Do you need quality data to ensure fleet success? | Utilimarc Fleet FYIs

Gretchen Reese (00:12):

Hey there. Welcome to the Fleet FYIs podcast, the weekly podcast by Utilimarc that reveals how you can make the most of your data for smarter fleet management. My name is Gretchen, and every week you'll hear from me, or some of the industry's finest in candid conversations that will not only shed some light on over two decades worth of fleet data insights, but also some of the industry's hottest talking points and key metric analysis with the aim to help you better understand your fleet from every angle.

(00:52):

But before we begin, if this is the first time you've heard our show, thanks for stopping by. I'm so glad you decided to come along for the ride with us. I've got a quick favor to ask you. Once you finish today's episode, if you could take a few minutes to leave us a review on your favorite podcasting platform, we would really appreciate it. Give us a rating, five stars, I hope, or tell us what you liked, or leave us a comment or a question about what you've heard in today's episode. If we haven't yet covered a topic that you're interested in hearing more about, let us know. We would be happy to go over it in detail in a later show.

(01:34):

Hello everyone, and welcome back to another episode of the Fleet FYIs podcast. Today we've got a data side trick topic on the books, and we're talking about quality data. I'm excited. Any good fleet manager will want the same of their data sets, to be reliable, understandable, and to provide a return for the company. If these three things aren't true, then what's the point of the data itself? I'm sure we've probably all asked ourselves that question at least once or twice. We want our data to work for us, not to confuse us more than perhaps it already does. Unfortunately, you still have a lot of fleets that are relying on inefficient or outdated systems, and they might not even realize it, which can be a bit challenging, especially as you're trying to modernize and integrate new strategies into your fleet management overall goals. In these cases, data is likely super unclear. It could be inaccurate and difficult to validate potentially, depending on different input times and input frequencies.

(02:36):

There's a lot of fluctuations here, but whilst enterprise resource planning and fleet management information systems are incredibly valuable for organizations and essential to running a fleet, these systems, a lot of times, don't play well with others. That can be quite challenging, creating a lot of silo data, which no one likes, but it tends to be enormous, especially in an industry like ours. What does this mean for fleets in general? I'm sure you're probably wondering that too. Essentially, what this means is that using disparate systems makes it nearly impossible to cross-reference data, and to get a full picture of fleet performance. Now, this results in errors that are easily slipping by, and fleet managers that ultimately work on data sets that they don't trust and can't work with. In these cases, fleet data systems end up a bit of a sunk cost, when they really could be an asset generating returns for the organization or the fleet department in general. Now, that seems a little peculiar, doesn't it? Let's dig in.

(03:49):

Here's the question. How do we actually ensure quality data? Because that can be a bit of a complication, especially if these systems don't actually work well together. Well, if you start with reliable and high quality data, that's the first step for successful reporting. That's kind of a no-brainer, don't you think? This actually allows for your organization to have a full view of what's going on within itself. You can share reports with full confidence, and make smarter data-backed business decisions. To get this point however, data quality starts with unified data streams, which are error free information and appropriate storage. Part of this comes with the actual unifying process in itself. So, for any fleet that is collecting data from various systems, having them all under one umbrella makes a fleet manager's job so much easier. We're talking night and day here.

(05:02):

For larger fleets with diverse assets, this is even more critical. Our business intelligence platform, for example, unifies fleet assets by connecting various data streams into one place. This is increasingly important with the influx of new technologies, making it easier for fleet managers to seamlessly integrate a new data source into the mix, but basically what I mean when I say our business intelligence platform can do this, if you think about it like a technology layer that sits on top of all of your data sources, that's what Utilimarc does. I don't mean to turn this into a sales pitch. That's not the point of this episode, but it's more just to give you an accurate visual of what exactly it could look like to unify all of your data sources in one place. The second piece of this is accuracy of information. The next step of this whole data quality venture journey process, whatever you want to call it, it asks a simple question. Is the data that we're actually seeing accurate?

(06:03):

As with any statistical variable, there's going to be a distribution of error, making it easy to identify outliers. Additionally, with the ability to now cross-reference data sources, discrepancies are quickly flagged, giving fleet managers an opportunity to dig into what went on. This can happen with any type of data validation system. Again, Utilimarc does this as well. We have our own process that our analysts and our data scientists work with, and I can dig into that more on a later episode. If you guys would be interested in hearing more about that, don't be afraid to let me know. Let's take fuel fraud for example, just to work on a real life example here. If you take a unilateral view of your data, it could be really difficult to confidently identify any fuel fraud going on within your fleet. Fuel card data might show that consumption's higher than usual, but without another source to check, you can be left without solid answers.

(06:55):

It's basically a story with no context, and as a marketer, as a writer, and as a storyteller myself, we all know I love a bit of context. My point is that with a program like business intelligence, or a software platform like a business intelligence platform, you can get a full, clearer picture, rather than having all of these disparate data sources that aren't working together. They're actually kind of doing this push and pull against each other. If your fuel card data especially shows higher consumption and telematics reports higher mileage, then you've got a clear explanation, and especially when they work together, then you get the clearer big picture. If you see the higher fuel consumption with no increase on mileage however, now you've identified an internal problem that could be worth looking into, especially if there's no error on the telematics side. That could indicate that you have a bit of fraudulent purchasing going on. You never know.

(07:51):

The last piece of this is data management, because I think managing the data is just as big as making sure that it's properly cleaned, and processed, and standardized, and all in one place. Once your data is unified and cleaned, data storage is the next factor that actually contributes to your data quality. So, depending on your fleet's goals and what you want to get out of your data, the way it is presented makes a major difference. For any fleet manager that's looking for higher level insights to share internally, seeing small scale granular data can make it nearly impossible. Especially if you're a visual person like me, the big picture really, really helps, especially when you have all your data in one place. On one hand, or actually I should say, on the other hand, fleets that want to look into digging into their data closely will have no use for a bigger picture summary. So really you can go both ways here. It just depends on what your goals are and what you actually need out of a data management platform.

(08:52):

Just to give you, again, a better idea of how a company like Utilimarc, how we work with data, and how we manage data, I'll give you a little bit of insight really quickly before we sign off for the week. So, if you're paired with a top-tier BI platform, a team of fleet analysts, our team of fleet analysts provide the industry expertise and personal understanding that can make all the difference into making your insights profitable. We say this because our team has collectively over two decades of working with fleets on a close level, so industry knowledge, their domain knowledge. We've heard all the pain points. We understand what fleet managers want out of their data. Our team's able to leverage that comprehensive business knowledge, so meaning you can merge fleet data with organization-specific context and goals, which is absolutely key when you're looking to integrate a more forward-thinking fleet management strategy.

(09:48):

Ultimately, the payoff of high quality data is accurate reporting that all members of your organization can confidently rely on whilst making these key decisions. Data quality lays the foundation for a fleet that is fully optimized, from operations, to cost, to performance, you name it. I'd love to know what you guys think, or any questions you might have, whether it's on business intelligence, data quality, the data cleansing process, standardization process. Let me know. You can send me an email, tag me on LinkedIn, use the #Utilimarcfleetfyis, send me a carrier pigeon. You know the deal by now. Anyways, that is all for me this week. I will chat to you all again next Friday. Ciao.

(10:40):

Hey there, I think this is the time that I should cue the virtual high five, because you've just finished listening to another episode of the Fleet FYIs podcast. If you're already wanting more content, head over to Utilimarc.com, which is Utilimarc with a C. U-T-I-L-I-M-A-R-C.com for this episode's show notes and extra insights coming straight from our analysts to you. That's all from me this week. So until next time, I'll catch you later.