Data normalisation is being used more and more in the fleet management and risk management communities, in relation to big data.

Understanding what data normalisation is, knowing why it is important to today’s fleet operations, including risk management programs, gives fleet operators and fleet managers a real advantage in both valuing, and utilising the big data they are already gathering.

As a short answer, data normalisation helps fleet risk management by making all elements of the collected data comparable and consistent.​

With this, accurate insights on the where, what and who of a fleet’s risk trends can be gained, allowing fleet managers to address them through training and management resources.

On the other hand, without effective data normalisation, big data insights can be flawed, leading to the wasting of training and management resources by deploying them in the wrong places.

Big data and fleet management

The amount of data being produced, collected, and stored has been estimated as growing at an average rate of 40 to 60% a year.

This extends to a fleet’s vehicles and drivers.

As they become more and more connected, fleet managers work to use this data to better manage fleet operations and reduce associated risks. The benefits from this being, reduced incident rates and claims costs, through improved training effectiveness.

Of course, this data abundance is only set to grow.

In the last decade, we’ve seen the rise of telematics, dash cams, GPS devices, smartphone apps, and tablets in fleet management, as ways to deliver efficiencies in operations, improvements in safety, and better customer service.

In the coming decade, this trend will only increase with 5G, (semi) autonomous vehicles and the true, mobile, always-connected Internet of Things.

Add to these in-vehicle data points, the increasing amount of out of vehicle, employee data we now have in digital formats – such as reviews, training records, and compliance checks – and the effective use of big data is a big challenge.

The Joe Bloggs, Mr. Bloggs and J. Bloggs problem 

With big data, the integrity and ability to synchronise data across multiple and disparate data sources is the key to its use and value.

As a human, reading the above sub-heading, you would suspect that Joe Bloggs, Mr Bloggs, and J. Bloggs are all the same person, and therefore data attributed to one should be attributed to all.

Well, at least suspect enough to check into it.

For a machine that deduction is not as intuitive, but is crucially important if the full benefit of all the associated data is to be realised.

This is where data normalisation comes in. It addresses the challenge of data-sets becoming bigger and more diverse, and how to incorporate them into a platform for effective analysis.

If this challenge isn’t met, gaps in the data sets grow, which in turn leads to wrong insights and wasted actions.

All of which can be masked by the belief that as the data is so big and rich, the insights, conclusions and actions must be correct.

Meaning that what should be a virtuous circle can become a vicious circle.

Other big data issues for fleet management

In addition to this Joe Bloggs challenge, big data for fleet management has other challenges.

The complexity of a system of systems

Big data is driven by the growth in connected platforms, and the amalgamation of all the data they take in and pass on. A system of systems.

Understanding the nature of this system and its connections, so that when something is wrong it can be identified, isolated, and corrected is a skill in itself.

Of course, as the connected world grows, so does this system of systems.

The shortage of professional big data analysts

With this exponential rise in data, a huge demand has been created for data scientists and data analysts, to both work the data and, just importantly, to understand it.

Also, their skill set needs to cover communicating what they find to other parts of the business, such as fleet managers, in ways that can be understood.

This crucial capability is in short supply.

Uncertainty about the future elements of data management

A further challenge for fleet managers wanting to use big data insights is the changing nature of the technologies available.

Adopting the right tech for the job is a crucial decision in itself. It is also one that impacts the range of choices that can be made in the future, so it needs to be well thought out.

Data privacy and security

A concern in everything and anything to do with data is that of security and privacy.

By its use of disparate data sources, this is a vulnerable area for big data and therefore always needs to be a front of mind consideration for fleet operators.

Data normalisation – the solution to big data issues for fleet risk management

Big data is mostly what is called unstructured data. Organising it and turning it into a structured form that can be used for analysis and insight by fleet managers is what data normalisation does.

Data normalisation covers these five core areas:

Removal of duplicates. This cleans up the data, making it easier to analyse.  

Grouping. Making related data easier to access and use by having it in close proximity.

Resolve conflicts. Again, cleaning up the data and improving the insight from analysis of the data.

Formatting. Converting data into a format suitable for further processing and analysis.

Consolidation. Combining data into a much more organised structure, again making it easier to access and analyse.

An important point to note is that data normalisation is not a static, one-time activity. It is an ongoing, critical, process.

The benefits of data normalisation

A fleet risk management database should use data normalisation so that the data can be visualised and analysed.

Without it, a fleet operator can collect all the data it wants, but most of it will simply go unused, and not benefit the fleet’s operations.

Additionally, data normalisation means databases take up less space. Saving data warehousing costs and improving performance, as a database that isn’t clogged with irrelevant information means analysis can be actioned more quickly and efficiently.

Of course, the main reason to use the data is to look at how to improve fleet operations and safety. This can become a complex task, if the data has come from multiple sources, as cross-examining them can be challenging. Data normalisation makes that process easier allowing fleet managers to answer the questions they have, more quickly while knowing that the data they are using is accurate.

That is, data normalisation creates a virtuous circle.

Clara has data normalisation baked in

The challenges and benefits of data normalisation for fleet management are at the very heart of Clara, our risk management platform.

As the following graphic shows, Clara takes in data from any source, and through our expert and specialist data normalisation processes, makes the data consistent and comparable.

This delivers fleet managers time-critical alerts, for the rapid management of incidents and FNOL management.

Plus, the risk analysis and insights, in a simple single view, of their risk profiles for prioritising training and management resources to address medium-term issues.

Data normalisation as used by Clara, CMS' SaaS Platform for fleet risk management - Diagram showing how it works

If you’d like to find out more about Clara, and how it can help you with your fleet risk management programs, please call us on +44 (0)345 241 9449 or contact us here.