Learn what data preparation is, why it is necessary, how it runs, and how to optimize it.
Think of a data professional (data scientist/data engineer/business analyst/…), and guess what they do all day. Design big data algorithms? Build state-of-the-art, scalable pipelines? Discover insights that drive business growth?
Data professionals spend over 40% of their time preparing data before they even start using it for their job.
The data preparation process is the most time-consuming task in a data operative’s schedule.
And yet managers spend very little time optimizing the biggest time-eater of their workforce.
In this article, we’ll look at what data preparation is, why it is necessary, how it runs, and how to optimize and streamline it to free more time for revenue-generating tasks.
Data preparation, also called preprocessing, is the process of collecting, cleaning, enriching, and storing data to make it available for business and analytical initiatives.
The data prep workflow gets data ready for multiple use cases:
Data preparation is the prerequisite step for any data product and initiative. But how does data prep look in practice?
The data preparation workflow has 7 steps. Let’s go over each step and the best practices to complete each one.
Data preparation starts by extracting raw data from its data sources. Data collection can be hard because each source of data has its own logic and needs to be handled in a different way.
For example, collecting advertising data from Facebook Ads will require a different infrastructural design than collecting Excel reports on revenue performance from your accounting subsidiaries.
Best practice: Use devoted tools that take care of data collection for you.
Data preparation tools automate the heavy lifting of data extraction, so you don’t have to worry about the technical aspects of data collection (like changing API endpoints, pagination, retrying at failures, etc.). For example, in Keboola, you can collect new data by configuring Extractors in a couple of clicks.
It is really that simple:
This is just the first step. You can build the entire data pipeline in Keboola by simply adding new components for each step of the data preparation workflow.
Data cleaning, also known as data transformation or data wrangling, is the process of changing the raw data into a usable form. When data is extracted, it is rarely ready to be used as it is.
For example, unstructured data like video files or images need to be re-coded into a machine-readable language so we can process them.
But even with structured data (think CSV or Excel, data that looks like a table), you need to transform itto make it usable:
Best practice: Data cleaning can be challenging for business users who don’t know how to use SQL or Python to clean data. Pick a data preparation tool that offers no-code transformations, so business users can prepare their own datasets without needing to wait on the IT or data department.
For example, Keboola offers both no-code and low-code transformations, helping both data professionals and business users to self-service their needs. Engineers and business users can collaborate side-by-side on the same data sets by simply switching between the low-code and no-code tools.
Download: Get your data ready for analysis with Keboola’s Data cleaning checklist.
Enrichment is the process of adding new data to your existing datasets, to bring more information to existing features for your analyses. The additional data helps you understand existing data better or build new features that support your data-driven decisions and products.
There are three ways to enrich data:
Best practice: Keep data enrichment and data collection separate.
Enrichment is an added process that can succeed or fail irrespective of the data collection pipeline. By separating the two processes, you lower the chances of both failing. And if one does fail you, the bug resolution process will be much easier.
Before you ingest data into your systems, validate it. Validation is an integral part of data quality assurance.
There are two ways to validate data:
Best practice: Keep it simple. Data validation can be an all-consuming task. Make sure to do the necessary checks on your data, but don’t over-engineer your solutions.
Store the prepared data where it is going to be consumed. This can be a data lake, a data warehouse, a BI tool, or even your app if you use data for customer-facing features.
Best practice: Rely on tools that do the heavy lifting for you. Data loading (technical expression for storing data) abilities differ between tools:
Not all tools offer all three storage paradigms. So choose the tool that answers your storage needs. Or opt for tools like Keboola, which offer all three. Extra benefit? Keboola provisions a Snowflake DWH for you if you don’t have one.
To be able to share and collaborate on the same datasets (and therefore avoid long explanation meetings or re-doing someone else’s work), you need to document them.
When documenting data, we define:
Best practice: Document data in the same location where you produce and consume it. The most common mistake is documenting data separately from the data processes. This causes misalignment issues (data changes, the documentation doesn’t), discoverability issues (“Where is that page which explains my table?”), and maintenance issues (increases room for errors when changing data/documentation).
The best way to document data is to use features like the Data Catalog, which keeps data explanations tightly coupled with the data itself and can be easily shared alongside the data you share.
Once you set up the data prep workflow, automate it. Unless you’ll only need a dataset once (low chances), make your life easier by automating end-to-end all the preceding 6 steps.
Automation helps you streamline your work, lowers the chances of manual errors, and allows you to establish templates that can be reused and shared by others.
Best practices: Make data pipelines observable. Monitor your data pipelines to make sure everything is going as expected. Make sure to set up alerts if a step in the pipeline fails. If you can, make pipelines (or choose self-serving data processing tools like Keboola) that let you introspect and dig deeper into the logs to figure out why a pipeline failed.
The seven steps described above are not a linear process. More often, we move from one step to the next and revisit the previous one for some more cleaning.
But when done correctly, data preparation offers several business advantages. In fact, we counted seven benefits of good data preparation.
When data preparation is done right, the process benefits your business in seven ways:
Unlock all 7 benefits with self-service data preparation tools like Keboola.
Keboola is a data platform as a service that is packed with features that help you prepare data:
And so much more!
Keboola is designed to automate and streamline all of your data operations, so you can cut the manual work and spend more time on strategic tasks that benefit the business.
From security to governance and machine learning, Keboola has features that support all your data operations.
Keboola helped multiple clients automate the heavy lifting of data preparation. With Keboola:
Keboola provides the infrastructural backbone that lets you digitally grow and transform with data.
Curious about how you can streamline and automate data processing with Keboola?
Let’s jump on a quick call to discuss how to make your data operations easier.