What is Data Quality and Why It Matters for Business Analytics?

“Quality means the customer comes back, not the product” – this well-known quote has become a motto for many companies and teams. Keep in mind that the customer might be your internal client — the Management Board, CFO, or Business Manager — and the product could be a report or analysis. Let’s take a closer look at why data quality is such a critical element of business analytics. Would you rather have no analytics at all or base your decisions on a set of chaotic, inaccurate, or so-called “dirty” data? The accuracy of your data directly impacts the effectiveness of reporting tools and, ultimately, the business decisions you make.

Data Quality – Less Is More

The larger or more dynamic your organization, the more data sources you’re dealing with — and their consistency is key. To avoid information chaos and prevent poor decision-making, it’s essential to manage the process through a Data Quality Management (DQM) framework.

Technology has enabled nearly limitless data collection. Companies want to know everything about their customers to tailor offerings and forecast demand. Employee activity is monitored to identify bottlenecks and optimize time. Add to that revenues, margins, and other performance indicators. To draw actionable insights from this data, you need a holistic view and the right KPIs for each area.

Employees waste up to 50% of their time on tasks related to poor data quality
(MIT Sloan)

Before diving into analysis, ask yourself: are your KPIs based on reliable data? Managing data quality is essential. It involves a set of practices by analysts and data professionals to maintain data accuracy throughout the lifecycle — from acquisition to analysis.

What Defines High-Quality Data?

• Reliability – Can the data be verified as true and accurate?
• Consistency – Internal and cross-source coherence, including naming conventions and calculation logic.
• Timeliness – Data must be up to date and reflect current conditions.
• Accuracy – Data must be presented clearly and tailored to the recipient’s needs and knowledge level.
• Completeness – All essential components must be included without errors.
• Relevance – Data must be meaningful to the use case or business problem being addressed.

Data Quality Management – The Key to Success

Monitoring, reporting, and analytics are only as good as the data behind them. Before any analysis begins, ensure the source data is trustworthy. Define and implement validation, cleansing, and consistency-checking procedures.

41% of B2B marketers cite inconsistent data as the biggest barrier to ROI optimization

(Dun & Bradstreet)

Effective data quality management starts with strategy. Key elements include:

• Understanding data sources, ownership, and technologies involved
• Optimizing processes (automation, normalization, and data standards)
• Proactive monitoring and error correction in source systems
• Establishing clear data hygiene procedures
• Defining accountability for data quality and verification

Data Hygiene (Clean Data)

Clean data is essential to effective data management. Acting on poor-quality data means delivering less value — or even losing revenue.

Businesses lose up to 20% of revenue due to bad data

(Kissmetrics)

Data cleansing involves detecting and correcting duplicate, inaccurate, outdated, or irrelevant data. It can be done manually or with the help of automation — the goal is the same: to ensure high-quality, trustworthy data for business decisions. It’s time-consuming (analysts spend up to 60% of their time on it!), so automation and identifying root causes of data issues is key.

Key stages of data cleansing include:

• Validation – Screening for basic errors that compromise the analysis process
• Standardization – Harmonizing inconsistent naming, formats, or categorization
• Duplicate removal – Detecting and eliminating redundant entries
• Completing or removing partial records – Filling in missing data or excluding incomplete ones
• Conflict resolution – Fixing contradictory values between datasets (e.g., mismatch between order count and order value)

Investing in Data Management

Managing your data properly means having confidence in your reports and the decisions you make based on them. Before you worry about dashboard design or KPIs, focus on trustworthy, consistent, and well-prepared data sources. Automation will save time and eliminate human error. You don’t need an enterprise-level data warehouse or massive budget to manage data quality — optimized processes and a culture of quality are what matter most. For the rest, the right tools and expertise will get you there.

Need support with your data quality strategy? Get in touch — at Enterium, we use proven data management practices and ETL techniques to deliver clean, business-ready datasets. We work primarily in the Microsoft ecosystem, always tailoring solutions to your specific needs and goals.