BI Tools Defined
There are a lot of different types of BI tools that can assist in freight operations. We especially consider BI tools that present analytical data to the user. When we talk about artificial intelligence, the BI tools end up being a gateway between our data and the end user.
So the types of things that we love to see in BI are trends. There’s the trending of customer shipment volume and customer payment volume. Then there are the trends in carriers and for carriers against certain tariffs. This also includes their on-time statistics. These are all things that become important information. This is especially when we want to leverage when we’re getting information back to users.
Some Examples to Work With
Here are specific examples. This applies to on-time reports for a carrier, or average days that a carrier takes to deliver. Or it could be about pick up on-times as well as delivering on-time. So we can drill down. It’s not on-time for a carrier or their carrier statistics, but what are they on-time for? Are they on-time for their appointments? What about being on-time for the delivery? Are they on-time for their loading appointments?
By taking the general statement of carrier on-time report, we can dig deeper into that. Then we could find deeper tools and information that can provide added value to the users. Artificial intelligence is a natural progression for BI tools. It is the natural evolution of BI tools. That may be a more appropriate way to frame it. This is because what we see is the fact that BI tools provide a tremendous value. We’ve been needing and using them throughout our organizations for a long time.
Artificial Intelligence and BI Tools
AI takes that and steps it up a little bit further. It allows us to generate information that’s more intelligent. It looks more in-depth into the organization or into the way we model data. We can’t do these things with a traditional report. What we want to do is merge those two things together. We want to do take a basic report, which is a report that we would generate and see. This includes things like a profitability report or something similar. Then we want to take that and stick it against our machine learning models. We can then see what kind of trends and information we can model out of that same data, or with even more data. This provides even more value to the user.
Machine learning tends to be something that we leverage a lot in the AI world. This is especially when it comes to business analytics and data analytics. We can again apply machine modified models or the data set against standard models. We can then start trending our data that looks deep into more intelligent aspects of that data set.
Does an API Make a Difference?
It does make a difference if BI tools have an API. There are a lot of opportunities to have a BI tool. Things like single sign-on connectivity to your BI tool is great. It becomes challenging because you need to have the information back to the user when they need it. So having a single sign-on or having everybody in your office have access to your BI tools is great. But the question becomes: when are they going to do that during their day? When will they leave their system that they’re working and move over to the BI tool and go check out a report? I mean, it sounds great, but it’s not practical for their day-to-day run and the way that they’re handling their day. What’s better is if you have an API. Then you can integrate that to your application.
Tai is a great help for this. You can bring your business intelligence tools and visibility into your application. It is a key value of Tai and we can do this with a couple of lines of code through Tai. So while the users are interacting or doing their daily work they’re seeing the BI results. If your BI tools have an API, then you can do that as well. You can call up some of those business intelligence reports. From those, you can show that information to your users in context.