Defining Data Normalization for Fleet Telematics

May 15, 2020

Fleets have long carried a reputation for slow integration of technology, but things are quickly beginning to change. With the implementation of the ELD mandate, fleets must add electronic logging devices for recording HOS (hours of service) in each vehicle. Fleet technology providers saw the opportunity to provide fleets with additional benefits through telematics and predictive maintenance. To reap all the benefits, fleets need the ability to analyze massive amounts of data—this is where data normalization comes in.

What is Data Normalization?

Finding a data normalization definition is fairly easy, but it may sound confusing at first. It boils down to this: Data normalization is a technique for organizing and decoding data as part of machine learning. Essentially, data normalization reorganizes databases to eliminate redundancy and group similar data. This occurs by changing the data sets to a common scale without distorting range values. However, this isn’t always required for machine learning; it’s only required when features have dissimilar ranges.

Reorganizing the database allows you to properly utilize the data within it, for queries and analysis. This is essential for fleets since data is the key to optimizing fleets both large and small today.

The Benefits and Importance of Data Normalization

Fleet management is a complicated and time-consuming process, but data helps managers optimize their fleets with ease. Without data normalization, it’s impossible to sort this data into something usable. Data would need to be collected from various sources, which may be limited or formatted differently. Beyond simplification of data, there are other benefits of data normalization.

Predictive Maintenance

One of the largest benefits of data normalization is helping to decode diagnostic trouble codes (DTC). This is vital since every manufacturer, and even every model, has its own set of codes. There are standardized codes, but these codes are relatively few considering that each vehicle model has its own unique codes. There’s even an evolution of codes within these protocols. Without a way to translate these codes, fleets are left in the dark on vehicle maintenance issues. But it’s more comprehensive than just decoding DTC. Data normalization in fleet telematics provides the ability to predict certain failures. This will keep you steps ahead of even preventative maintenance, in which you anticipate breakdowns and make replacements on a regular schedule. With predictive maintenance, you can proactively fix issues even before the dashboard light comes on.

There is more to predictive maintenance with data normalization. The more data that’s available for the system, the better the system works. When your data set is larger, you’re going to get more accurate predictions and patterns are more easily identified. In conjunction with fleet maintenance software, you’ll receive alerts for the increased probability of failure along with the VIN, DTC, description, performance variables, and other important data. This helps you take action that improves the efficiency and safety of your fleet.

Reporting & Alerts

Data normalization also helps with reporting and setting up alerts by standardizing the language between each vehicle. In other words, a fleet manager is able to set up alerts or protocols for speeding and have it apply to all vehicles at once. There’s no need to create separate rules for each vehicle.

Sales

Sales has always been about the numbers, but those numbers are far easier to acquire today—along with dozens of others that were innumerable prior. Along with this, sales has always pulled data from multiple sources. Data normalization makes it possible to pool all of that data into one centralized database, then groups similar data and eliminates redundancies. While this sounds beneficial enough, there’s more to it. It’s best understood with the following data normalization example:

Say your fleet uses several software-as-a-service (SaaS) applications. You may regularly export data logs to your own centralized database and then spend time organizing this data. Data normalization would filter out this data as it comes in. It would eliminate any repeating data and translate the logs into the same readable language. This saves you hours and perhaps even weeks of data reorganization.

This helps your marketing team as well, since the software segment leads. It greatly simplifies the process of reaching and quantifying leads. But it also makes segmentation and lead scoring far easier.

Consistency

When information is stored in one place, it automatically reduces the risk of error and simplifies business. But with the normalization of data, this centralized data is also consistent. Errors, redundancies, and inconsistent language are all expunged from the database. This leaves your data clean and easy to read and analyze.

Telematics and Data Normalization

Telematics are a cloud-based technology, rising in the age of cloud-based systems. This increasing ability to share data over the cloud drastically alters business in many ways. For one, it eliminates back end management of data and administrative paperwork. Managers get real-time data directly from the source, and that data pours directly into a centralized database. But having the data isn’t enough, unless there is data normalization. Without it, the data is essentially useless. You have to be able to read it quickly in order to make real-time decisions.

Telematics offers remote diagnostics, safety systems, driver behavior, and more. It creates reports from this data and trends historical data to predict failures and inefficiencies. It then uses algorithms to calculate corrections, also known as optimization. This includes optimized routing, driver training, maintenance scheduling, and more.

Why is this a benefit? Well, consider risky behavior like speeding. In order to determine how much of a problem it is, a fleet manager would have to pour over hard data, video evidence, and more from multiple sources. Telematics data with data normalization can tell you in real-time when speeding occurs, how frequently, with which vehicles and which drivers, and how it compares to others. It can relate this data to mechanical data, accident data, and inspections to calculate risk. In other words, you spend less time trying to figure out the extent of one issue and more time making decisions that affect the success of your business.


Azuga fleet telematics and GPS tracking gathers data from multiple areas of your fleet, compiles it into one central database, and translates it into easy to read reports. Learn more about what telematics can do for your business, at Azuga.

Explore fleet tracking blog posts by category.

Safety

Accountability

Efficiency

Reporting

Rewards

Defining Data Normalization for Fleet Telematics

May 15, 2020

Fleets have long carried a reputation for slow integration of technology, but things are quickly beginning to change. With the implementation of the ELD mandate, fleets must add electronic logging devices for recording HOS (hours of service) in each vehicle. Fleet technology providers saw the opportunity to provide fleets with additional benefits through telematics and predictive maintenance. To reap all the benefits, fleets need the ability to analyze massive amounts of data—this is where data normalization comes in.

What is Data Normalization?

Finding a data normalization definition is fairly easy, but it may sound confusing at first. It boils down to this: Data normalization is a technique for organizing and decoding data as part of machine learning. Essentially, data normalization reorganizes databases to eliminate redundancy and group similar data. This occurs by changing the data sets to a common scale without distorting range values. However, this isn’t always required for machine learning; it’s only required when features have dissimilar ranges.

Reorganizing the database allows you to properly utilize the data within it, for queries and analysis. This is essential for fleets since data is the key to optimizing fleets both large and small today.

The Benefits and Importance of Data Normalization

Fleet management is a complicated and time-consuming process, but data helps managers optimize their fleets with ease. Without data normalization, it’s impossible to sort this data into something usable. Data would need to be collected from various sources, which may be limited or formatted differently. Beyond simplification of data, there are other benefits of data normalization.

Predictive Maintenance

One of the largest benefits of data normalization is helping to decode diagnostic trouble codes (DTC). This is vital since every manufacturer, and even every model, has its own set of codes. There are standardized codes, but these codes are relatively few considering that each vehicle model has its own unique codes. There’s even an evolution of codes within these protocols. Without a way to translate these codes, fleets are left in the dark on vehicle maintenance issues. But it’s more comprehensive than just decoding DTC. Data normalization in fleet telematics provides the ability to predict certain failures. This will keep you steps ahead of even preventative maintenance, in which you anticipate breakdowns and make replacements on a regular schedule. With predictive maintenance, you can proactively fix issues even before the dashboard light comes on.

There is more to predictive maintenance with data normalization. The more data that’s available for the system, the better the system works. When your data set is larger, you’re going to get more accurate predictions and patterns are more easily identified. In conjunction with fleet maintenance software, you’ll receive alerts for the increased probability of failure along with the VIN, DTC, description, performance variables, and other important data. This helps you take action that improves the efficiency and safety of your fleet.

Reporting & Alerts

Data normalization also helps with reporting and setting up alerts by standardizing the language between each vehicle. In other words, a fleet manager is able to set up alerts or protocols for speeding and have it apply to all vehicles at once. There’s no need to create separate rules for each vehicle.

Sales

Sales has always been about the numbers, but those numbers are far easier to acquire today—along with dozens of others that were innumerable prior. Along with this, sales has always pulled data from multiple sources. Data normalization makes it possible to pool all of that data into one centralized database, then groups similar data and eliminates redundancies. While this sounds beneficial enough, there’s more to it. It’s best understood with the following data normalization example:

Say your fleet uses several software-as-a-service (SaaS) applications. You may regularly export data logs to your own centralized database and then spend time organizing this data. Data normalization would filter out this data as it comes in. It would eliminate any repeating data and translate the logs into the same readable language. This saves you hours and perhaps even weeks of data reorganization.

This helps your marketing team as well, since the software segment leads. It greatly simplifies the process of reaching and quantifying leads. But it also makes segmentation and lead scoring far easier.

Consistency

When information is stored in one place, it automatically reduces the risk of error and simplifies business. But with the normalization of data, this centralized data is also consistent. Errors, redundancies, and inconsistent language are all expunged from the database. This leaves your data clean and easy to read and analyze.

Telematics and Data Normalization

Telematics are a cloud-based technology, rising in the age of cloud-based systems. This increasing ability to share data over the cloud drastically alters business in many ways. For one, it eliminates back end management of data and administrative paperwork. Managers get real-time data directly from the source, and that data pours directly into a centralized database. But having the data isn’t enough, unless there is data normalization. Without it, the data is essentially useless. You have to be able to read it quickly in order to make real-time decisions.

Telematics offers remote diagnostics, safety systems, driver behavior, and more. It creates reports from this data and trends historical data to predict failures and inefficiencies. It then uses algorithms to calculate corrections, also known as optimization. This includes optimized routing, driver training, maintenance scheduling, and more.

Why is this a benefit? Well, consider risky behavior like speeding. In order to determine how much of a problem it is, a fleet manager would have to pour over hard data, video evidence, and more from multiple sources. Telematics data with data normalization can tell you in real-time when speeding occurs, how frequently, with which vehicles and which drivers, and how it compares to others. It can relate this data to mechanical data, accident data, and inspections to calculate risk. In other words, you spend less time trying to figure out the extent of one issue and more time making decisions that affect the success of your business.


Azuga fleet telematics and GPS tracking gathers data from multiple areas of your fleet, compiles it into one central database, and translates it into easy to read reports. Learn more about what telematics can do for your business, at Azuga.

Take a look at related posts.