Edge analytics to the rescue
Most of the data cleaning and management that occupies scientists’ time can easily be carried out automatically by edge analytics software. This includes the functionality for pre-processing IoT data in real time, effectively turning big data into small and relevant data.
Key tasks that you can achieve with edge analytics include:
- Data cleaning, including removing outliers and smoothing out noisy data.
- Data normalization and standardization, making sure all your data looks the same, so it can be fed directly into dashboards and other analytical tools.
- Data enrichment, including adding metadata, timestamps and other information to your sensor data.
- Flow control functions, such as throttling and joining data from several sensors in the same payload, or splitting data if needed.
- Data reduction and anomaly detection, for instance to make sure that only relevant data is being sent to the cloud and in an aggregated, filtered format unless there is an anomaly.
- Data integration, to add data to your preferred enterprise or cloud-hosted analytics system in streaming format or in file format, depending on your preference.
How much is it worth to carry out these tasks at the edge? Consider that according to Baseline Magazine, 40% of data scientists currently do not have enough time in the day to even do analysis.
And while data scientists are a happy group, with 79% claiming to be satisfied at work, it is fair to say cleaning data is not their favorite task: 58% said they spend too much time doing it, and more than 50% said what they really enjoy is predictive analysis and data mining.
Happier data scientists. Lower software cost. And more
If you use technology to offload menial data cleaning and management to the edge, you not only potentially free up about two-thirds of an average data scientist’s day but also help them spend more time doing the things they like.
This, in turn, could reduce staff turnover in your data science team while at the same time allowing your business to achieve deeper, more meaningful insights from your IoT deployment. Plus, there is one final economic benefit.
Most databases and analytics systems charge by the amount of data they have to store or process. If most of that data is not being used, then you could end up wasting a significant portion of your budget.
With smart edge analytics, on the other hand, you make sure that the only data that gets to your cloud systems is fully relevant. This means you make the most cost-effective use of cloud resources. It doesn’t take a data scientist to figure out that this makes sense.
Read more about the Crosser Edge Computing solution here>