Inactive

Leading petrochemical company seeks startups in Data Management for Oct 8-10

Deadline: October 6, 2019

Senior executives from TOTAL will be visiting MIT from Oct 8-10 for the purpose of exploring potential partnerships with MIT startups. The goal of October’s visit is to meet experts that can help to find the best way to enhance Data Management (DM) practices throughout TOTAL entities. Data must be managed effectively and valued to maximize its business impact. Attendees from TOTAL include Michel Lutz, Group Data Officer, and Gaile Lejay, Business analyst.

TOTAL is looking for efficient and state-of-the-art tools for different purposes:

Data integration - ingest/collect
Data quality - transform/process
Master Data
Organization's critical data
Management - classify & structure
Data modelling, data architecture & mapping - graph representations, relational data models, data flow design, lineage tool
Metadata management - dictionary/glossary
Data visualization - advanced presentations, interactive visual discovery
Data security & storage - data tracking, encryption

Data management is already used locally in TOTAL, the idea is to apply it to all Group companies, i.e. TOTAL S.A, all its subsidiaries, and other structures under the Group’s control.

In your response, please

Explain concisely what you offer
Your match with the company above and their interest
Relevant usecases / customer stories
Your availability for meeting, see above.

More details about TOTAL's needs:

Data integration: Integrating data and transforming them into ready to use formats; Flat file transfer; Transfer with transformation and aggregation; Distributed integration, with containers; Messages management
Data quality management: To implements quality techniques in order to ensure data is fit for consumption and meets the needs of data consumers; Data profiling tools to produce high level statistics that enable analysts to identify patterns in data and perform initial assessment of quality characteristics; Data querying tools to answer questions raised by profiling results and find patterns that provide insight into root causes of data issues; Data analysing, data standardization, data cleansing, data enrichment, data enhancement, data quality monitoring and planning, data migration.
Master data management: To monitor and to ensure the consistency and the sustainability of the organization’s critical data; Data deduplicating and matching (duplicate identification matching rules, match link rules, match merge rules); Referential versioning and historization; Master data records management: source records, master records, golden records, etc.; Transcodification and ID management; Hierarchy management and reference data structured (lists, taxonomies, …).
Metadata management: Metadata repository tools; Configuration management tools or database (CMDB); Database management and system catalogues; Managing the different sources of metadata; Analyzing and reporting metadata; Metadata control; Metadata stores.
Data modelling: Defining a logical organization of the used data to meet the need of the business; Precisely representing business objects; Build and maintain (versioning, etc.) relational data models (conceptual, logical and physical); Graphic representation of attributes.
Data architecture & data mining: Defining and maintaining data models with business and IT teams, with a focus on main business objects (product, client, supplier, etc.); Ensuring data coherence between multiple IT bricks: data repositories, middleware, business applications, portals, etc
Data visualization: Business intelligence visualization modules; Information graphics tools / Infographics; Classical visualizations (tables, pie charts, lines charts, bar charts, etc.); Advanced presentations: scientific / “artistic” visualizations; Data mashups: combination of data and services to create visualization for insight or analysis; Interactive visual discovery.
Data security and storage: Identity management technology; Data tracking tools; Data masking / encryption tools; Archiving.

The group TOTAL is a French multinational integrated oil and gas company founded in 1924 and one of the seven "Supermajor" oil companies in the world. Its businesses cover the entire oil and gas chain, from crude oil and natural gas exploration and production to power generation, transportation, refining, petroleum product marketing, and international crude oil and product trading. Total is also a large scale chemicals manufacturer. Total has its head office west of Paris. The company is a component of the Euro Stoxx 50 stock market index.

Deadline: October 6, 2019

September 11, 2019

TOTAL is looking for efficient and state-of-the-art tools for different purposes:

Data integration - ingest/collect
Data quality - transform/process
Master Data
Organization's critical data
Management - classify & structure
Data modelling, data architecture & mapping - graph representations, relational data models, data flow design, lineage tool
Metadata management - dictionary/glossary
Data visualization - advanced presentations, interactive visual discovery
Data security & storage - data tracking, encryption

Data management is already used locally in TOTAL, the idea is to apply it to all Group companies, i.e. TOTAL S.A, all its subsidiaries, and other structures under the Group’s control.

In your response, please

Explain concisely what you offer
Your match with the company above and their interest
Relevant usecases / customer stories
Your availability for meeting, see above.

More details about TOTAL's needs:

Data integration: Integrating data and transforming them into ready to use formats; Flat file transfer; Transfer with transformation and aggregation; Distributed integration, with containers; Messages management
Data quality management: To implements quality techniques in order to ensure data is fit for consumption and meets the needs of data consumers; Data profiling tools to produce high level statistics that enable analysts to identify patterns in data and perform initial assessment of quality characteristics; Data querying tools to answer questions raised by profiling results and find patterns that provide insight into root causes of data issues; Data analysing, data standardization, data cleansing, data enrichment, data enhancement, data quality monitoring and planning, data migration.
Master data management: To monitor and to ensure the consistency and the sustainability of the organization’s critical data; Data deduplicating and matching (duplicate identification matching rules, match link rules, match merge rules); Referential versioning and historization; Master data records management: source records, master records, golden records, etc.; Transcodification and ID management; Hierarchy management and reference data structured (lists, taxonomies, …).
Metadata management: Metadata repository tools; Configuration management tools or database (CMDB); Database management and system catalogues; Managing the different sources of metadata; Analyzing and reporting metadata; Metadata control; Metadata stores.
Data modelling: Defining a logical organization of the used data to meet the need of the business; Precisely representing business objects; Build and maintain (versioning, etc.) relational data models (conceptual, logical and physical); Graphic representation of attributes.
Data architecture & data mining: Defining and maintaining data models with business and IT teams, with a focus on main business objects (product, client, supplier, etc.); Ensuring data coherence between multiple IT bricks: data repositories, middleware, business applications, portals, etc
Data visualization: Business intelligence visualization modules; Information graphics tools / Infographics; Classical visualizations (tables, pie charts, lines charts, bar charts, etc.); Advanced presentations: scientific / “artistic” visualizations; Data mashups: combination of data and services to create visualization for insight or analysis; Interactive visual discovery.
Data security and storage: Identity management technology; Data tracking tools; Data masking / encryption tools; Archiving.

Disclaimer: MIT Startup Exchange can make introductions that ideally provide open ended discussions in order to share mutual interests and potentially create common ground that incite the parties to collaborate. MIT Startup Exchange introductions may eventually lead to mutual partnerships, but that is not in any way guaranteed by MIT, MIT Corporate Relations, MIT Industrial Liaison Program (ILP) or MIT Startup Exchange, which takes no responsibility for these outcomes and no formal part in such discussions following our introduction. MIT Startup Exchange and its activities and events are not for purposes of soliciting investment or offering securities.