DataOps: Industrializing Data and Analytics

Eckerson Group Best Practices in DataOps: How to Create Robust, Automated Data Pipelines

DataOps promises to take the pain out of managing data for reporting and analytics. In most companies, data travels a tortuous route from source systems to business users. Behind the scenes, data professionals go through gyrations to extract, ingest, move, clean, format, integrate, transform, calculate, and aggregate data before releasing it to the business community.

These “data pipelines” are inefficient and error prone: data hops across multiple systems and is processed by various software programs. Humans intervene to apply manual workarounds to fix recalcitrant transaction data that was never designed to be combined, aggregated, and analyzed by knowledge workers. Reuse and automation are scarce. Business users wait months for data sets or reports. The hidden costs of data operations are immense.

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