Effective Data Governance as a vehicle for opportunity & stability
Beyond the new regulatory obligations of the RGPD, companies are facing new issues:
increase in data volume, high storage costs, cyber security, IS mergers, greater human involvement.
Data Governance is a set of principles and practices to ensure the high quality of data thoughout its entire life cycle.
#Cartography #Ecatalog #MasterDataManagement #MDM #DataQualityManagement #DQM #Databaker #DataOwner #DataSteward
Your data concerns
Security
- What is the most critical data?
- Who creates, modifies, accesses critical data?
- Where is the sensitive data stored?
- How sensitive is it?
Compliance
- What data is subject to the RGPD?
- Are all personal data collected necessary for the purpose?
- How to prove consent to processing?
Asset value / Data quality for the company in full digitalization
MDM, DQM: reliable and standardized data can also be reused…
- What is my available business data, overview?
- Is the data stored, maintained, classified and accessible in an optimal way to benefit from it?
- Who is responsible for it?
- What is the value and impact of the data on the business?
- How is it modified (flows)?
Approach / Scope according to maturity level
A new culture where each organization is required to take ownership of and develop data governance in order to achieve digital transformation.
Agile data governance – What benefits for the company?
Agile data governance is a democratized bottom-up approach to data, fueled by technology maturity and growing concerns and challenges around data quality, data privacy and data regulation.
It’s governance that unlocks the value of data for end users, enabling them to solve business problems, leverage self-service analytics and do their jobs better and more efficiently.
Opéra Use Case
Tools used
From mapping to the analysis of production data (quality, security, integrity…), Opera accompanies you in the integration of tools with high added value.
Blueway, Datagalaxy, Talend, Informatica, Semarchy.
Change management
Change management is nowadays one of the main challenges when talking about data governance. Indeed, digital transformation implies the internal development of the trust granted to its ecosystem, and change management is the key!
The Trust appears to be the cornerstone of change within an organization. It needs to be capped by effective data governance to understand what exists and reinforced by three important elements:
Our services in 4 phases:
- Phase 1: Discovery of the organization’s ecosystem
- Phase 2: Presentation of the risks and uses
- Phase 3: Developing a Trust & Risks culture
- )àPhase 4: Setting up Trust governance