Data preparation is the process of cleaning and transforming raw data before processing and analysis. It is an essential step before processing and often involves reformatting data, making corrections to data, and combining data sets to enrich data.
Data preparation is often a lengthy undertaking for data professionals or business users. Still, it is essential as a prerequisite to put data in context to turn it into insights and eliminate bias resulting from poor data quality. The data preparation process usually includes standardizing data formats, enriching source data, or removing outliers.
Data consumers can only make accurate business decisions with clean data. Data preparation helps with the following.
Data Preparation Benefits
- Fixing errors quickly — Data preparation helps catch errors before processing. After data has been removed from its source, these errors become more challenging to understand and correct.
- Produce top-quality data — Cleaning and reformatting datasets ensures that all data used in the analysis will be high quality.
- Make better business decisions — Higher quality data that can be processed and analyzed more quickly and efficiently leads to more timely, efficient, and high-quality business decisions.
Additionally, as data and data processes move to the cloud, data preparation moves with it for even more significant benefits, such as the following.
Cloud Data Preparation Benefits
- Superior scalability — Cloud data preparation can grow at the pace of the business. Organizations 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 fixes can be turned on as soon as they are released. These upgrades allow 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.
The specifics of the data preparation process vary by industry, organization, and need, but the framework remains essentially the same.
Steps of Data Preparation
- Gather data: The data preparation process begins with finding the correct data. The results can come from an existing data catalog or can be added ad-hoc.
- Discover and assess data: After collecting the data, it’s essential 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.
- 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 erroneous data and filling in gaps, removing extraneous data and outliers, filling in missing values, conforming data to a standardized pattern, or 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 step and must be resolved before moving forward.
- Transform and enrich data: Transforming data is updating the format or value entries to reach a well-defined outcome or 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.
- 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.
Good data preparation allows for efficient analysis, limits errors and inaccuracies that can occur 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.
Cloud Data Security with Satori
Satori, The DataSecOps platform, gives companies the ability to enforce security policies from a single location, across all databases, data warehouses and data lakes. Such security policies can be data masking, data localization, row-level security and more.
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