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Data preparation, also sometimes called “pre-processing,” is the act of cleaning and consolidating raw data prior to using it for business analysis and machine learning.
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 and machine learning. It might not be the most celebrated of tasks, but careful data preparation is a key component of successful data analytics.
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 or model 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 business analysts and data scientists trust, understand, and ask better questions of their data, making their analyses and modeling 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 — data preparation can take up to 80% of the time spent on any analytics projects — which is why successful data preparation strategies invest in solutions built for repeatability and automation.
With self-service data preparation tools, analysts and data scientists can streamline the data preparation process to spend more time getting to valuable business insights and decisions, faster.
What Steps Are Involved in Data Preparation Processes ?
Data preparation steps can vary depending on the industry or need, but typically consists of the following:
- Acquiring data: Determining what data is needed, gathering it, and establishing consistent access to build powerful, trusted analysis
- Exploring data: Evaluating the data’s quality, examining its distribution, and analyzing the relationship between each variable to better understand how to compose an analysis (also referred to as data profiling)
- Cleansing data: Improving data quality and overall productivity by deleting unnecessary data, removing poor quality data, or fixing inaccuracies 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 data preparation 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 and data scientists can spend less time prepping and more time analyzing.
Below is a deeper look at each part of the process.
The first step in any data preparation process is acquiring the data that an analyst or data scientist will use for their analysis. It’s likely that analysts rely on others (like IT/data engineers) to obtain data for their analysis, likely from an enterprise software system or a cloud data warehouse or data lake. IT will usually deliver this data in an accessible format like an Excel document or CSV.
Modern analytic software can remove the dependency on data engineers so analysts of every skill level can tap right into trusted sources like AWS, Snowflake, SQL, Oracle, SPSS, Salesforce, Marketo, or other applications, data warehouses, or data lakes. This means analysts can acquire the critical data for their regularly-scheduled reports as well as novel analytic projects on their own.
Examining and profiling data helps analysts and data scientists understand how their analysis will begin to take shape. Users can leverage 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, as can working with analytics tools that allow for data sampling.
During this phase, analysts and data scientists 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.
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
Data comes in many shapes, sizes, and structures. Some data 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 analysts use 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 Broader Data Analysis
Solid data preparation is the foundation of valid, powerful analysis and machine learning. It’s a key piece of the broader analytics ecosystem known as analytics automation.
With data preparation and automation capabilities delivered through 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.
In this age of mind-bogglingly large datasets, a platform that can prep, process, and automate your data analytics and machine learning is a prerequisite for your business’s success.
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.
But don’t just take our word for it. Take it for a test run today with one of our Starter Kits, prebuilt analytic templates that let you start solving in seconds. Simply download the Starter Kit and plug in your data to experience different use cases for departments, industries, analytic disciplines, or tech integrations.
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.