DQ&MDM Practice Lead, Adastra Bulgaria
From Understanding Your Data to the Single Source of Truth
Join us for this session and find answers for questions like:
What are the data cleansing techniques?
What data quality problems can be solved automatically?
Can there be achieved 100% data quality? What could be the effort?
When is matching and merging used?
What is a golden record and how it is created?
As your organization grows, the volume and complexity of data increase exponentially. Not only that, but also the data itself is evolving fast. The purpose, quality and location of any data set can change overnight. The growing number of user groups who interact with your data can affect its integrity.
A scalable, enterprise-wide Data Governance framework is central to ensuring the performance, quality, sustainability, and scalability of your Information Management systems. Our data governance experts work hand-in-hand with your organization to align on business objectives, analyze current compliance state, and mitigate the risk of poor data quality.
Why Adopt Adastra’s Data Governance Framework?
Enable your organization to assess data faster using tailored data governance methodology. Derive actionable insights that can quickly be implemented with minimal disruption to your business.
Ensure a single version of truth for your organization.
Save time and costs for business planning and accountability.
Maximize ROI and strategic decision-making outcomes.
Standardize corporate management of all critical data assets.
Lower Quality Assurance costs for complex data projects.
Experience faster ramp-ups and time-to-market for new business.
Improve operational efficiency and cross-department collaboration.
Meet and exceed regulatory requirements and avoid penalties.
Data Governance Services
We guide organizations collate and collect their massive data sets and help them build a refined data governance strategy. We make sure your data is proactively and efficiently managed across your company in a standardized format and can be accessed and used when needed to get business insights faster and increase operational efficiency.
The Data Governance Framework Guarantees
Ensure a common language when it comes to understanding of the business definitions across your organization through governed data.
Rely on a data that is complete, valid, accurate, consistent, and provided in a timely manner. Understand both its origins and its alterations.
Ensure your data that is rationalized across multiple systems, has been matched, linked, and merged to provide a single source of truth.
Attain self-service capabilities and derive better business decisions from a more accurate, consolidated, governed, and trusted set of information.
How do we do governance?
For Data Governance implementation, we adopt a 6-step strategic roadmap including:
- High-level needs and capabilities assessment
- Strategic vision, objectives, and organizational impact assessment
- Planning and program definition
- Discovery, scoping and design
- Implementation Support
- Planning, support principles and policies through approvals, streamline processes and procedures
Data Governance FAQ
Data governance is a data management concept that addresses the risk versus value of data across an organization from ingestion to analysis. Data governance is policy-driven to manage regulatory compliance, data quality, and data access. Good data governance means getting the right data to the right people at the right time to drive faster time to insights.
Data governance is important for a multitude of reasons. It allows to set up the system of individual accountabilities for the data that is used. This is particularly important because it ensures that data that is used across organization is accurate, well trusted, shareable and usable for beneficial decision-making purposes.
Everyone within an organization that interacts with data is responsible for the proper access, usage, storage, archiving and disposal of enterprise data.
Data governance is focused on mitigating risk while improving data accuracy. Example use cases include GDPR and CCPA compliance for data privacy, Solvency II compliance for risk management in insurance, IFRS compliance for financial reporting, role-based access to information to foster collaboration and more.