Data Knowledge Results | Value Added Decision Intelligence Services

We build complete analytical decision systems, designed and implemented to be used regularly by business users. The industrialisation of our solutions is part of any project, once the proof-of-concept is passed, and the business cases proven.

Our services in this area cover the following:

• Building datamarts - On top of existing datawarehouse if possible, we will recommend building an analytical datamart to create the analytical memory of your company, and keep an organised history of the critical information supporting the decision process. The reason why such datamarts are requested, is that most of the time, existing datamarts are subject-oriented stores of data supporting a reporting function that lack the level of data granularity that analytical processes request.
• Industrialisation of Analytical Data Models (ADM) - An ADM is a single, big file presenting in one step all information needed to compute analytical models (predictive, descriptive, simulations, etc.): analytical algorithms do not work natively on relational data models. The quality of any analytical process dramatically depends on the ADM. The quality of the data presented, of the recoding and of the derived variables, is essential for the success of any project. Based on our experience, we already have generic ADM 's for Finance and Telco sectors, covering our B2B & B2C propositions. 
• Industrialization of all analytical processes - All analytical solutions and models can be fully industrialized and are often based on the ADM built at the beginning of the production chain. These industrialized processes often play the role of publishing, on a regular basis, key decision support information about the market:  individual view of profitability scores, propensity-to-buy indications, churn scores, risk scores, etc. Building an historical central repository of those key indicators further allows to support the preparation of strategic market actions - selection of top profiles across multiple dimensions- as well as the expansion strategy - market view of segments mutation and cross-over reporting, KPI reporting on the effects of the marketing actions, churn evolution, etc.
• Data Preparation Processes - This includes the automation of quality processes, computing derived values, preparing ADM's, etc.
• Building Applications - Applications are sometimes necessary in order to use the outcome of our systems.  The Groups Exploration Tool is a good example of that since exploring Networks using a table paradigm is useless from a business point of view. Our applications often integrate algorithms in order to intelligently present the results to the business users.