SPEED DATA TRUST AND CATALOG ADOPTION
Qbiz Data Stewardship Accelerator
Data is the lifeblood of any modern organization, but it can also be a source of frustration, confusion, and risk if it is not correctly managed, curated, and governed. Stewardship is the practice of ensuring that data is accurate, consistent, complete, relevant, and secure throughout its lifecycle. Stewardship also involves enriching business context, definitions, glossary terms, lineage, quality indicators, and other metadata that help users understand, trust, and leverage data for their needs.
Many organizations have invested in data catalog software, which helps users discover, explore, and document assets across different sources and platforms. Catalog software can automate some aspects of data stewardship, such as scanning, profiling, and tagging data. Still, it cannot replace the human expertise, judgment, and collaboration essential for effective stewardship. Catalog software is only as good asthe information it contains, and without properstewardship, it can become outdated
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THE PROBLEM
Data Stewards are the linchpin, they play a crucial role in managing and ensuring the quality, integrity, and security of data within an organization. Yet when rolling out a governance program the position of Data Steward is often overlooked. Subject matter experts (SMEs) with very different business functions as their day jobs get pulled in as data stewards because they are close to the data. They must wear a lot of hats that business SMEs don’t necessarily have the time or training for, including:
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Data Custodian
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Remediation Owner
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Data Quality Manager
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Data Validator
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Data Analyst
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Compliance Manager
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Policy Enforcer
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Privacy Officer
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Access Controller
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Business Owner
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System Owner
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Domain Expert
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Data Ambassador
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DG Evangelist
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Data Strategist
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Data Educator
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Consensus Builder
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Problem Solver
COMMON CURATION STRUGGLES
Without good data stewardship, an organization’s engineers, analysts, and knowledge workers face many challenges and frustrations, such as:
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Spending too much time and effort finding, understanding, and validating data and not enough analyzing, interpreting, and communicating insights
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Dealing with errors, inconsistencies, and duplication that compromise data quality and integrity, leading to poor decisions and outcomes
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Lacking confidence and trust in data's accuracy, completeness, and relevance fosters questions about the validity and reliability of results.
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Struggling to keep up with the ever-changing and growing volume, variety, and velocity of data, thereby missing out on valuable opportunities and insights
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Experiencing silos, fragmentation, and duplication that hinder collaboration, integration, and reuse, thereby creating inefficiencies, uncertainty, and redundancies
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Failing to align data with business goals, requirements, and standards, thereby risking compliance, security, and governance issues
QBIZ DATA STEWARDSHIP ACCELERATOR
A fast and effective solution to curate data and implement data catalog software best practices for a specific business department, subject area, or sub-domain. Our solution can help you achieve the following benefits:
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Increase data trust and confidence among data consumers and producers
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Enhance data value and usability by providing rich and relevant metadata
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Improve data quality and reduce data errors, inconsistencies, and duplication
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Align data with business goals, requirements, and standards
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Comply with data regulations, policies, and best practices
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Empower data users with self-service access, discovery, and collaboration
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BESPOKE QBIZ APPROACH
Qbiz’s Data Stewardship Accelerator follows a proven approach and process that focuses on one data domain or sub-domain at a time, such as customer, product, or finance. This allows us to address each data domain’s specific needs and challenges in a targeted and efficient manner. Our process consists of three phases: assessment, consensus building, and catalog curation. Each phase involves close collaboration and communication with your stakeholders, such as data owners, consumers, analysts, engineers, scientists, and governance teams.
The Assessment phase is critical to laying a strong foundation for AI adoption. This phase includes:
Usage Analysis
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Top Users
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Top Reports
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Data Discovery
Quality Analysis
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Accuracy
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Validity
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Completeness
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Column sampling and profiling
Knowledge Transfer
Assessment
We start by assessing the current state of your data domain, including the sources, types, volumes, and data quality. We then use various tools and techniques, such as usage analysis, profiling, and quality analysis, to identify the data assets, issues, and opportunities within the domain. We also evaluate the existing data governance framework, including policies, standards, roles, and responsibilities, and how they apply to the data domain.
Consensus Building
In this phase, we facilitate collaboration and alignment among data stakeholders, including data owners, consumers, stewards, and governance teams. We help define and communicate data stewardship's goals, benefits, roles, and responsibilities. We assist in rationalizing and harmonizing data assets by resolving conflicts, redundancies, and inconsistencies. We support establishing and enforcing data stewardship policies, standards, and processes aligned with business objectives and data strategy. Centralized governance enables successful data governance by providing top-down oversight.
Catalog Curation
We help implement data stewardship practices using data catalog software in this phase. We guide you through ingesting metadata from various sources into the data catalog and validate and verify data assets' definitions, classifications, and policies. We assist in enriching and organizing data assets by adding additional information to enhance understanding and usability. We also train you on using data catalog features to support data stewardship outcomes.
DG Blueprint
DG Foundation
DG Buildout
DG Move-in
→ DG drivers & motivations interviews
→ Usage analysis
→ Requirements analysis
→ DG gap analysis
→ DG framework
→ DG charter creation
→ Define DG pillars
→ Identify data domains & sub-domains
→ Define roles & responsibilities
→ Identify maturity model
→ Design data architecture
→ Baseline maturity model
→ Define data management council
→ Identify key stakeholders
→ Resourcing plan & funding
→ Identify data regulatory and compliance risks
→ Create policies, standards & practices
→ Create security controls
→ DG education & adoption plan
→ Document DG workflow and approval authority
→ Create a DG intake process
→ Data architecture integration
→ DG tool selection process
→ Technology evaluation POC
→ DG tool implementation & configuration
→ Security & privacy controls
→ Data classification
→ Data integration & migration
→ Automation of active metadata management
→ Data quality & controls
→ Data cleansing & unification
→ Automate policy enforcement
→ Stewardship curation
→ Usage monitoring & observability
→ Quality analysis
→ Data rationalization
→ Facilitate communication
→ Metadata ingestion
→ Validation
→ Manual enrichment
→ Maturity model evaluation
→ Data remediation process
