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Data is the new oil. Clive Humby Who are we? Multidisciplinary - PowerPoint PPT Presentation

Oct 29, 2022 •157 likes •440 views

Data is the new oil. Clive Humby Who are we? Multidisciplinary Team Data Scientists Data Engineers Database Engineers Machine Learning Engineers Multidisciplinary Backgrounds Data Science Business

  2. “Data is the new oil”. Clive Humby

  3. Who are we?

  4. Multidisciplinary Team Data Scientists • Data Engineers • Database Engineers • Machine Learning Engineers •

  5. Multidisciplinary Backgrounds Data Science • Business Intelligence • Engineering • Economics • Database Architecture • Software Development •

  6. Our Mission

  7. Provide to our customers high quality solutions in Big Data, Data Engineering and Data Science in order to boost their business while giving support to efficient strategic decision making.

  8. Our Vision

  9. To be an innovative company from whom people feel proud of being part of and to be a reference in quality in the IT market.

  10. Our Values

  11. Collaborative people • Innovation culture • Analytical mind • People success • Quality •

  12. Our experience

  13. Data Science Projects Text Mining/Text Analytics/Chat Bot • Data/Big Data Analytics and Predictive Models • Computer Vision and Image Detection • Anomaly/Fraud Detection • Clustering and Recommendation software • IoT Systems with Time Series Analysis •

  14. Some projects we have done:

  15. IT Operations Industry Prediction for ticket dispatch • Classification of ticket text closure • Worked hours anomaly detection • Server resources anomaly detection • Root cause detection with change/incident • correlation

  16. Health Industry Classification of cancer images for skin cancer • type identification

  17. Software Industry Log analytics with anomaly detection •

  18. Finance Industry Fraud detection on transactions •

  19. Energy Industry IoT system with prediction, simulation and • anomaly detection

  20. How can we help your business? (in six steps)

  21. Step 1: Spotting a pain You identify a problem. • It could be something you would like to improve profit or reduce expenses.

  22. Step 2: Gathering data You provide data about the issue. • It could be data about employees, sales, products or everything else.

  23. Step 3: Preparing data We clean, standardize, normalize and remove • missing/wrong data in order to avoid misunderstandings.

  24. Step 4: Drilling data We understand the data, apply analytic technics • on it and suggest a strategy to extract valuable insights for your business.

  25. Step 5: Creating a solution We develop a model or a bot that best suits your • problem.

  26. Step 6: Deploying the solution We deploy the final solution that provides • predictions/insights for your business.

  27. The end.

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