Which of the following is the first step in the data preparation process?

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 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.

Get Started With Data Preparation

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 replicable and accessible to the rest of your business.

The Alteryx platform empowers analysts, 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.

In this age of mind-bogglingly large datasets, a platform that can prep, process, and automate your data analytics is a prerequisite for your business’s success.

The Alteryx end-to-end analytics platform makes data preparation and analysis intuitive, efficient, and enjoyable. Beyond the unmatched volume of data preparation building blocks, Alteryx also makes it faster and easier than ever before to document, share, and scale your critical data preparation work.

Which is the first step in the data preparation process?

Which of the following is not a step of data preparation process?

What is data preparation and processing?

Which of the following is the last stage of the data preparation process?