Maggiore - The front-to-end data lake solution

Our best-of-breed tool suite contains all tools considered top of the range in their respective field, e.g. the data integration, reporting or analytics – they are scalable and suitable for both the occasional user as well as hardened data scientists.

Our data lake transformation program coordinates and leads your efforts to move from your traditional system landscape into a scalable, flexible and top-notch solution and supports you to effectively make use of it – you may still use existing core systems but selectively complement them and replace expensive or unreliable tools.

Our data lake config & technology selector provides you with a setup ensuring interruption-free interaction of the tools.

Our data model blueprint provides a working scheme to model more complex data entities typical in the energy industry, e.g. irregular time series.

Explore our Maggiore Tool suite

Click on the tools to learn how we use them to improve your business

Data integration and management tool for cloud solutions, allowing a speedy and cheap data integration and maintenance
Gartner study: Talend is leader in areas data integration and data quality

Business intelligence tool which allows easy data preparation and visualization
Allows accessing several data sources, easy definition and configuration of reports and automated processing and reporting

Microsoft Azure is a cloud solution service that provides infrastructure and software as a service (IaaA and SaaS)
The services include computational, mobile, storage, data management, messaging and many other services
We use Microsoft Azure as a platform on which we operate the other tools

Fast data science platform uniting data preparation, machine learning and predictive model development
Gartner study: RapidMiner is leader in area of data analytics
We particularly like the user friendly handling of data preparation, which typically takes a lot of time, and the way it makes machine learning and model training look easy

Framework for distributed processing of large data sets across clusters of computers, so scaling from single servers to many machines
Designed to detect and handle failures at the application layer, so delivering a highly-available service

SQL data warehouse for the cloud, so providing a complete relational data base for both structured and semi-structured data
Requires no administration and provides broad support for ETL and BI tools, and enables developers to build modern data applications

Our digital role mask provides a typical and generic description of information management and data science related roles and an adjustment and mapping into your organization, complementing existing role concepts and integrating in the existing organization.

Our transformation training schedule provides a methodic tool set and topically and role-wise modular schedule for enhancing existing analytics and other information management related skills, getting to know the Maggiore tool suite and creating awareness concerning the value of information and data security.

Meet the roles within our concept

Chief information manager (1/7)

Central coordinative role

Coordination of analytics activities, linking data exploration with productive operation, provision of infrastructure for data analytics, responsibility for concept for and compliance with information governance

Chief data steward (2/7)

Central coordinative role

Supports the information owner: coordinates with decentral data stewards and data scientists, ensures link between prototyping and operational readiness, and interfaces with Business-As-Usual and processing teams

Analytics use case owner (3/7)

Decentral lead role

Responsibility for explorative activities and actual analytics use cases: initiatives for improvements of methods, creating new information and increasing value of information

Data scientist (4/7)

Central or decentral expert role

Business and data expert with focus on data exploration: executes analytics studies, verifies hypotheses, (dis-) proves feasibility of models and value of information

Information owner (5/7)

Decentral lead role

Responsibility for information entities in operations: definition of entities, gate criteria in information life-cycle, data quality standards, access & usage of the information

Data steward (6/7)

Decentral coordinative role

Leads and coordinates operative data management activities in the data life-cycle such as securing & improving data quality and interfaces with Business-As-Usual and operational teams

Data operator (7/7)

Central support role

Daily processing and Point-of-Contact (PoC) in case of interruptions: data ingestion and provision, running of productive models

Our information life-cycle framework covers aspects for governance and processing of information that focus on increasing the value of information and provides a criteria-based gated process for the assessment of business cases and business models.

Our value of information concept uses set criteria and business case information to provide a semi-quantitative measure for creating awareness about the essentiality of any information in your business.

Our extensive data catalogue lists typical information entities used in the energy industry and covers value of information related attributes – it is flexibly adjustable to cover your data entities but also additional attributes you find important to govern.

Our data quality check-up provides criteria along six quality dimensions to make sure you can rely on your information.

Our analytics use cases catalogue provide an overview over the potential of some standard use cases for the use case owners and a training field for data scientists.