EPSM CONSULTING
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 .