Building an internal dataops team: 7 considerations for success

Advanced data analytics is transforming the way companies make decisions and respond to market changes. Here's what that means for CIOs looking to build internal dataops teams.

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With most businesses determined to leverage data in smarter and more profitable ways, it’s no wonder dataops is gaining momentum. The growing use of machine learning to manage tasks, from creating predictive models and deepening insights into consumer behavior to detecting and managing cyberthreats, also adds to the dataops incentive. Businesses that can move to rapid autonomous or semi-autonomous examinations of sophisticated data sets will gain a strong marketplace advantage.

As businesses consider the challenges of a more mature and robust analytics practice, some are turning to dataops-as-a-service—outsourcing the work of harnessing company data. While this approach can address some talent issues and speed up your data analytics journey, there are also risks: Without having a clear understanding of the business drivers behind data analytics, outsourcing your data needs may not deliver the data intelligence you need. And adding third and even fourth parties to the data ingestion and analysis process can increase data protection risks.

Your other option: build an internal dataops team.

This approach also has its challenges, and requires more than finding the right team members or mimicking a good devops initiative. But the payoff is worth the effort.

A dataops initiative done well will not only make a business more intelligent and competitive, it can also enhance data accuracy and reduce product defects by combining data and development input in one place. 

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