Data management aims to maximize the value and utility of data within an organization. It ensures that data is accurate, secure, accessible, and properly governed, empowering organizations to derive actionable insights, drive innovation, and make informed decisions based on reliable information.
Data management determines the effective handling and governance of data throughout its lifecycle by devising best practices for data lifecycle management. The absence of data management strategies can result in inaccurate data, data silos, security risks, compliance issues, inefficient processes, missed opportunities, poor governance, and increased costs. Implementing effective data management strategies helps organizations unlock the value of their data, ensure data quality and security, and drive data-driven decision-making.
The purpose of data management is to effectively handle and govern data throughout its lifecycle to meet the needs of an organization
In contrast, data management ensures that those rules and procedures are followed to ensure the quality and usability of data for decision-making.
The maturity level of data management practices can vary across organizations. While some organizations have implemented comprehensive data management strategies, others are still in the early stages of developing their data management capabilities. Here are a few common scenarios that businesses should consider for data management:
- Developing data management: Initiated data management projects, invested in data infrastructure, and are in the early stages of defining data management roles and responsibilities.
- Adhoc data management: Aware of data management's importance but needs to prioritize it or allocate sufficient resources.
- Industry-specific data management: Have dedicated data management teams, rigorous data governance frameworks, and well-defined data security and privacy measures
- Advanced data management: Have dedicated teams, established data governance frameworks, automated data integration, and quality processes, and adopted advanced technologies for data analytics and decision-making
Unlock the power of data to make the best decisions
We partner with our customers to evaluate data governance maturity levels and implement the techniques as part of the data management that matters most for their business. Virtusa brings tailored service offerings for unlocking the value of information from data governance (define the policies) to data management (actual implementation of tools, frameworks, and policies).
Data governance advisory services
It includes an assessment of the current landscape and comes up with the policies.
- DG assessment – data lineage, data quality, MDM/RDM, data security
- DG roadmap
- Advisory: DG policies, process, and DG office
Data catalog
It enables ML-based data cataloging
- Discover metadata
- Build technical catalog
- Build business glossary
- ML-driven data association
Data lineage
It automates data lineage
- Identify the location of sensitive data
- Collate technical & business metadata
- Discover data flow across systems
- Establish data lineage
Data quality
It enhances data quality (DQ) for a unified business experience
- Metadata management and data cataloging
- Rules engine
- Define DQ KPIs
- Implement continuous monitoring
- Advanced DQ with ML and statistical methods
MDM
- MDM solution design
- MDM configuration and implementation
Data archiving
Implement ILM policy to identify and archive data
- Define ILM policy
- Locate sensitive data
- Implement tier-based data archival
- Audits, reports, and restore
Data Security
Assess protocols and implement security controls
- Sensitive data discovery
- Data masking (dynamic/static)
- Continuous monitoring
- Data subsetting and retirement