I talked about data logging two months back in the post What is Data Logging. In the post I explained the principle of what data logging is, along with some applications. As a natural follow-up to that post, we're back to discuss the why of it all.
You have to measure it to improve it
You’ve probably heard of A/B testing, or split testing. It’s a method of running a control element and a variable, and measuring the results of each.
The idea is that to improve any element in a business (or in life for that matter) you need data to measure against. If the version with the new variable gets better results, you probably want to switch to using that newer version.
But no improvement can be made without first measuring what needs to be improved. Otherwise, you have no idea if the change got better results. Change without measurable results is not an improvement.
Organised data saves you money
Honestly, what area of business can't organised data improve? There are probably more ways that data can save money than I can list here.
But here are a few ways just for a start:
- Reduce the cost of lost product
- Reduce the cost of production through efficiency of resources
- Improve labour efficiency
- Reduce costly calculation or tracking errors
- Make data more transferable
- Capture worker productivity
- Improve product quality in manufacturing and shipping
- Monitor energy usage
- Provide notifications for problems before product loss happens
Monitoring is a huge financial benefit to logistics technology and the supply chain.
You can spot the trends
When you have data that can be organised into easy to see charts or patterns, you can follow the trends.
This allows you some measure of predictive power.
We can’t see into the future, but the right data does allow us to make better, more informed decisions about what’s likely to happen.
Data provides a competitive advantage
You no longer need a team of trained analysts to make data useful to your company. Existing software is available to analyse all the data for you. Everything can be automatically organised into charts and visuals right in front of you. Even better, you can filter the information to see only the parts you need for a particular use.
In fact, enterprise-level SaaS tools can analyse massive amounts of data for a relatively low cost, so there's no reason you shouldn't be taking advantage of this competitive edge when making business decisions.
Data is only going to grow
Believe me, the abundance of big data we have today is only going to keep getting bigger.
There are literally hundreds of millions of devices connected to the IoT. We’re talking smartphones, fitness trackers, and even commonplace items like lightbulbs and toys.
According to Daniel Burrus, the data guru, the growth is thanks to the continued advancements in “bandwidth, digital storage, and processing power.”
Data logging allows you to correct problems quicker
When you're getting real-time data, you can act fast. Problems are going to happen, even with the best planning. An unexpected issue doesn’t have to mean huge losses when there’s no lag in communication.
Imagine this scenario:
You have a shipment of products that need to stay refrigerated. Temperature fluctuations will cause product loss.
If you have data loggers providing compartment temperature information to an app, you can alert the driver and come up with a solution to save the product.
It’s not a far-off scenario at all. In fact, the biopharmaceutical industry estimates that about 20% of their products are damaged during shipping. That is a costly loss, one I talked discussed earlier in the post Three Reasons Data Logging is Vital for Your Pharma Company.
You’ll have more efficient production monitoring
Data logging is a key part of debugging and troubleshooting. Because apps and systems are constantly growing, pretty much no business can get away without data logging for their software.
You need to keep systems running smoothly. And that involves tracking and making sense of the data.
What it all has to do with your apps and systems
The apps and systems that you use can only help you insofar as the data you provide them are accurate. That’s how machine learning currently works.
In the future, it’s likely that AI will become more intelligent and start to use reasoning abilities to solve problems more like humans do, but that technology is still years off. For now, AI requires tons of information in order to “learn”.
Take the Pixel 2 smartphone, for instance. How exactly does its Now Playing feature pop up exactly the song you are listening to? How does the phone know?
The simple answer is that a system on the phone is connected to a cloud database. That database contains many thousands of songs, and that database keeps getting updated with new music. Because the Pixel 2 constantly updates its system to contain all the data for those songs, it can compare the music you are listening to, to its own internal database downloaded from the cloud, and pull up the information without needed to send back and forth to the cloud for an answer.
That’s why it’s so fast. And you don’t have to send any kind of request to the system.
It’s pretty amazing, right? The bottom line here is, for the near future, the more resources you can allocate to data, the better you'll be able to utilise the amazing capabilities available.
Did you enjoy the post? Take a look at the blog post I mentioned earlier, Three Reasons Data Logging is Vital for Your Pharma Company, if you didn't already do so, or get acquainted with some Logmore Features in our new series. The first part, Missions, can be found here.