“Business success today, and sustained survival, relies ever more on the deft and accurate leverage of data. Both AI and machine learning (ML) outcomes depend heavily on the quality, reliability and relevance of data leveraged in training models.
Yet while many organizations have a wealth of existing data, they struggle to manage that data consistently so that it is appropriate and available for use.
DataOps methodology, which is the application of more agile and automated approaches toward data management to support data-driven business outcomes, seeks specifically to provide the fundamental underpinning of reliable data flow throughout an organization. So, while analytics and data science tooling are glamorous, it is important to remember that these technologies depend heavily on a solid foundation of ongoing data management. With this in mind, organizations are focusing their efforts on DataOps as a key supporter of data-driven business outcomes — with AI and analytics outcomes emphasized.”