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New GenAI & AI agent development

Data & Analytics

Data Governance Consulting

We define data owners, policies, glossaries and quality rules, then embed them in your catalog and pipelines, so teams know who is accountable and can rely on the numbers they use.

  • Data ownership
  • Catalog & glossary
  • Quality controls
  • Access & privacy
Years of delivery
8+
Years of delivery
Projects shipped
350+
Projects shipped
Clients across US, UK, AU & SG
200+
Clients across US, UK, AU & SG

What’s included

Everything You Need to Make Your Data Trustworthy

We set up the policies, ownership and controls that give your business reliable, secure and well-documented data.

  • Data Governance Maturity Assessment

    We review your current policies, ownership and controls against recognised frameworks and highlight the risks that need attention first.

    • Maturity assessment
    • Risk register

  • Governance Framework & Operating Model

    You get a practical framework with committees, decision rights and a rollout plan sized for your organisation.

    • Governance charter
    • Rollout roadmap

  • Data Ownership & Stewardship Roles

    We name data owners and stewards for each domain with clear responsibilities, so issues have someone accountable.

    • RACI matrix
    • Role descriptions

  • Data Catalogue & Business Glossary

    We document your data assets, definitions and lineage in a searchable catalogue, so teams find and understand data fast.

    • Data catalogue
    • Business glossary

  • Data Quality Rules & Scorecards

    We define quality thresholds, automate checks and publish scorecards, so leaders see which datasets are fit for decisions.

    • Quality rules
    • Quality scorecard

  • Privacy, Classification & Compliance Controls

    We classify sensitive data and implement controls to help you prepare for GDPR, CCPA and similar regulations.

    • Classification scheme
    • Control mapping

  • Access Policies & Data Lineage Tooling

    We configure access rules and lineage tracking in Collibra, Purview or Alation, giving you audit-ready visibility.

    • Access policies
    • Lineage map

  • Governance Programme Support & Auditing

    We run stewardship forums, review policy adherence and refine controls each quarter so governance becomes routine.

    • Quarterly review
    • Policy audit

Need something specific?

Why Kramate

Why Business Owners Choose Kramate for Data Rules and Trust

We set simple rules your team will follow, not a thick binder that sits on a shelf.

  • Rules Short Enough to Follow

    Instead of

    thick policy binders nobody reads

    With Kramate

    We write each rule in a few plain lines, and we test them with your team first.

  • A Name Beside Every Problem

    Instead of: bad data that nobody feels responsible for

    With Kramate: We help you give each data area an owner, so problems have someone to go to.

  • Ready When an Auditor Calls

    Instead of: scrambling for proof at the last minute

    With Kramate: We keep your records of who can see what, so you can show it quickly.

  • A Score Anyone Can Understand

    Instead of: not knowing which data to trust

    With Kramate: We give each key set of data a simple score, so leaders see what is safe to use.

  • We Come Back to Check

    Instead of: rules that fade after the project ends

    With Kramate: We hold regular check-ins to see if people are following the rules, and we adjust them.

Our approach

How We Run Data Governance Consulting, Step by Step

Clear rules on who owns your data, who may use it and how it stays safe.

7 clear steps 7 deliverables you keep

  1. Current-State Review

    We review how your data is handled today, interview team leads and note the biggest risks.

    You receive: Assessment report

    You arrange the team interviews

  2. Governance Plan

    We design a simple operating plan with a small council, clear rules and a realistic first year.

    You receive: Governance plan

    You approve the plan

  3. Who Owns What

    We agree which person owns each data set and which people look after its day-to-day quality.

    You receive: Role chart

    You name owners and stewards

  4. Data Dictionary

    We write down what data you hold, where it sits and what each business term means.

    You receive: Data catalogue

    You confirm the meanings

  5. Quality Scoring

    We set quality rules, such as no blank phone numbers, and score each data set against them.

    You receive: Quality scorecards

    You set acceptable levels

  6. Sensitive Data Rules

    We label sensitive data. We set access rules that match the privacy laws you follow.

    You receive: Control rules

    You confirm your legal duties

  7. Ongoing Council Help

    We help run the council, check that rules are followed and prepare evidence for auditors.

    You receive: Audit pack

    You hold regular council meetings

Tools & technologies

Technologies We Use for Data Governance

We configure the catalogue, quality and access tools your organisation already licenses or prefers.

  • Collibra: Data governance platform for catalogues, glossaries and ownership.
  • Microsoft Purview: Catalogues data, tracks lineage and classifies sensitive information.
  • Alation: Data catalogue that helps teams find and understand trusted data.
  • Python: Readable language for business logic, data and automation.
  • Apache Airflow: Schedules and monitors data and model pipelines.
  • Metabase: Self-serve dashboards for exploring business data.
  • Okta: Single sign-on and multi-factor login for staff and customers.
  • HashiCorp Vault: Stores passwords, keys and tokens securely for apps and teams.
  • Azure: Microsoft cloud hosting and services.
  • AWS: Amazon cloud hosting and services.
  • Snowflake: Cloud data warehouse that scales storage and queries separately.
  • Databricks: Unified platform for big data processing, analytics and machine learning.
  • Google BigQuery: Serverless Google warehouse for fast queries on very large datasets.

Expert guide

Data Governance Consulting: A Complete Guide for Businesses

Data governance consulting helps an organization decide who is accountable for its data, which rules apply to it and how those rules are checked. Done well, it is a small set of clear decisions: who owns customer data, what counts as an active customer, who may see salary records, how long invoices are kept, and what happens when a number looks wrong.

Done badly, it becomes a binder of policies that nobody reads. This guide describes how data governance consulting works in practice.

It covers a lean starting point, roles, policies, quality controls, catalogs and lineage, privacy duties in the USA, UK, Australia and Singapore, and how to budget and choose an adviser, so the program you end up with is one your teams will actually follow.

What data governance consulting covers, and what it does not

Governance answers three questions: who decides, by what rules, and how do we know the rules are followed. It overlaps with security (who can access), quality (is it right), privacy (is it lawful) and architecture (where does it live), but it is the layer that assigns accountability across all of them.

It is not a tool purchase and not an audit alone. A catalog or a quality tool helps only after someone owns the data and agrees what good looks like. Frameworks from bodies such as DAMA International (opens in a new tab) provide a shared vocabulary, which you adapt to your size.

A minimum viable start

Organizations that try to govern every dataset at once stall. A practical consultant begins narrow:

  1. Pick the data that hurts or carries risk: typically customer, financial and employee records, plus anything feeding board reports or AI.
  2. Name an accountable owner for each, a business leader, not an IT contact.
  3. Write down critical definitions, such as active customer and net revenue, in one shared glossary.
  4. Classify sensitivity into a few levels, such as public, internal, confidential and restricted, and attach handling rules.
  5. Add checks to the most important pipelines and report failures to the owner.
  6. Review and extend after the first set works, domain by domain.

Roles and decision rights

Typical governance roles
RoleResponsibilityTypical decisions
Data owner Accountable for a domain, usually a senior business leader Who may access it, acceptable quality, retention
Data steward Day-to-day quality and definitions within the domain Resolving errors, maintaining the glossary, approving changes
Data custodian Technical operation of systems, often IT or data engineering Storage, backups, access implementation, pipeline fixes
Governance council Small cross-functional group that settles disputes Policy exceptions, priorities, cross-domain definitions

Keep the council small and its meetings decision-focused. If it only receives status reports, replace it with a shared channel and meet when a real conflict needs settling.

Policies and standards worth writing

  • Classification and handling: what each sensitivity level means for storage, sharing and masking.
  • Access: how requests are approved, reviewed and removed, based on least privilege.
  • Retention and deletion: how long each record type is kept and who confirms deletion. Keeping less data also lowers storage cost and breach exposure.
  • Quality standards: thresholds that matter to the business, such as no duplicate invoice numbers.
  • Sharing and sourcing: rules for sending data to partners and for bringing in external datasets.
  • Data for AI: which data may train or feed models, and who approves new uses.

Each policy should fit on a page or two, be written in plain language and name an owner and a review date.

Turning rules into automated controls

A rule that depends on memory fails. Wherever possible, encode governance in the pipelines:

This work sits close to data engineering, and the controls are easiest to add while pipelines are being built. If the data landscape is fragmented, master data management often follows.

Data governance consulting and privacy: USA, UK, Australia and Singapore

Privacy law gives governance concrete deadlines and obligations. An inventory of personal data, with owners and purposes, is the foundation for meeting almost all of them. Always confirm requirements with legal counsel.

The NIST Privacy Framework (opens in a new tab) is a voluntary, region-neutral way to organize this work. For delivery, Kramate offers four to six hours of overlap with US Eastern time and a full working day with the UK, Australia and Singapore, which helps when workshops span owners in several regions.

Making governance stick

Adoption depends on making the right behavior the easy one. Give people a searchable glossary, a single place to report a data issue, and quick answers. Track measures that reflect real outcomes: how many critical datasets have owners, how long data issues take to resolve, and how many reports use approved definitions.

Avoid vanity metrics such as the number of policies published. Celebrate the fixes that prevented a wrong decision, because that shows the business why governance exists.

Cost factors and how to plan a budget

Consulting effort scales with the number of business domains, regions and regulations in scope, the maturity of existing documentation, and the amount of tooling and pipeline change required. Internal time is the largest hidden cost, since owners and stewards must make real decisions.

A fixed-price assessment with a prioritized roadmap is a sensible first step. Implementation can then follow on a dedicated-team or hourly basis, depending on how much is engineering versus advisory work. Keep tool licensing separate from advisory fees, and delay buying a catalog until roles and definitions exist.

How to choose a data governance consulting partner

  1. Pragmatic scope: they propose a narrow first release and say what they would defer.
  2. Business-side focus: they work with owners, not only IT.
  3. Engineering ability: they can implement tests, catalogs and lineage, not only write policies.
  4. Regional awareness: they know where your obligations differ and when to involve counsel.
  5. Handover plan: your team can run the program without them.
  6. Plain deliverables: short policies, a glossary and a roadmap you can use.

How Kramate delivers governance work

We start with a free consultation and send a detailed estimate within 24 hours. An NDA is in place before detailed discussions, a dedicated project manager keeps you updated daily on Slack or Teams, and every two-week round ends with a review of what has been decided and built. All intellectual property, including policies and code, is transferred to you. The full process is in our approach to governance engagements.

Frequently asked questions

What is data governance?

It is the set of roles, rules and checks that make data accountable: who owns it, how it may be used, what quality is expected and how problems are resolved. It spans quality, privacy, security and definitions.

How do you start a data governance program?

Choose a few high-value or high-risk datasets, name business owners, agree key definitions and add basic quality checks. Expand domain by domain once the first set works. Starting everywhere at once usually stalls.

What is the difference between a data owner and a data steward?

The owner is accountable for a domain and makes access and quality decisions. The steward handles daily upkeep: fixing errors, maintaining definitions and answering questions. One owner often works with several stewards.

How does governance relate to data quality?

Governance sets who is responsible and what good means. Quality work, such as automated tests, profiling and cleansing, enforces it. Without clear ownership, the same quality fixes are repeated forever because nobody removes the cause.

Does governance help with AI projects?

Yes. It records where training and input data came from, whether that use is permitted, who approves new uses and how problems are traced back to a source. See our AI consulting services for model-side planning.

How do we know governance is working?

Look for outcomes: critical data has named owners, issues are resolved quickly, reports share one definition per metric, and audit or customer requests are answered from records instead of last-minute scrambles across teams.

Sources and further reading

FAQ

Frequently asked questions

Cost depends on organisation size, number of data domains, existing policies and how much tooling you need, such as a catalogue. Regulatory scope and the number of workshops also matter. A focused programme on one domain costs less effort than a company-wide one. We give a no-obligation estimate within 24 hours.

We usually assess the current state, define roles and policies, set quality rules, introduce tooling and then embed the routines. Pace depends on executive backing, how available data owners are and how many domains you include. Starting with one critical domain shows results sooner.

You own them. Kramate signs an NDA before detailed discussions, and the policies, glossaries, role definitions, quality rules and templates transfer to you. They are written in your language and for your organisation, so they can evolve without us.

We build on what you have. Governance roles are mapped to people already responsible for data, and policies are enforced through your current databases, catalogue or BI tools where possible. We avoid adding heavy new process where a small change in existing workflows is enough.

Data management is the day-to-day work of storing, moving and processing data. Data governance sets the rules, roles and accountability for how that work is done. Governance decides who owns a dataset and what quality means. Management carries it out.

Identify an executive sponsor, list your main data domains, and gather any existing policies or audit findings. Name the pain points, such as conflicting reports or access disputes. Knowing which regulations apply to you, for example GDPR or CCPA, helps us scope properly.

Track things people feel: fewer disputed numbers, faster access approvals, named owners for key datasets and data quality scores that improve over time. Audit readiness is another sign. We set these measures with you and review them at regular intervals.

Taking new projects

Let’s talk about your Data Governance Consulting needs

Tell us about your project and get a free consultation and estimate within 24 hours.

  1. We reply within 4 business hours A real consultant reads your brief — no bot, no queue.
  2. 30-minute discovery call We dig into goals, constraints and budget. NDA signed on request.
  3. Proposal in 24 hours Scope, timeline, team and price — clear enough to decide on.
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