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Customers' Business Cases

La préparation des données avec Tale of Data est présente chez de nombreux clients, tous secteurs d'activité.


Vous trouverez ci-dessous quelques exemples de clients qui utilisent notre solution de préparation de données pour mener à bien leurs projets de fiabilisation de données, d'analyse de données et d'enrichissement de la donnée.

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Rencontre avec un client
Centre de données
Accès aux empreintes digitales

Nos clients

Partage des données

Open Data

Intranet

Réutilisation

Travail collaboratif

Détection de fraude

Blanchiment

Fraude à la TVA

Fausses factures 

Financements occultes

Compliance et risques

Audit

Gestion des Risques

Litiges

RGPD

Sécurité

Lutte contre le financement du terrorisme

Cyber-sécurité

Menaces internes

Bourse
pièces d'empilage
Agent d'assurance
réunion d'affaires

Industrie

Données de capteurs (IoT)

Transactions financières

Plant Information

Détection d’anomalies

Banque et finance

Risque de Crédit (RWA)

BCBS-239

KYC

Bâle III

Assurances

Fausses déclarations

Déshérence

Evaluation des risques 

Solvabilité II (pilier 3)

Migration intégration

Segmentation

Migration

Intégration fournisseurs

Nos clients en détails.....

Rencontre avec un client

Détection de fraude

Blanchiment

Fraude à la TVA

Fausses factures 

Financements occultes

En savoir plus >

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Détection de la fraude aux documents administratifs

Detection of document fraud

Our client, a French ministry, wanted to improve the efficiency of controls over the allocation of administrative documents.


The size of the database (nearly one hundred million records) and the variety of applications allowing the entry of information - most often manual entry - severely limited the effectiveness of fraud detection.

 
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Marketing

Optimization of Marketing Campaigns by improving Data Quality and enriching CRM data

 

Our client wanted to increase the relevance of the marketing messages sent to his customers. To achieve this goal, he needed to improve the segmentation of its customer base and therefore solve the following two problems:

  1. Reliability of CRM data: multiple views of the same customer (duplicates), inconsistencies in emails, postal addresses and phone numbers

  2. The lack of contextual information about customers in the CRM

réunion d'affaires

Migration Intégration

Segmentation

Migration CRM

Recommandations

Optimisation de campagnes marketing

En savoir plus >

 
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Banque et finance

Risque de Crédit (RWA)

BCBS-239

KYC

Bâle III

En savoir plus >

Banque et finance

BCBS 239 - Compliance

 

Our client, one of the most important private banking players in Europe, had an obligation to comply with the BCBS 239 standard.

 

On January 9, 2013, the Basel Committee published a set of principles under the name BCBS 239, the objective of which was to enable banks to increase their reporting capacity and the accuracy of regulatory reports.

 
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Partage des données

Aggregating Multiple Databases with Record Lineage

 

Our client wanted to publish, on a single portal, a database resulting from the pooling of records fetched from 12 source databases.


Since overlaps existed between the different source databases, it was necessary to deduplicate so that portal visitors had a single view of each record.


Additionally, since the users of the portal were able to correct and / or enrich the data (= Crowdsourcing), it was necessary to keep, for each entry in the aggregated database, a link to the corresponding record(s) in the source databases (= Record Lineage), in order to pass on the record-corrections to the source.

Graphique

Partage des données

Open Data

Intranet

Réutilisation

Travail collaboratif

En savoir plus >

 
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Agent d'assurance

Assurances

Fausses déclarations

Déshérence

Evaluation des risques 

Solvabilité II (pilier 3)

En savoir plus >

Marketing

Notre client souhaitait améliorer l’efficacité des contrôles sur les comptes inactifs ou les contrats d'assurance vie en déshérence.

La solution Tale of Data lui permet d’identifier de manière unique ses clients :

- Rapprochement des personnes physiques ou morales fortement similaires grâce au moteur de dédoublonnage multicritères, multi-algorithmes

- Détermination d’un score de similarité permettant un dédoublonnage fin

- Enrichissement avec des référentiels externes (référentiels métiers, Open Data, …)

 
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Sécurité

Internal threats: prevention of the leak of sensitive information

 

Our client, one of the largest private banking players in Europe, wanted to minimize the risk of sensitive information leaking (identities, financial transactions, etc.). Since this type of leak is most often due to internal malicious acts, the Information Systems Security Manager wanted to exhaustively identify the sensitive information present in the bank’s information system in order to increase the level of protection.

Two questions therefore arose:

  1. Where exactly are stored sensitive data? Which databases? What tables? Which columns? But also which files? (e.g. Excel files and other listings disseminated on the internal network)

  2. What types of sensitive data are these?

Analyser les données

Sécurité

Lutte contre le financement du terrorisme

Cyber-sécurité

Menaces internes

En savoir plus >

 
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Data Center

Compliance et risques

Audit

Gestion des Risques

Litiges

RGPD

En savoir plus >

Compliance et risques

Personal data scans for GDPR compliance

 

Our client had to comply with the General Data Protection Regulation (GDPR). In order to do that, all the personal data present in his information system had to be associated with processing acceptable to the supervisory authority.

To achieve this goal, our client had to be able to answer these 4 questions:

  1. Who within the company keeps personal data?

  2. What types of personal data are these?

  3. Where are these personal data stored? Databases but also Shadow IT (e.g. Excel files disseminated on the internal network)

  4. For what purpose are these data kept?

 
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Industrie

Monitoring and improving the quality of PI data

 

Our client, an industrial group with hundreds of subsidiaries around the world, wanted to control and improve the quality of PI data (PI = Plant Information: data emitted by sensors installed on production sites).

The objectives were as follows:

  • Create PI nomenclatures (Assets, Attributes, Tags) with clear naming rules, which are free from duplicates in order to allow better reuse of Tags as well as cross-site analyzes.

  • Set up an efficient monitoring system for PI Tags (= time series): real-time detection of missing or inconsistent data, identification of defective sensors, etc.

  • Supply Data Scientist teams with reliable data, which is an essential prerequisite for building consistent and efficient predictive models (forecasting, predictive maintenance, etc.).

usine

Industrie

Données de capteurs (IoT)

Transactions financières

Plant Information

Détection d’anomalies

En savoir plus >

 
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Other scenarios are possible, do not hesitate to contact us to discuss your business cases.