How to Achieve Good Data Quality: The Ultimate Guide for Modern Enterprises

In an age where data drives everything from supply chains to boardroom decisions, good data quality is no longer a nice-to-have—it’s a business imperative. But what does “good data quality” actually mean, and how can you achieve it?

Whether you’re a CIO preparing for digital transformation or a data manager struggling with inconsistencies, this guide unpacks everything you need to know, without the fluff. From understanding the core pillars of data quality to implementing a proactive data governance strategy, consider this your go-to resource for building high-integrity, business-ready data.

What Is Data Quality?

Data quality refers to the fitness of your data to serve its intended purpose. In practice, this means the data must be:

• Accurate: Free from errors, typos, or duplications.

• Complete: No missing fields or critical gaps.

• Consistent: Uniform across systems, units, and formats.

• Timely: Up-to-date and available when needed.

• Relevant: Aligned with current business needs.

• Reliable: Trusted by stakeholders for decision-making.

When data fails to meet even one of these criteria, it weakens analytics, misguides strategy, and incurs costs, often silently.

Why Data Quality Matters

Poor data quality costs the average business millions in inefficiencies, failed initiatives, and compliance risks. But more than that, it’s a barrier to innovation.

✔️ AI and automation fail without clean data.
✔️ S/4HANA and cloud migrations stall due to inconsistencies.
✔️ Customer trust erodes when incorrect or duplicate data causes errors.

Put, you can’t scale digital without solving data quality.

The Pillars of Good Data Quality

Let’s break it down into five key areas you must get right:

1. Accuracy

Ensure every data point is correct. That means:

• Validating data on entry.

• Eliminating typos, anomalies, and duplication.

• Matching entries against trusted sources (e.g., Companies House, government IDs).

Tip: Automate validation at the point of capture—manual correction is costly and error-prone.

2. Completeness

Partial records = incomplete pictures. For example, if customer records lack industry codes or contact details, marketing and compliance both suffer.

Best practice:

• Define required fields for each data domain.

• Use workflows to prevent incomplete submissions.

• Set up alerts for missing key fields.

3. Consistency

You can’t integrate or analyse inconsistent data.

A customer called “Acme Ltd” in one system and “ACME Limited” in another creates friction and duplication. Multiply that across materials, suppliers, and locations—and you’ve got a mess.

Standardise:

• Naming conventions

• Units of measurement

• Formatting rules

Use case: Master Data Management (MDM) platforms help enforce consistency by establishing a single source of truth.

4. Relevance

Holding on to outdated or unused data bloats systems and introduces noise. Keep data aligned to purpose.

Ask regularly:

• Is this data still useful?

• Does it help meet regulatory or business goals?

• Should it be archived or removed?

5. Timeliness

The value of data decays. Ensure updates are made in near real-time and systems are synchronised.

Stale data causes:

• Delivery failures

• Misdirected services

• Poor forecasting

Practical Strategies to Achieve and Maintain High Data Quality

Let’s get tactical. These proven strategies will lift your organisation’s data quality above industry standards:

1. Design Robust Data Collection Processes

Garbage in, garbage out. Define:

• What data should be collected

• Who owns it

• Where it comes from

• How often it’s updated

Use structured forms, drop-downs, and auto-completion where possible to avoid free-text chaos.

2. Automate Validation & Standardisation

Use tools to check incoming data for:

• Format errors

• Duplicates

• Conflicting values

Example tools include SAP Business Technology Platform (BTP) services, Maextro, or native ERP validation rules. Build in logic to stop bad data before it spreads.

3. Run Regular Cleansing & Enrichment Campaigns

No matter how clean your data starts, decay is inevitable.

Schedule:

• Cleansing: Remove duplicates, fix errors.

• Enrichment: Add missing but valuable context (e.g., segmentation, geocoding).

Data stewards should own this as part of ongoing governance.

4. Implement a Master Data Management (MDM) Solution

MDM is the backbone of enterprise-wide data quality. It:

• Creates a golden record for each entity (customer, product, vendor, etc.).

• De-duplicates and harmonises conflicting entries.

• Syncs clean data across your landscape.

If you operate across multiple regions, brands, or systems—MDM isn’t optional. It’s essential.

5. Foster a Culture of Data Ownership

Even the best tools fail without buy-in. Encourage teams to treat data as a shared asset.

Promote:

• Clear data ownership

• Regular audits

• Data quality KPIs tied to team objectives

Data Quality Tools & Technologies to Explore

To execute these strategies at scale, consider the following technologies:

• SAP Master Data Governance (MDG)
  Enterprise-grade MDM with deep SAP integration.

• Maextro by Bluestonex
  A fast-to-deploy, user-friendly data governance platform built on SAP BTP.

• SAP Data Intelligence
  For integrating, orchestrating, and monitoring data pipelines.

• Cloud Transport Management + CI/CD on BTP
  Helps maintain version control and governance over deployed solutions.

Measuring Data Quality: What Gets Measured Gets Managed

Set clear KPIs:

• % of duplicate records

• % of missing key fields

• Data error rates over time

• Business process failure rates linked to poor data

Use dashboards and scorecards to track progress, ideally surfaced in a centralised data quality monitor.

The Data Quality Ecosystem: A Visual Framework

Imagine a circular, interconnected process:

Capture → Validate → Cleanse → Enrich → Govern → Monitor → Improve

Each stage feeds the next. Good data quality isn’t a project—it’s a continuous improvement cycle.

Final Thoughts: Good Data Quality Is a Competitive Advantage

In a digital-first world, data quality is brand quality. It drives:

• Better decisions

• Smoother operations

• Stronger customer trust

• Faster time to market

Companies that treat data quality as a strategic priority, not an IT chore, will pull ahead in every major transformation—AI, automation, cloud migration, and beyond.

Start now. Clean data is the first domino in your transformation journey.

Want Help Getting There?

Whether you’re starting with a data audit or rolling out an MDM solution, Bluestonex and Maextro are here to guide you. Our SAP-certified experts help organisations simplify complexity, standardise governance, and build data foundations that fuel real innovation. Get in touch here or find out more about the impact Maextro could have on your data here.