Data Governance

Competence throughout the management cycle of company data able to solve problems of:

• Harvesting
• Modeling
• Lineage
• Impact Analisys
• Data Quality
• Data Profiling
• Semantic Lineage

in different industrial contexts such as IOT, Banking, Automotive, Energy, Public Sector.

Data Virtualization

Expertise on established Data Virtualization Technologies (Denodo and Tibco) for:

• Integrate and access data from heterogeneous sources.
• Create a semantic layer that creates a unified view of your data, without the need to move or replicate the data.
• Include a data security and governance layer to control data access and ensure regulatory compliance.
• Use a query optimizer to optimize queries and ensure the best possible performance.

Data Catalog

Expertise in:

• Creation and Management of the Data Catalog, the centralized metadata register that describes information on business data, (details on the data themselves, relationships between data, data properties, data owners …).
• Business Glossary creation and management of relational business glossaries and semantic lineages.
• Monitoring of the architecture of the company BI system.
• Use of market-leading products such as Talend Data Catalog, Irion.

We follow the most widespread and recognized methodological approach to Data Management developed by the Data Management Association (DAMA) in which Data Governance is central and consists of Strategy, Roles, Policies, Products / Services and Evaluations.

Starting from Data Governance, 9 areas of Knowledge and specific Expertise on data processing radiate from Data Governance.

Collaborative Approach

The collaborative approach to Data Governance is based on 5 pillars:

• Data mapping and quality (Data preparation – Data Lineage). for easy and quick access, detect errors and inefficiencies and maintain traceability
• Data Virtualization to build the Data Fabric and streamline access to source data without moving it
• Metadata recognition and management (Data Catalog)
• Data Democratization (Business Dictionary)
• Security by default (security and data protection, protection of intellectual property, comply with legal obligations and GDPR)

The Benefits for the Business Client

The benefit for the Business Customer is intrinsic in the process of Data Democratization because it allows:

• The definition of clear rules, in terms of rights and obligations, for those who use data
• Usability
• Reducing time and effort in using data
• The increase in the autonomy of the Customer of the Business area, consequently reducing that of IT or, in general, of the structures that manage data from a technical point of view

Strategic Consulting

Define the most effective data governance strategy to efficiently use data and drive business growth.

Identify the organization-critical data that has the greatest impact on business operations and results.

Guidance to manage risk, reduce costs, and effectively execute business goals.

Definition of policies and procedures for data management (roles, responsibilities, access rules, quality, …)

Training

IT Manager training: definition of policies and procedures for the management of IT systems and technologies, security and information privacy.

Compliance Officer Training: definition of company policies and procedures for the management of regulatory compliance.

Business Analyst Training: understanding business needs and defining data requirements.

Depending on the needs, the training may involve the Chief Data Officer and the Data Privacy Officer.

Implementation and Monitoring

Assessment, Set up and Implementation of technological tools to support data governance for:

• Metadata Management
• Data Quality
• Data Virtualization
• Data Catalog
• Data Lineage

Continuous monitoring and improvement: for the continuous verification of the effectiveness of the policies and procedures adopted, in order to identify any critical points and improve data governance activities.

Professional Services

Data Steward to define and monitor data: management policies and procedures; critical data; quality; the semantic layer of virtualization; access to data.

Data Architect & Data Engineer for: design, development and management of Data Architecture also in compliance with regulations.

Data Analyst for: Data analysis and extracting and identifying actionable insights into trends, opportunities, and threats.

Data Scientist to: trigger value-added Machine Learning services on enterprise data.