Data Preparation

What Is Data Preparation?

Data preparation, also sometimes called “pre-processing,” is the act of cleaning and consolidating raw data prior to using it for business analysis. It might not be the most celebrated of tasks, but careful data preparation is a key component of successful data analysis.

Doing the work to properly validate, clean, and augment raw data is essential to draw accurate, meaningful insights from it. The validity and power of any business analysis produced is only as good as the data preparation done in the early stages.

Why Is Data Preparation Important?

The decisions that business leaders make are only as good as the data that supports them. Careful and comprehensive data preparation ensures analysts trust, understand, and ask better questions of their data, making their analyses more accurate and meaningful. From more meaningful data analysis comes better insights and, of course, better outcomes.

To drive the deepest level of analysis and insight, successful teams and organizations must implement a data preparation strategy that prioritizes:

• Accessibility: Anyone — regardless of skillset — should be able to access data securely from a single source of truth
• Transparency: Anyone should be able to see, audit, and refine any step in the end-to-end data preparation process that took place
• Repeatability: Data preparation is notorious for being time-consuming and repetitive, which is why successful data preparation strategies invest in solutions built for repeatability.

With the right solution in hand, analysts and teams can streamline the data preparation process, and instead, spend more time getting to valuable business insights and outcomes, faster.

What Steps Are Involved in Data Preparation Processes?

The data preparation process can vary depending on industry or need, but typically consists of the following steps:

• Acquiring data: Determining what data is needed, gathering it, and establishing consistent access to build powerful, trusted analysis
• Exploring data: Determining the data’s quality, examining its distribution, and analyzing the relationship between each variable to better understand how to compose an analysis
• Cleansing data: Improving data quality and overall productivity to craft error-proof insights
• Transforming data: Formatting, orienting, aggregating, and enriching the datasets used in an analysis to produce more meaningful insights

While data preparation processes build upon each other in a serialized fashion, it’s not always linear. The order of these steps might shift depending on the data and questions being asked. It’s common to revisit a previous step as new insights are uncovered or new data sources are integrated into the process.

The entire data preparation process can be notoriously time-intensive, iterative, and repetitive. That’s why it’s important to ensure the individual steps taken can be easily understood, repeated, revisited, and revised so analysts can spend less time prepping and more time analyzing.

Below is a deeper look at each part of the process.

Acquire Data

The first step in any data preparation process is acquiring the data that an analyst will use for their analysis. It’s likely that analysts rely on others (like IT) to obtain data for their analysis, likely from an enterprise software system or data management system. IT will usually deliver this data in an accessible format like an Excel document or CSV.

Modern analytic software can remove the dependency on a data-wrangling middleman to tap right into trusted sources like SQL, Oracle, SPSS, AWS, Snowflake, Salesforce, and Marketo. This means analysts can acquire the critical data for their regularly-scheduled reports as well as novel analytic projects on their own.

Explore Data

Examining and profiling data helps analysts understand how their analysis will begin to take shape. Analysts can utilize visual analytics and summary statistics like range, mean, and standard deviation to get an initial picture of their data. If data is too large to work with easily, segmenting it can help.

During this phase, analysts should also evaluate the quality of their dataset. Is the data complete? Are the patterns what was expected? If not, why? Analysts should discuss what they’re seeing with the owners of the data, dig into any surprises or anomalies, and consider if it’s even possible to improve the quality. While it can feel disappointing to disqualify a dataset based on poor quality, it is a wise move in the long run. Poor quality is only amplified as one moves through the data analytics processes.

Cleanse Data

During the exploration phase, analysts may notice that their data is poorly structured and in need of tidying up to improve its quality. This is where data cleansing comes into play. Cleansing data includes:

• Correcting entry errors
• Removing duplicates or outliers
• Eliminating missing data
• Masking sensitive or confidential information like names or addresses

Transform Data

Data comes in many shapes, sizes, and structures. Some is analysis-ready, while other datasets may look like a foreign language.

Transforming data to ensure that it’s in a format or structure that can answer the questions being asked of it is an essential step to creating meaningful outcomes. This will vary based on the software or language that an analysts uses for their data analysis.

A couple of common examples of data transformations are:

• Pivoting or changing the orientation of data
• Converting date formats
• Aggregating sales and performance data across time

Data Preparation for Machine Learning 

Machine learning is a type of artificial intelligence where algorithms, or models, use massive amounts of data to improve their performance. Both structured data and unstructured data are critical for training and validating machine learning algorithms that underpin any AI system or process. The rise of Big Data and cloud computing have exponentially increased the use cases and applications of AI, but having a lot of data isn’t enough to create a successful machine learning model. Raw data is hard to integrate with the cloud and machine learning models because there are still anomalies and missing values that make the data hard to use or result in inaccurate models. Building accurate and trustworthy machine learning models requires a significant amount of data preparation.

According to a survey by Anaconda, data scientists spend 45% of their time on data preparation tasks, including loading and cleaning data. With self-service data preparation tools, data scientists and citizen data scientists can automate significant portions of the data preparation process to focus their time on higher-value data-science activities.

Data Preparation in the Cloud

With the rise of cloud data storage centers, including cloud data warehouses and cloud data lakes, organizations are able to increase the accessibility and speed of their data preparation and data analytics while also leveraging the power of the cloud for improved security and governance. Historically, organizations stored their data in on-premise data centers. These physical servers limit organizations’ ability to scale their usage of data up or down on demand, cost large amounts of money to operate, and often consume vast amounts of time, especially when working with large datasets.

As data exploded, organizations needed greater data storage capabilities and faster insights. With the rise of the cloud, end users can now easily access data through powerful remote servers via the internet and scale their use of storage and processing resources on demand. This is critical for efficient data preparation and building data pipelines. However, organizations should consider the differences between cloud data warehouses and cloud data lakes when migrating to a cloud solution.

Cloud data warehouses house structured, filtered data that has already been processed and prepped for a specific purpose. This is helpful when organizations anticipate similar use cases for their data, as the processed dataset can be reused infinitely. However, after this initial data preparation, use cases become very limited. Trying to revert or reuse processed data poses a great risk as pieces of the dataset are highly likely to go missing or become altered during reversion, compromising the data’s fidelity.

Cloud data lakes, on the other hand, are large repositories for raw data that companies can use and reuse for multiple purposes. Business analysts and data scientists across the company may have vastly different use cases. Cloud data lakes offer cost-effective storage and widespread data access without the risk of losing critical information in the structuring process.

Data Preparation Within Broader Data Analysis

Solid data preparation is the foundation of valid, powerful analyses. It’s a key piece of the broader analytics ecosystem known as analytics automation.

With data preparation and automation capabilities delivered though analytics automation technology, data workers can take control of the time and mental energy they previously spent on manual prep work.

Alteryx Data Preparation Tools

A solution like the Alteryx Analytics Automation Platform can help you speed up the data preparation process — without sacrificing quality. Plus, it helps make the process more repeatable and accessible for the rest of your business. The Alteryx platform empowers data analysts, data engineers, citizen data scientists, data scientists, and IT to turn data into results. This means you can democratize data and analytics, optimize and automate processes, and upskill your workforce simultaneously. The Alteryx Analytics Automation Platform makes data preparation and analysis fast, intuitive, efficient, and enjoyable. Beyond the unmatched volume of data preparation building blocks, Alteryx also makes it easier than ever before to automate, document, share, and scale your critical data preparation work, accelerating other parts of the analytics process, including machine learning.

Data Blending Starter Kit for Alteryx

This Starter Kit will jumpstart your path to mastering data blending and automating repetitive workflow processes that blend data from diverse data sources.

• Visualize customer transactions. Blend transactions and customers to provide visual reporting insights that help you identify trends and opportunities.
• Identify non-exact matches with fuzzy matching. Learn how to enable quick fuzzy-matched blending of similar but not exactly matching data and feed it into automated workflows for real-time insights.
• Calculate ad area distribution. Blend spatial data to calculate ad area distribution, increase sales, and improve ROI.

Data Blending for Tableau Starter Kit

This Starter Kit provides analytic workflows to seamlessly integrate Alteryx with Tableau for powerful data visualization and business intelligence. This Starter Kit illustrates how to monitor account executive performance, create trade areas, and understand buyer behavior.

• Quickly prepare, blend, and enrich data with the help of hundreds of automation building blocks
• Publish your insights directly to a Tableau dashboard
• Build rich insights using geospatial, statistical, and predictive analytics on large datasets using drag-and-drop, low-code/no-code analytics
• Leverage ready-to-use business solutions including trade area and market basket analysis

Data Blending with AWS Starter Kit

This Starter Kit will jumpstart your data integration with AWS S3, Redshift, and Athena to build automated solutions and deliver faster insights, from data prep, data blending, and profiling through interactive spatial and predictive analytics.

• Quickly prepare, blend, and enrich data with the help of hundreds of automation building blocks
• PStream in/out or up/download data from your AWS S3, Redshift, or Athena instance
• Build rich insights using geospatial, statistical, and predictive analytics on large datasets using drag-and-drop, low-code/no-code analytics