Analysis of IT support activities

Context and problematic

Client : the teams in charge of supervising the activity of IT support.
Need : Have a global and precise vision of all areas to better allocate resources, reduce tickets and be pro-active on emerging/ growing themes.
Constraint : Data (tickets) not annotated and not standardized between the teams (templates, chat, emails,…)


  • Identification of request topics (tickets) with 3 levels of precision (ex: Outlook – Certificate – Expiration)
  • Identification of trends for each subject (emerging, constant, more topical)
    Identification des tendances pour chaque sujet (émergent, constant, plus d’actualité)
  • Unsupervised analysis consistent with feedback from those responsible for each scope (difficult to quantify because unsupervised)
  • Our intervention

    1 Data Architect for V1

    • Definition / framing of the project
    • Data cleaning and standardization
    • Ticket analysis based on unsupervised Machine Learning methods:
      Clustering of tickets (LDA, DBSCAN, K-Means…)
      Topic Modeling / characterization of clusters
      Trend detection on identified topics
    • Automation of the analysis and the final report that shall present the results


    V1 carried out for 2 perimeters, discussions in progress for the putting into production and the launch of a V2.

  • For V1:
    Deliverable: automated report
    Subjects identified & degree of precision: OK for the 2 perimeters
    Subject trends: OK for both perimeters
  • Technical environment

    Clustering : Scikit-Learn, Gensim
    Topic Modeling : Gensim, Wordcloud,
    Visualisations / Restitution: Plotly, Jupyter
    Jenkins, Git

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