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Big Data &

Data Management

During our journey throughout "performance management" projects delivery , we figured out that providing Business Users  with a quick access to raw data improves their decision making process and  help them better monitor the gap between their actual results and expected results.


For instance, monitoring a customer behavior (a buyer, a subscriber, ..) or tracking a good shipment  on a real time basis can be key to successfully anticipate  a loss or improve a profit by proactively taking the adequate  action (Promotion, Order management ..) .


As a result EPSM developed its expertise around this area  by providing easy-to-deploy (quick go-to-market) non-intrusive solutions that can fit to any existing IT landscape and provide business users with better insighit into their data.

Self Service Reporting & Query at place

Use best-of-breed reporting technology to design and build  self-service reporting platforms that embrace  "Query at place" concepts.  

Without the need to move the raw data from it is original storage allow users (business or IT) to run ad-hoc analysis and build their own dashboards

Data Ingestion , real time, near real time

Over the years , classic ETL has shown some limitations when it comes to treating unstructured data or processing high volume of data. And most of the time this exercise requires a import amount of resoruces (memory, CPU,..) .


New Data Ingestion tools  came to the picture to bypass classic ETL limitation and enable real time data processing with limited amount of resoruces

Predictive Analysis & Machine Learning

Technologies related to "Artificial Intelligence", "Deep Learning" and "Machine learning" is evolving quickly and new frameworks are released to the market at a constant trend. Identifying the right framework and deploying the adequate predictive models can definitely anticipate changes ito customer behavior , project  market and portfolio trends, and predict system and machine failure 

Data Lakes and Warehouse augmentation

Data Lakes were proven to be an efficient alternative to support various analytical use cases , establish right level of governance and enable optimal data retrieval.
One of the common use case of "Data Lakes" is completing or augmenting an  existing Datawahreouse . 

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