Data preparation – its applications and how it works
Good data preparation allows for efficient data analysis, limits errors and inaccuracies that can occur to data during processing, and makes all processed data more accessible to users. It’s also gotten easier with new tools that enable any user to cleanse and qualify data on their own.
What is data preparation?
Data preparation is the process of cleaning and transforming raw data prior to processing and analysis. It is an important step prior to processing and often involves reformatting data, making corrections to data, and combining datasets to enrich data.
Data preparation is often a lengthy undertaking for data engineers or business users, but it is essential as a prerequisite to put data in context in order to turn it into insights and eliminate bias resulting from poor data quality.
For example, the data preparation process usually includes standardizing data formats, enriching source data, and/or removing outliers.
Benefits of data preparation in the cloud
76% of data scientists say that data preparation is the worst part of their job, but efficient, accurate business decisions can only be made with clean data. Data preparation helps:
- Fix errors quickly — Data preparation helps catch errors before processing. After data has been removed from its original source, these errors become more difficult to understand and correct.
- Produce top-quality data — Cleaning and reformatting datasets ensures that all data used in analysis will be of high quality.
- Make better business decisions — Higher-quality data that can be processed and analyzed more quickly and efficiently leads to more timely, efficient, better-quality business decisions.
Additionally, as data and data processes move to the cloud, data preparation moves with it for even greater benefits, such as:
- Superior scalability — Cloud data preparation can grow at the pace of the business. Enterprises don’t have to worry about the underlying infrastructure or try to anticipate their evolutions.
- Future proof — Cloud data preparation upgrades automatically so that new capabilities or problem fixes can be turned on as soon as they are released. This allows organizations to stay ahead of the innovation curve without delays and added costs.
- Accelerated data usage and collaboration — Doing data prep in the cloud means it is always on, doesn’t require any technical installation, and lets teams collaborate on the work for faster results.
Additionally, a good, cloud-native data preparation tool will offer other benefits (like an intuitive and simple-to-use GUI) for easier and more efficient preparation.
Data preparation steps
The specifics of the data preparation process vary by industry, organization, and need, but the workflow remains largely the same.
1. Gather data
The data preparation process begins with finding the right data. This can come from an existing data catalog or data sources can be added ad-hoc.
2. Discover and assess data
After collecting the data, it is important to discover each dataset. This step is about getting to know the data and understanding what has to be done before the data becomes useful in a particular context.
Discovery is a big task, but Talend’s data preparation platform offers visualization tools which help users profile and browse their data.
3. Cleanse and validate data
Cleaning up the data is traditionally the most time-consuming part of the data preparation process, but it’s crucial for removing faulty data and filling in gaps. Important tasks here include:
- Removing extraneous data and outliers
- Filling in missing values
- Conforming data to a standardized pattern
- Masking private or sensitive data entries
Once data has been cleansed, it must be validated by testing for errors in the data preparation process up to this point. Often, an error in the system will become apparent during this validation step and will need to be resolved before moving forward.
4. Transform and enrich data
Data transformation is the process of updating the format or value entries in order to reach a well-defined outcome, or to make the data more easily understood by a wider audience. Enriching data refers to adding and connecting data with other related information to provide deeper insights.
5. Store data
Once prepared, the data can be stored or channeled into a third party application — such as a business intelligence tool — clearing the way for processing and analysis to take place.
Self-service data preparation tools
Data preparation is a very important process, but it also requires an intense investment of resources. Data scientists and data analysts report that 80% of their time is spent doing data prep, rather than analysis.
Does your data team have time for thorough data preparation? What about organizations that don’t have a team of data scientists or data analysts at all?
That’s where self-service data preparation tools like Talend Data Preparation come in. Cloud-native platforms with machine learning capabilities simplify the data preparation process. This means that data scientists and business analysts can focus on analyzing data instead of just cleaning it.
But it also allows business professionals who may lack advanced IT skills to run the process themselves. This makes data preparation more of a team sport rather than wasting valuable resources and cycles with IT teams.
To get the best value out of a self-service data preparation tool, look for a platform with:
- Data access and discovery from any datasets — from Excel and CSV files to data warehouses, data lakes, and cloud apps such as Salesforce.com
- Cleansing and enrichment functions
- Auto-discovery, standardization, profiling, smart suggestions, and data visualization
- Export functions to files (Excel, Cloud, Tableau, etc.) together with controlled export to data warehouses and enterprise applications
- Shareable data preparations and datasets
- Design and productivity features like automatic documentation, versioning, and operationalizing into ETL processes
The future of data preparation
Initially focused on analytics, data preparation has evolved to address a much broader set of use cases and and is applicable to a larger range of users.
Although it improves the personal productivity of whoever uses it, it has evolved into an enterprise tool that fosters collaboration between IT professionals, data experts, and business users.
And with the growing popularity of machine learning models and machine learning algorithms, having high-quality, well-prepared data is crucial, especially as more processes involve automation, and human intervention and oversight may exist along fewer points in data pipelines.
Getting started with data preparation
Data preparation creates higher-quality data for data science, analysis, and other data management-related tasks by eradicating errors and normalizing raw data before it is processed. It is critical, but takes a lot of time and might require specific skills.
With today's smart data preparation tools, however, the process has become faster and more accessible to a wider variety of users.