The 3 types of data analysis

By Matthieu Henry D’Ollières, in charge of D&A at Altermès

The explosion and omnipresence of data in the enterprise are real opportunities to reinvent the way we work. Before undertaking a data analysis project, it is essential to know the objectives of each analysis.

Depending on their level of automation and integration, finance teams can rely on different types of data analysis to optimize risk management:

Descriptive analysis

The objective is to identify exceptions in the processes and to understand what happened and in what context (i.e. what, when and how).

These analyses are carried out using dynamic indicators, consistency checks and data cross-referencing.

Some examples of indicators and automated controls :

  • Calculation of the payment period
  • Backlog and number of orders released without receipt or invoice
  • Double payment of supplier invoice
  • RIB supplier present in a tax haven
  • Transaction with a country with a prohibited currency

Applied approach

  • Manual extraction of one or more data sources from flat files, APIs, DBMS, business applications
  • Use of queries or “scripts”: a set of instructions given to software to extract data flows from a process.

Tools used

  • Data preparation (Alteryx, SAS, Datawatch…)
  • Data analysis (ACL, Idea…)
  • Business intelligence (Microsoft Power BI, Microstrategy…)
  • Data visualization (Tableau Software, Qlik…)


  • Exception reporting from business applications
  • Adhoc Report

diagnostic analysis

The objective is to understand the origin of the exceptions identified in the processes (i.e. why and where did it happen?)

This type of analysis allows us to identify anomalies and understand the process biases that led to them. This allows us to work on improving processes to prevent certain anomalies from recurring. At the same time, process improvements also lead to productivity gains.

Some examples of diagnostic analysis:

  • Analysis of the steps that make up a process and the existence of variants in relation to the process in force in the company
  • Segregation of duties :
    • Creation and validation of purchase orders by the same employee
    • Validation of the receipt and entry of the invoice by the same employee
  • 2 and 3 Way Match:
    • Analysis of variance tolerance settings (price and volume)
    • Identification of transactions in error (price and volume) from orders, receipts and invoices…
  • Risk analysis :
    • Duplicate names, VAT number or SIREN number attached to a supplier
    • Duplicate names, VAT number or SIREN number attached to a supplier

Applied approach

  • Use of queries or “scripts” (objective: where is the problem?): a set of instructions given to software to examine whether internal controls have been violated. In addition to reducing the effect of human error, which is inevitable in a manual review, the script makes the action quickly reproducible.
  • Data mining (objective: why did this happen?) to identify correlations between numerous databases of one or more applications

Tools used

  • Descriptive analysis tools (see previous slides)
  • SQL queries …
  • Data mining (Blueway…)
  • GRC (i.e. Governance Risk Compliance) tools (SAP GRC, Oracle Management, BWISE, Supervizor…)
  • Process mining (Celonis…)


  • Functional analysis in order to identify the technical solutions that meet the challenges to be covered
  • Setting up and writing procedures for implementing queries and data/process mining applications
  • Process Diagnostics

Predictive analysis

The objective is to predict future outcomes based on historical data (i.e. what will happen and why?).

Applied approach

Implementation of the strategic data analysis vision: exhaustive description of the business process to be modeled, with a definition of the business objective, variables, control factors and constraints to be analyzed.

Tools used

  • Descriptive and diagnostic analysis tools (see previous slides)
  • Predictive models according to the nature of the data to be analyzed
  • Predictive analysis tools (Adobe, Board,…)


  • Building a new BI architecture from reporting to predictive analysis
  • Implementation of a Data Lake platform
  • Decision support information modeling
  • Development of a decision-making portal

Any questions? Any comments? Need clarification? The Altermès teams are available for you 👋

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