Data operations (DataOps)

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What is it?

DataOps (Data Operations) is an agile approach to data management that combines:

• DevOps principles.
• Agile development.
• Data management practices.

Its main goal is to improve data quality, speed, and reliability across the data lifecycle. DataOps enhances collaboration between data-focused teams such as:

• Data scientists.
• Analysts.
• Operations teams.

This is achieved through:

• Automation.
• Continuous integration.
• Standardised processes.

The ultimate objective of DataOps is to ensure that data is:

• Accurate, free from errors.
• Accessible, available to those who need it.
• Useful, ready for decision-making.

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Why filter companies by their usage?

Segmenting by DataOps usage allows you to tailor commercial strategies:

• Advanced companies: Help them optimise existing processes.
• Companies without DataOps practices: Guide them towards automation and optimisation of data operations.

Companies that do use it

These companies have already adopted modern data management practices and are likely interested in:

• Improvements to optimise existing workflows.
• Integrations with analytics tools or cloud architectures.
• Optimisation of more efficient data pipelines.

Your sales team could offer:

• Data pipeline optimisation.
• Performance analysis to identify bottlenecks.
• Consulting on cloud-based data architecture adoption.

Companies that do not use it

These companies may be managing data manually or in a decentralised way, making it difficult to:

• Automate processes.
• Scale data operations.

Your sales team could offer:

• Initial consulting to assess needs and propose a solution.
• Data automation tools to simplify workflows.
• Training in DataOps to implement efficient, standardised processes.

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Examples