What is Data Preparation? 101 Guide

Data preparation is the process of cleansing, transforming, and organizing raw data so it’s suitable for future and machine learning (ML).

It usually includes activities like:

• Structuring unstructured data
• Cleaning incorrect, incomplete, or duplicated data
• Standardizing formats
• Combining data from different sources
• Anonymizing personal information
• Converting data types
• Filtering irrelevant data
• Adding metadata

The goal is to take raw, messy data from various sources and shape it into high-quality, analysis-ready datasets.

With adequately prepared data, you can trust the insights from your reports, visualizations, and models. It lays the foundation for impactful analysis.

Data preparation is crucial in any analytics or business intelligence initiative.

Here are some key reasons why:

• Yields higher quality insights: With disorganized, unstructured data, you risk getting questionable insights. Proper preparation leads to accurate analytics.

• Increases productivity: often need help with disorganized datasets. Clean, refined data enables them to work more efficiently and concentrate on deriving insights.

• Uncovers additional insights: Data preparation tasks can uncover new insights and reveal more data correlations.

• Enables better decisions: Quality data leads to quality analytics, leading to better business decisions based on real insights. As the saying goes, garbage in, garbage out.

• Improves : Data preparation requires setting policies, rules, guidelines, and documentation for re-use.

• Scales artificial intelligence (AI) and ML: Tidy, structured data is necessary for training and implementing artificial intelligence (AI) and ML models at scale.

Solid data preparation helps you derive more value from your business data and analytics efforts while saving time and driving more intelligent decisions. It’s a prerequisite for productive analytics.

Machine learning algorithms need polished and structured training data to create accurate models. Appropriately preparing data for analysis is especially vital for ML projects.

Tasks to prepare your data for ML might include:

• Handling missing values: Algorithms can’t handle NULL values, so you must input the missing data.

• Encoding categorical data: Converting text categories to numbers for modeling.

• Feature engineering: Creating new aggregated or derived features from existing data to uncover insights.

• Feature selection: Selecting the most relevant features to avoid overfitting.

• Data normalization or scaling: Standardizing data ranges to enable meaningful comparisons.

• Data augmentation: Artificially increasing training data by adding modified copies.

• Splitting datasets: Creating training and validation or test sets to evaluate models.

• Addressing class imbalance: Re-sampling if one target class dominates to prevent bias.

With proper data preparation, you can train robust and accurate models and avoid issues like overfitting, underfitting, and biased predictions.

Investing in solid data preparation and analysis massively pays off when deploying ML in production.

Taking the time and resources to prepare sound data brings many advantages. It saves resources and fuels impactful analysis while mitigating the risks of “dirty” data.

Here’s why data preparation is a foundational best practice for analytics success.

Efficient data analysis

Clean, organized data enables analysts to derive insights immediately instead of wasting time formatting and cleaning data. Data preparation leads to more productive and uninterrupted analysis workflows.

High-quality data

Comprehensive data preparation enhances your data’s quality and reliability. You can tackle problems up front and avoid jeopardizing downstream initiatives. Trustworthy data sets the path for trusted analytics.

Improved data consistency

Preparation resolves formatting issues, conflicting representations, and variance across sources, creating standardized, consistent data—essential for accurate consolidated reporting and analytics.

Effective data preparation follows a systematic approach. This methodical style enables you to extract the maximum value from your data.

Continuously revisiting the steps helps you ensure a sound data foundation, and you can automate much of the process to accelerate future preparation.

Your preparation method may differ depending on your business needs, data types, and resources, but it usually involves the following steps:.

Collect your data

Gather relevant data from all necessary sources, including databases, CRM systems, spreadsheets, and any other areas where you house data. Identify any gaps upfront.

Evaluate and explore your data

Assess the scope, formats, and quality of your data. Look for inconsistencies, errors, and areas for improvement. You could also explore it with visualizations.

Clean your data

Fix any issues by removing duplicates, correcting formatting problems, and handling missing values. Standardize the data as needed using chosen data preparation techniques.

Transform your data

Perform transformations such as aggregations, merging datasets, calculations, encoding categorical data, and more. Engineer features to better highlight the relationships and patterns—this helps train ML models or simplify analysis for humans.

Store data safely

Organize your cleaned dataset and store it suitably for analysis—for example, in a or . It’s also best practice to control who can access and alter this information for increased security.

Visualize and analyze your data

Confirm your data quality by visualizing it and spot-checking. Once you trust your data, you can perform any necessary analysis techniques and use them to build models.

Though essential, data preparation can be tricky. Overcoming its challenges takes diligence but enables superior analytics.

Documenting your issues can help you continuously improve your data pipelines, and using data preparation software and tools can accelerate the process.

Strategic data preparation enables you to tap into your data’s full potential while saving time and headaches.

Knowing these common challenges helps you understand what to do if you encounter a problem.

Incomplete data

Reliable analysis requires sufficient data. Strategies like data imputation can fill these gaps. Cross-checking data against other sources can help identify and fill in missing information.

Data inconsistency

Conflicting data representations, formats, and labels can create confusion, so you should standardize and transform your data. Establish consistent rules, formats, and taxonomy upfront to help you streamline consolidation.

Multiple data sources

Integrating data from multiple sources, like databases and APIs, is complex. Careful extract, transform, load (ETL) processes are required to ensure nothing is lost or broken. Using platforms and stitching helps simplify the process of combining data sources.

Let’s walk through a hands-on example to see data preparation in action.

Imagine you work at a . You have raw sales data across your stores and want insights into your best-selling products.

The raw data is in a spreadsheet format with one row per transaction. Columns include transaction ID, store ID, product ID, product name, price, and transaction data.

First, you would assess the original data to identify any issues. This would include finding consistent product name and date formatting and duplicate transaction rows.

To clean the data, you would remove the duplicates and create consistent formatting rules—like standardizing all date values to a YYYY-MM-DD format.

Next, you would transform the data to aggregate it from the transaction to the product level. Let’s imagine you decide to summarize the total sales revenue and quantity sold for each product at each store.

You’d then load the aggregated dataset into a database or for easy access during analysis. The final prepared dataset provides clean, consistent, and unified data at the appropriate level for you to garner productive insights.

With the heavy lifting of preparation done, analysis—like visualizing best-sellers by store—will be straightforward. You could use this information to know where to order more stock, spot upselling opportunities, and learn how to produce more desirable products.

This example demonstrates how proper preparation converts tangled data into valuable business insights that can influence strategic decisions.

can help you simplify data preparation for enhanced .

It automatically collects granular across your digital platforms and unifies scattered sources into tidy datasets—cleaning, processing, and structuring data behind the scenes. Functional transformations, like and , are generated, making it easier to understand and use your data.

Amplitude also handles missing values, filters unwanted data, establishes a , and continuously cleans and preps billions of data points to give you a clearer picture of your business.

By automating weighty data preparation tasks, Amplitude enables the effortless transformation of raw data into actionable insights.

Level up your data-backed strategies and .