With many fleets today awash in data from a wide variety of sources, it can sometimes be overwhelming for shop personnel, line managers, and C-level executives alike to make sense of all that data. And fleet maintenance management systems data can come in many forms – both structured and unstructured – with data originating from a wide variety of sources.
It’s so important that fleet personnel can make sense of disparate data so it can be interpreted and converted into actionable information. In this column, the first of several, we’ll begin to review some of the key characteristics, challenges, and opportunities occurring in the areas of big data and data management, as well as in reporting, business intelligence, and analytics.
Data, Data Everywhere
In today’s complex fleet operations and management environment, data can originate from most anywhere – from back-office financial and human resources/human capital management systems, to routing and dispatch systems, to fleet maintenance management systems.
At the same time, such systems can have a mix of structured and unstructured data. While both categories create massive amounts of data (hence the term “big data”) it is particularly in the area of unstructured data, which is generally found in open-form text, where many data management challenges are occurring today.
Such input can come from sources as vehicle component information from manufacturers, vehicle condition report information provided by drivers, fuelers, and technicians, as well as findings from preventive and corrective maintenance actions.
Add to this information from repair orders that include reason for repair, work accomplished, direct and indirect labor distribution, parts inventory, and many other system inputs, and it’s easy to see why many fleet personnel are overwhelmed.
The Importance of Proper Data Management and Reporting Tools
Today’s fleets don’t just run on Number Two diesel, unleaded regular gas, or natural gas – they need clean and accurate data as well. The underlying information must be clean and de-duped (without duplicate and redundant data), and accurate, to provide visibility into the operation, and to keep everything running properly.
Proper data management procedures must be in place no matter the type of system used in a company’s IT or “information technology” stack. Those configurations can range from smaller, stand-alone PC-based systems, to larger client/server systems, with their database, application server, integration, operating system, and user interface (UI) layers, to large mainframe systems, to Software-as-a-Service (SaaS) and cloud systems.
Data (and, more accurately, information) must be readily accessible and provided in a form that can be easily analyzed, providing insight into truly actionable information. Such results can come in the form of static reports or business intelligence dashboards, which are typically based on organizations’ key performance indicators (KPIs). Finally, organizations are increasingly using analytics tools and newer data visualization solutions as a way for users to construct models via visual representations (such as charts, graphs, and other views) of the underlying data, and make use of this valuable information.
Making Use of All That Data
So, what should fleet management personnel do when assessing a fleet’s data strategy? The good news is that this journey needn’t be done in isolation – IT partners should be an integral part of any data management discussion – and many of the associated costs should be part of the enterprise IT overhead allocation. As those systems are typically shared, enterprise-level resources, it’s important to work with their IT partners to ensure that a common data structure is defined and being used across solutions.
These analyses need to occur no matter whether such data is housed – in part or integrated across the enterprise – in fixed asset, financials, human capital management, or ERP (Enterprise Resource Planning), EAM (Enterprise Asset Management) or CMMS (Computerized Maintenance Management) systems.
In many cases, this process also means referring to industry-defined data structures, such as those provided by ATA VMRS codes, which then can be used in conjunction with and OEM and component supplier information.
Proper data management and analysis are critically important in today’s increasingly sophisticated fleet operations environment, which can include elements of legacy mainframe, client/server, and/or PC-based systems, as well as on-demand managed services and cloud solutions. Also, an increasing number of reporting, business intelligence, and analytics solutions are now available that can be used by business users, and not just IT and quant staff, to conduct analyses and identify trends.
And today’s subscription-based software deployment models can also mean that the cost for some data visualization and desktop analytics solutions for business users can be quite reasonable -- and easy to use -- via a cloud deployment model.
Data – and more accurately information – is more important than ever, and essential to truly understand how well a fleet operation is performing. Understanding which areas are performing and which are underperforming are critical to a fleet’s efficiency and overall effectiveness. Those analyses can – and should -- help drive the planning and scheduling of needed tasks, ranging from preventive and corrective maintenance action, component and parts identification and sourcing, identifying equipment cost and performance trends, and input for improved spec’ing opportunities.