What is Data Preparation, and why is it an increasingly important part of the analytic process?
Data preparation enables business users and analysts to overcome traditional data challenges by simplifying and automating data acquisition, cleansing and blending processes. The technology lets users quickly access, manipulate, enrich, blend and reconcile disparate data from a variety of sources, whether structured or unstructured. Data can then be prepared for analysis in a fraction of the time it takes using spreadsheets and other manually-intensive measures.
This means that users gain fast access to not only the right data, but all of the data, which is crucial to gaining a holistic view of the business. While it gives users the autonomy they require to do their jobs, it also satisfies IT’s need for security and compliance by incorporating governance capabilities like data masking, data retention, data lineage and role-based permissions. Data preparation tools reduce the time it takes for data workers to find and patterns in data and ultimately generate insight.
Data preparation is widely regarded as one of the most time-consuming practices in the analytic process. The popularity of this software category has grown as a result of self-service; enabling business and data analysts to move beyond waiting for IT to provide curated data for analytics. Modern tools go even one step further by leveraging machine learning algorithms that can steer users in the right direction or automate mundane data tasks.
Data preparation software is made up of several key components like data ingestion and profiling, data cataloging, metadata management, data modeling, and support for data quality. The marketplace for these software tools is unique because it encompasses providers, technologies and products from horizontal markets. Many providers include data prep capabilities inside their existing analytics and BI, data science, and data integration solutions. Other vendors offer more specialized, stand-alone tools products with independent licensing.
When a data preparation use case is general in nature, we recommend evaluating stand-alone software. For the more complex or big data jobs, a platform is likely to be the better choice.
Evaluating data preparation software? Start here with this directory of the most popular tools and software to consider.
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