The image of a snowball getting bigger and bigger as it rolls downhill usually appears in cartoons for comic effect. However, it isn’t very funny if your company is sitting at the bottom of that hill paralyzed by a deluge of data. Too many companies are unable to navigate between the demand to accelerate analytics and operational initiatives to reach business goals and the pressure to slow down to ensure compliance with evolving privacy regulations.
No wonder VentureBeat reports 87 percent of data science projects never make it into production. MIT Sloan finds 81 percent of organizations don’t understand their data because it’s still locked in silos. And a recent HBR survey reveals 69 percent of companies have yet to create a data-driven organization.
Companies that want to overcome paralysis while taking the potential for calamity seriously have prioritized data management, with many appointing a data czar or chief data officer (CDO). Still, obstacles abound, including a perceived conflict between rapid value creation and the risks associated with privacy compliance. Organizations also face resistance to change, an inability to drive collaboration across departments, and the failure to recognize mature information governance (IG) as a core business function required for the management of the organization-wide data lifecycle. Gartner predicts only half of CDOs will be successful.
As these challenges suggest, the problems organizations face have as much to do with people and processes as with technology. Here are four tips to help your company become a data-driven culture:
In providing enterprises with a methodology and set of best practices, DataOps is similar to successful operational initiatives in other industries, including, FinOPs, Marketing Operations and DevOps. DataOps borrows best practices from DevOps, data management and data governance and creates a framework for collaborating on developing and maintaining trusted data flows and pipelines across multiple stakeholders.
A key goal is to make business-ready data available fast by ensuring data is secure, high quality, compliant and easily accessible to business users and data scientists. To do this, specific DataOps strategies include:
DataOps achieves these goals by utilizing artificial intelligence (AI) and machine learning (ML) to streamline the data pipeline and automate the following processes, which are still currently manual and very labor-intensive for many organizations:
DataOps creates value by shrinking the time between when data from a variety of sources can be accessed and when business users and data science experts can confidently consume it. This allows companies to quickly launch new analytics initiatives with reduced risk of regulatory noncompliance and improve operations faster by rapidly making trusted data available. Today, CGOC sees this growing in importance across industries and will look to provide more resources and opportunities for education and sharing, including case studies. For example, companies that have launched DataOps initiatives report some impressive results, such as an 85 percent reduction in the time to create a business glossary, a 90 percent reduction in the time to discover meta data and assign terms, and 200,000 technical assets discovered across multiple clouds in less than five minutes. More details to follow.
If you’re already moving toward a DataOps framework and have anecdotes or resources you think the membership can benefit from, we’d love to hear from you. And as always, share your questions and experiences on twitter @CGOC_Council or LinkedIn.
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