Data preparation is the process of cleaning, structuring and enriching raw data, including unstructured or big data. The results are consumable data assets used for business analysis projects.
In the data science community, data preparation is often called feature engineering. Although data prep and feature engineering are used interchangeably, feature engineering relies on domain-specific knowledge compared to the standard data prep process. That is because feature engineering creates “features” for specific machine learning algorithms while data prep is used to disseminate data for mass consumption.
Both data preparation and feature engineering are the most time-consuming and important processes in data mining. Having data prepared correctly improves the accuracy of the outcomes. However, data preparation activities tend to be routine, tedious, and time consuming.
There is a host of tools on the market today that provide data preparation capabilities. They are typically applications meant to streamline and operationalize the data preparation process. These tools are found in centralized IT departments, are used by Data Engineers, and are designed for batching and scheduling data pipelines rather than exploring and discovering new analytics assets.
Stand-alone data prep vendors, such as Datameer and Alteryx, have been around for many years and shape the foundation of this software market. The applications are designed for transforming complex data into consumable datasets for analytics and then creating data pipelines to consistently produce this data.
Data preparation tools are great for IT teams to make centralized, complex data consumable on a scheduled basis via data pipelines. Neebo is great for exploring and discovering new analytics assets at the business lines, not only from those centralized data pipelines but also from the data that resides everywhere else.
Neebo’s Virtual Analytics Hub allows analytics teams to find, create, collaborate, and then publish trusted analytics assets in complex hybrid landscapes. Neebo provides unified access across analytics silos, increases use of analytics assets and furthers data knowledge.
Neebo is built for ad-hoc analytics and includes key data prep capabilities so analytic professionals can quickly enrich most assets rather than relying on centralized data pipelines and procedures used in data preparation tools. With Neebo, professionals can directly:
Neebo builds trust in the analytics assets though a community of experts. Neebo works interactively with data prep outputs through virtual queries, allowing analysts to easily discover, access, and use those datasets with ease. Data preparation tools can continue to be used for data engineering purposes, producing data pipelines and robust datasets for the enterprise. End-users build on the hard work of the data engineering team – by tagging, publishing, sharing, these datasets in real time and promote greater use of these assets – all in a SaaS solution.
Neebo allows analytics teams to find, create, collaborate and publish trusted ad-hoc analytics in complex hybrid landscapes. Neebo provides unified access across analytics silos, increases use of analytics assets and furthers data knowledge.
A cooperative environment between Neebo and data preparation tools provides customers with a number of benefits: