Gartner Market Guide for Data Preparation Tools 2019

Data preparation tools have matured from initially being self-service-focused to now supporting data integration, analytics and data science use cases in production. Data and analytics leaders must use this research to understand the dynamics of and popular vendors in this rapidly evolving market.

Key Findings

• The market for data preparation tools has evolved from being able to support only self-service use cases. Modern data preparation tools now enable data and analytics teams to build agile datasets at an enterprise scale, for a range of distributed content authors.
• The market for data preparation tools remains crowded and complex. The choices range from stand-alone specialists to vendors that embed data preparation — as a key capability — into their broader analytics/BI, data science or data integration tools.
• While most data preparation tool capabilities have been maturing at a steady state, organizations continue to cite “operationalization” — the ability to promote self-service models to production — and “hybrid cloud deployment support” as the two biggest inhibitors to enterprisewide adoption.

Recommendations

• For data and analytics leaders focused on data management solutions and analytics strategies:
• Create a deployment strategy for data preparation that will enhance user understanding of data, reduce data preparation efforts and increase agility. Evaluate tools based on capabilities such as connectivity, machine-learning-based automation and data cataloging, to improve searchability and trust across distributed data assets.
• Evaluate stand-alone data preparation tools when your use case is a general-purpose one needing integration of data for different analytics/BI and data science tools. Contrastively, evaluate the embedded data preparation capability of incumbent tools if you need data preparation only in the context of those tools, platforms or ecosystems.
• Evaluate data preparation tools for their ability to scale from self-service models to enterprise-level projects. Give preference to tools that can coexist with other data management tools (such as data quality or data governance) and have the ability to capture, analyze and share metadata (and lineage) with them, to ensure security, governance and compliance.

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