Everything You Need To Know About Data Preparation Solutions (FAQs)
What Is Data Preparation?
Data preparation is the process of cleaning and organizing raw data so that it can be used for analytics. There are six steps in the data preparation process: gathering, profiling, cleaning, labelling and structuring, transforming and enriching, and validating. These steps are usually undertaken by IT, BI, and data management teams as they integrate data sets into larger repositories of data, in order to make sure that the new data is accurate, complete, and formatted in the same way as the data already in the repository. This can help ensure the consistency and accuracy of analysis and BI carried out using the data.
As well as cleaning and re-structuring data, data preparation can be used to make a dataset more informative. During the preparation process, the data is often enriched by combining it with other data sets. This adds useful context and helps remove inconsistencies. Ultimately, this enables data analysts to derive more meaningful insights from the data.
What Are Data Preparation Solutions?
Data preparation is a hugely important process for data analysts and data scientists. However, it often requires a lot of time and effort to undertake; in fact, various studies have found that data scientists and analysts spend between 45%-80% of their time on data preparation, rather than analysis*. That can be a problem for organizations that don’t have time to carry out data preparation or may not have an in-house team of data scientists.
Enter: data preparation solutions.
Data preparation solutions are cloud-native platforms that use machine learning to automate and streamline the data preparation process, so that data analysts and scientists can spend more time analyzing data, rather than just collecting and cleaning it.
But data preparation solutions aren’t just helpful for data scientists—they also enable other individuals, who may not have a background in IT or data analysis, to run the data preparation process. This saves IT time and resources, whilst making data preparation more accessible to businesses without their own team of data scientists.
*In 2015, CrowdFlower’s Data Science Report found that data preparation accounts for approximately 80% of the work of data scientists; Anaconda’s more recent 2020 State of Data Science report found that data scientists spend about 45% of their time on data preparation tasks.
How Do Data Preparation Solutions Work?
Data preparation solutions apply machine learning and automation to each stage of the data preparation process, helping data analyst teams to transform raw data quickly and effectively into useful, context-rich data that’s ready for analysis. Let’s take a look at each step.
Step One: Data Collection
The first step in the data preparation process is locating and gathering any relevant data you need. Data preparation solutions integrate with various data sources, including operational systems, data warehouses, data lakes, and applications. They then pull relevant data from these sources and collate it centrally.
Step Two: Data Profiling
Once the data has been collected, the data preparation solution profiles it—in other words, it analyzes the data using machine learning to identify patterns, relationships, inconsistencies, anomalies, and missing values. Some data preparation solutions also offer visualization tools that help users to understand at-a-glance how much work needs to be done before the data can be considered useful.
Step Three: Data Cleaning
When all the issues with the data have been identified, it’s time to fix them in a process called data cleaning. In this step, errors are corrected, missing data is filled in, outliers are removed, sensitive data is masked, and all the data is re-structured to make sure it’s all in the same consistent, readable format. Once the data has been cleaned, the user should be left with a complete and accurate data set.
Step Four: Data Labelling And Structuring
The next step in the data preparation process involves identifying unstructured data and labelling or re-organizing it so that ML algorithms and BI and analytics tools can understand it. For example, data stored in CSV files must be converted into tables, and descriptions are added to images, videos, and audio recordings.
Step Five: Data Transformation And Enrichment
As well as re-structuring the data, analysts need to transform it into a usable format to make it more easily understood and accessible. This may involve creating new fields or columns that collate data from existing ones, for example.
At this stage, the data should also be combined with any other relevant data sets that could provide useful context or deeper insight.
Step Six: Data Validation
Once the data has been cleaned, labelled, and transformed, the data preparation solution runs tests against the data to validate its consistency, accuracy, and completeness. The validated data can then be stored in a secure repository until the analyst is ready to use it or integrated directly into a third-party BI or analytics tool.
What Features Should You Look For In A Data Preparation Solution?
Data preparation solutions offer various levels of automation and technical capabilities to meet different use cases, so it’s important that, before you start comparing solutions, you consider your specific business requirement, the volume and variety of data you need to prepare, and the expertise of your team. That being said, there are some features that all strong data preparation solutions should offer:
- Data Connectivity: The solution should offer support for various data sources, such as databases, files, and cloud storage, and it should be able to connect to both structured and unstructured data.
- Integration with BI and Analytics Tools: Your chosen solution needs to be compatible with any business intelligence and analytics platforms that you’re using. It should also offer easy integration with your visualization tools for seamless analysis.
- Data Profiling: To help you understand the structure, quality, and completeness of your data, the solution should offer comprehensive data profiling capabilities. These should include identification of missing values, outliers, and anomalies.
- Data Standardization: Your solution should be able to ensure uniformity across different data elements.
- Transformation and Enrichment: To help you get the most out of your data and make more informed decisions, your solution should offer built-in transformations to manipulate and enrich data (e.g., merging, splitting, and aggregating). Depending on the level of analysis you need to do, you may also want to look out for support for custom transformations and scripting for complex data manipulations.
- Data Quality Monitoring: Your solution should monitor the quality of your data over time and offer automated alerts and notifications for data quality issues.
- Collaboration and Workflow Management: The best data preparation solutions help facilitate collaboration between multiple users working on data preparation tasks. These may include workflow management capabilities to track and version data preparation steps, and version controls that help you track changes in data and revert to previous states if needed.
- Data Lineage and Audit Trails: You should be able to track and visualize data lineage to understand the origin and transformation history of your data.
- Data Governance and Security: To help you protect sensitive data and comply with data privacy regulations, your solution should offer role-based access controls to help you manage who can access and modify data.