In June 2019, we wrote about the three pillars for establishing a Data Operations function to provide access to data in a transparent, controlled and reportable way. XMon supports Data...
XMon is known for its ability to track reference data calls and provide in-depth analytics of usage metrics, consumption, cost allocation and spend optimization. Our focus is reference data, but XMon’s core engine is able to process nearly any type of data.
To put this into perspective, we decided to source COVID-19 data and to plug it into XMon. We generated evolution graphs and set up alerts to be proactively notified of the status of the pandemic and of its development globally and per country.
We also had a brief look into whether Covid-19 had any significant impact on reference data consumption across our customer base.
Here’s what we did.
First, it was important to find a reliable source for Covid-19 data that we could connect XMon to. We decided to source ours from the Covid-19 free API, available here:
The API is easy to integrate with and provides historical data in JSON format, sourced from the Centre for Systems Science and Engineering (CSSE) at Johns Hopkins University.
Designing the XMon Metric
Once the data source was decided on, we configured an XMon metric with sufficient attributes to enable us to run reports and process automated alerts. We were interested in recorded the following attributes:
- Timestamp of data
- Number of new cases (daily and cumulative)
- Number of recovered cases (daily and cumulative)
- Number of deaths (daily and cumulative)
Once set up in the system, the XMon metric looked like this:
The XMon data collection agent was configured to obtain data historically, starting March 1, 2020 and automatically on a daily basis after the historical upload was completed.
Once enabled, data started flowing into the XMon dashboards and in a matter of minutes was available in the monitoring dashboards, the graph below shows the number daily confirmed cases for the UK since March 1, 2020:
The daily deaths in the United Kingdom shows a spike on the 29th of April, which corresponds to the UK government’s addition of deaths in care homes:
Automated Notifications and Alerts
While data was being collected, we configured automatic analysis rules to detect and proactively notify then team when a fall in the number of daily new cases over a 3 consecutive day period was detected, per country. The screenshot below shows how the rule was defined in XMon for Australia:
Displayed in a colour coded dashboard, we notice that all countries we were monitoring still had an increase in the number of cases over three consecutive days in the observed week (27th April 2020 – 03rd May 2020):
The XMon Analytics engine continues to process and analyse data and generate automated alerts when the number of Covid-19 cases decreases over a three day period per country.
Once historical data acquisition was completed, we ran historical trend analysis graphs, which showed the evolution of the daily deaths attributed to Covid-19 for selected countries of interest as well as the 7-day moving average:
Split by region, we obtain the graph below, showing the number of daily deaths across South America, Oceania, North America, Europe, Asia and Africa:
Did Covid-19 affect reference data request volumes?
An interesting question was to see whether there was a noticeable impact of the Covid-19 pandemic on reference data consumption across our client base. We ran a simple scenario to determine whether data volumes had been impacted since March 1, 2020 and compared it to the same period last year. Stripping out variations due to active XMon cost optimisations as well effects of significant business changes, we notice that there was nearly no difference in data requests over the periods of interest, if anything things may be on a lower trend:
XMon provides powerful data ingestion and processing engines that are used to process vast amounts of requests for reference data across our clients. The XMon engine can, and has also been used to track other types of data, for example internal data requests flowing within the organization, or, as this article has briefly shown, scientific data related to the global pandemic we are all witnessing at the moment.
Reach out for more information about the provided analytics, graphs or alerts, or to see how the XMon team can help make sense of your data, whether it’s reference data or otherwise.
Please stay safe!