What is Data Preparation? Examples, Steps & Techniques

Organizations today are surrounded by data, which can quickly become a burden when not managed well. Every system, app, and device generates more information than teams can meaningfully use. Marketing reports don’t match finance dashboards. Operations teams spend hours cleaning CSV files before every meeting. Analysts burn time fixing, not finding, insights.

This is where data preparation comes in. It’s the quiet backbone of every analytics project—the step that determines whether insights arrive in minutes or months and whether decisions are made based on fact or frustration.

Done well, data preparation transforms chaos into clarity. Done poorly, it turns even the most advanced analytics stack into a bottleneck.

Understanding data preparation

Data preparation is the process of collecting, cleaning, transforming, and organizing raw data so it’s ready for analysis. It ensures that the information entering your dashboards, reports, and models is accurate, consistent, and trustworthy.

In practice, it’s what happens between data collection and data analysis: fixing errors, filling gaps, merging datasets, applying business rules, and shaping data into a usable format.

While it can sound like a technical chore, data preparation is fundamentally a business exercise. It’s about aligning data to the context of your goals, whether you’re predicting customer churn, tracking supply-chain efficiency, or forecasting sales.

In a world where most companies pull from dozens of data sources, the ability to quickly prepare and unify that data isn’t just an IT function; it’s a competitive advantage.

Why data preparation is critical for business

Data preparation sits at the intersection of technology and strategy. When it’s done right, the entire organization benefits:

• Faster time to insight: Analysts spend less time scrubbing spreadsheets and more time exploring data.
• Better decisions: Leadership can trust that KPIs are built on consistent, verified inputs.
• Greater collaboration: Teams work from the same definitions and datasets, reducing the “whose number is right?” debate.
• Improved compliance and governance: Clean, well-managed data simplifies reporting and regulatory alignment.

The inverse is equally true. Incomplete, duplicated, or misaligned data leads to conflicting reports and costly missteps. According to Gartner, organizations lose an average of $12.9 million per year due to poor data quality. It doesn’t just create inefficiencies; it erodes trust in analytics programs and delays transformation efforts across the enterprise. 

In other words, every hour spent preparing data properly saves many more hours down the line.

Key steps in data preparation

While each organization’s workflow differs, most follow a similar sequence of steps to turn raw information into ready-to-use datasets.

Define your objective and scope

Before you touch the data, clarify the question you’re trying to answer. What metric are you tracking? Which systems contain the information you need? Starting with a clear business objective prevents scope creep and ensures that your data prep work aligns with real outcomes.

Collect and ingest data

Next comes pulling in data from multiple sources, like cloud applications, databases, spreadsheets, APIs, and sometimes even manual inputs. In modern platforms, ingestion is automated and connector-driven, ensuring that new or updated records flow in continuously rather than in one-off uploads.

Profile and explore

Once collected, the data has to be profiled. This step helps you understand what you’re working with: types of data, missing values, anomalies, and inconsistencies. Profiling surfaces problems early so you can decide whether to fix, remove, or enrich certain fields.

Clean and standardize

Cleaning involves correcting or removing inaccurate, incomplete, or duplicate records. Standardization ensures that data from different sources speak the same language. For example, “CA” and “California” referring to the same state, or date fields using a single format.

Transform, enrich, and model

After cleaning, the data is reshaped to fit analytical needs. That could mean combining datasets, aggregating fields, creating calculated columns, or enriching records with external data. This is where you model relationships, connecting customer transactions with demographic data or web behavior to reveal deeper insights.

Validate and publish

Finally, the prepared data is validated to confirm it meets accuracy and consistency standards, then published to downstream tools for reporting, dashboards, or machine learning. Validation is the “trust check” before the data is used for decisions.

Common techniques and approaches

Data preparation involves a mix of technical and analytical techniques, depending on the data’s shape and the problem at hand.

Handling missing values, duplicates, and outliers

Incomplete or duplicated records can distort analysis. Techniques like imputation (filling gaps with averages or medians), deduplication, and outlier detection help ensure cleaner inputs.

Standardization and normalization

Data from different sources often uses different scales, formats, or naming conventions. Normalization aligns numerical ranges (for example, converting currencies or percentages), while standardization ensures consistent units, categories, and structures.

Data blending and integration

Bringing together information from CRM systems, marketing automation tools, ERP platforms, and web analytics requires blending, matching, and merging records across systems. This step provides the holistic view organizations need for accurate reporting.

Enrichment and feature engineering

To make data more useful, teams often add context by pulling in third-party demographic data, location data, or historical performance. In machine learning, this might mean creating new “features” or derived variables that better predict outcomes.

Automation and self-service enablement

Traditionally, data preparation was manual and time-intensive. Today, automation tools and self-service interfaces allow business users to clean and shape data on their own, with repeatable workflows and governance guardrails. That shift is key to scaling analytics beyond IT.

Real-world examples of data preparation in action

The impact of data preparation becomes clearest when you see it in motion. Across industries, it’s the bridge between messy operational data and confident decision-making.

In marketing, imagine a team trying to measure campaign ROI across email, paid search, and social media. Each platform exports metrics in different formats, like impressions, clicks, and conversions, with inconsistent date ranges and naming conventions. Through data preparation, the team cleans and merges these sources, standardizes KPIs, and builds a unified view of spend and performance. Suddenly, attribution becomes reliable, and optimization becomes possible.

In supply chain operations, regional warehouses often report inventory levels separately. Without preparation, combining those feeds can lead to overlapping SKUs and mismatched units. A prepared dataset aligns item codes, normalizes quantities, and removes duplicates—giving logistics managers a real-time, enterprise-wide view of stock.

In HR analytics, data preparation helps teams merge employee data from multiple systems like recruiting, payroll, and performance to spot turnover patterns or forecast hiring needs. Cleaned and joined datasets reveal where talent gaps exist and what interventions might help.

Across these scenarios, the pattern is consistent: data preparation turns fragmented information into actionable insight. It reduces manual reporting and powers a culture of proactive, data-driven decisions.

How Domo helps organizations master data preparation

For Domo, data preparation is central to how businesses make decisions. Domo’s platform was built to simplify every step of the data journey, turning preparation from a technical burden into a collaborative advantage.

At the foundation, Domo connects to hundreds of cloud and on-premises data sources, pulling everything into a single, secure environment. Once data is ingested, users can explore and transform it visually—no coding required. Tables, joins, filters, and calculations can be applied with drag-and-drop simplicity, while advanced users still have full SQL and scripting capabilities.

But what truly differentiates Domo’s approach is its focus on speed and empowerment. Instead of waiting on engineering backlogs, business users can clean and combine data directly, while governance and access controls ensure consistency and compliance. The result: self-service data prep without data chaos.

Consider how this plays out in practice. A retailer might pull sales data from point-of-sale systems, marketing spend from ad platforms, and inventory from ERP. With Domo, those datasets can be automatically cleaned, joined, and visualized within a single workflow. Metrics update in real time. Decision-makers can track product performance or margin trends daily instead of monthly.

Or take a financial services team consolidating client data for regulatory reporting. Domo’s built-in dataflows automate transformations, validate records, and flag anomalies before submission, saving hours of manual reconciliation.

Ultimately, Domo’s goal is to remove friction. Data preparation should feel like a natural part of analytics, not a separate step. When data is clean, connected, and continuously updated, teams can focus less on fixing errors and more on discovering opportunities.

Challenges and best practices for success

Even with the right tools, effective data preparation requires intention. Technology can streamline workflows, but success also depends on process and culture.

1. Data silos and ownership. Departments often manage their own systems, leading to inconsistent definitions. Overcoming this requires alignment on shared metrics and collaboration between IT and business stakeholders.

2. Manual bottlenecks. Relying on spreadsheet-based prep limits scalability. Automated and reusable workflows reduce risk and free analysts to focus on higher-value tasks.

3. Governance and data quality. Without oversight, self-service prep can create “shadow data.” Establishing data stewards and validation checks ensures accuracy across the organization.

4. Skill gaps and adoption. Not every team member is a data engineer. Providing intuitive tools and training helps democratize data prep while maintaining standards.

To address these challenges, consider a few best practices:

• Start with high-impact use cases. Focus on data sources tied to measurable outcomes, like revenue, retention, or cost savings.
• Standardize definitions and metrics. A shared vocabulary ensures consistent reporting across departments.
• Document and automate. Treat data prep workflows as reusable assets, not one-off fixes.
• Balance freedom and control. Empower teams to prepare their own data, but layer governance policies that prevent errors and duplication.
• Continuously monitor quality. Data changes over time; so should your validation rules and checks.

Organizations that master these practices turn data preparation from a necessary step into a strategic discipline.

The future of data preparation

As analytics and AI advance, data preparation will only grow in importance. Automation and AI-assisted preparation are emerging as the next frontier, using machine learning to detect anomalies, suggest joins, and recommend transformations automatically. Recent research published in the Journal of Intelligent Information Systems highlights how AI frameworks are already improving data preparation for time-series and streaming data, offering faster, more accurate preprocessing for advanced analytics and machine learning.

At the same time, business expectations are shifting. Data prep can’t be confined to IT; it needs to live where business questions are asked. Modern tools like Domo are closing that gap by embedding data preparation directly into the analytics workflow, so teams can move easily from raw data to actionable insights in a single platform.

In the years ahead, the most successful organizations won’t be those with the most data, but those with the best-prepared data: clean, connected, and always ready to power the next decision.

Conclusion

Data preparation is the foundation of every insight your business produces. It turns disconnected, messy data into something usable that’s reliable, governed, and ready to tell a story about your business.

When preparation is automated, collaborative, and connected to the broader analytics lifecycle, it stops being a chore and becomes a competitive edge.

That’s what Domo enables: a modern approach to data preparation where every team, not just IT, can trust the data behind their decisions and move faster because of it.

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