it possible to collect more data than we ever previously imagined.

Photo courtesy of ranjithsiji via Pixabay.

In October, I was privileged to present Mark Leuenberger of Cox Enterprises with the 2018 Fleet Executive of the Year Award

Mark serves as the assistant VP in charge of the company’s supply chain services and procurement functions, and his distinguished accomplishment demonstrates his ability to deftly manage competing and complex priorities. These included guiding Cox’s continuing efforts to transition to more efficient gasoline, flex fuel, and hybrid vehicles; pushing through an initiative to find annualized cost savings of $3 million to $4 million in automotive repair operations; and much more.

Making the right decisions for these projects required Mark to gather and analyze a significant amount of data. It was essential, therefore, that the data be complete, accurate, up-to-date, and presented in a way to identify to actionable intelligence. In the era of “Big Data,” Mark’s challenges were no different than any other manager faced with a myriad of complex problems, competing priorities and limited resources. 

The Big Data Challenge 

More and more of us are ecoming familiar with the Big Data challenge. Digital technology has made it possible to collect more data than we ever previously imagined. What’s more, that data is now available at our fingertips in real time. Databases are expanding, and new databases are becoming available. Those are the upsides. 

The downside, of course, is that, unless we have analytical tools to help us understand the meaning of all that information, data overload may bring paralysis by analysis, lost opportunities and newfound obstacles. 

Enter data visualization: tools that provide easy-to-see depictions of real-time key performance metrics. Once the province of data scientists and engineers, companies now expect data to be analyzed throughout the organization. As a result, more managers are relying on a growing number of visualization models. 

In fact, the infographics firm, Ferdio, lists a compendium of over 100 visualization models. Google’s Data Studio allows users to independently create their own visualizations and business intelligence driven analytics without coding skills. 

Data Visualization Business Purposes 

But not all data visualization tools are created equal. For one thing, they must be designed for different users and purposes — strategic, analytical, tactical and operational. 

Strategic visualization tools are typically deployed in support of the C-suite and depict a company’s performance against long-term enterprise-wide strategic goals. Analytical models contain vast amounts of data created and used by IT analysts to provide support to executives. They supply a business with a comprehensive view of its historical data to identify trends and create predictions and targets, typically for use by middle management. 

Wysseier -

Wysseier

Tactical visualizations are used by middle management to analyze and monitor progress toward achieving near-term results, like sales, with breakdowns by key variables, including region, product or manager. They also allow for benchmark comparisons to competitors and more. 

Operational dashboards are useful for monitoring and managing operations that have a shorter time horizon. They are typically used at the department level, where subject-matter specialists reside. 

Fleet Safety Program Application 

Certainly, the ease of identifying exceptions to establish metrics or performance standards clarifies analytical priorities and speeds targeted remediation. A key feature of operational visualization models is the immediate identification of exceptions. This capability can provide focus for analysis and lead to more direct remedial action. 

At CEI, visualization of our DriverCare fleet driver and risk management data enhances the ability of our customers - safety, risk and fleet managers — to control the most comprehensive driver safety program down to the individual driver. 

Our performance reviews are also characterized by four other key visualization creation standards: 

  • They are customizable. Our customers expect to focus on the metrics that are most important to them, and we present such metrics based on their priorities. 
  • They are focused. Visualization models must be instantly understood and avoid clutter and distractions. 
  • They are drillable. A single view can point to a problem; however, drilling down into the details is critical to draw the most accurate conclusion. 
  • They point to action. The objective of any visualization is to quickly help a customer identify the best course of action. 

With today’s growing amount of both open and private data as well as more sophisticated analytics tools, more and more managers are driving their operations and businesses with Big Data and the visualization tools that expedite informed decision-making.

The CEI Group is the exclusive sponsor of the annual Fleet Executive of the Year award, presented by Fleet Financials magazine.

Editor's note: John Wysseier is the chief executive and president of The CEI Group, a business unit of Element Fleet Management.

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