I am big fan of advanced methods deployed to solve practical problems by ordinary users. Here is our recent achievement. My colleague, an experienced service desk manager, observed that the volume of work in his team has grown. He would
An approach to categorize multi-lingual phrases
I have 130,000 help desk tickets with multi-lingual descriptions. I need to divide this set into categories, such as “password reset”, “license expired”, or “storage failure”. Why? Users could then allocate a category to a new ticket they create. Then
Answering Why (with Chi-Square)
Analysts don’t like the “why” questions. They are tough to answer. For instance, in a help desk analysis, it is easy to show which tickets are resolved faster. But it is difficult to say why. In my practice in Sopra
What makes Data Quality so difficult
Garbage in, garbage out. Analysis of untrusted or poorly understood data will yield incorrect results. Hence the textbook approach is to clean the data first, and only then proceed with data analytics. For instance, in the data lakes, the data
Practical AIOps: 5 use cases
In Sopra Steria we manage the IT infrastructure and applications of big clients. We process millions of service tickets and infrastructure events. This massive stream of data comes from monitoring tools such as Zabbix, Nagios, Solarwinds, and higher level frameworks:
I stopped writing reports, and so can you
This post explains how to generate management-quality PDF or HTML reporting directly from Jupyter Notebook. With this technique, I reduced to zero the most irritating part of my projects: copy-pasting diagrams into PowerPoint.
Data Puzzle explained
For the Data Puzzle I posted last week, I received about a dozen of thoughtful and highly relevant answers. THANK YOU. I want to primarily thank to Luis Ruiz Santiago, Chetan Waman and anonymous J for comments under the previous
Data Puzzle
Here is a new data puzzle, coming from my recent analytics in Sopra Steria. I will describe the problem, but not the answer. If you like the challenge, please contribute your thoughts in the comments. The title of the data
A picture worth 1,000 words
I love mountains. Some of my dear ones say that this is only because they resemble histograms, which I love more. Not true (ha ha), but I must agree that visualizations done properly brings plenty of satisfaction. Histograms, when prepared
How to tell anomalies in data drift
In this article I tackle the following problem: how to define and distinguish anomalies (spikes, peaks, and outliers in data) in real-life, production situations. Typically, the data drift results in the absence of a reference level. Since we do not