Should CDOs Acting as Pillars of Strategy Consider DataOps?

Luxoft is a Business Reporter client.

Business leaders understand that AI is already woven throughout our lives and is critical for competing in increasingly digitalized markets. Intentionally or not, chief data officers (CDOs) and executive leaders in data and engineering are key pillars of their organization’s transformation journey.

A successful CDO equips business leaders to establish a “data-to-insights, insights-to-action” value chain where it is needed and worth the investment. However, after enabling this with modern data platforms and data culture, many still fall short in actually utilizing “data as an asset.” A key reason for this is the lack of standards and CI/CD practices required to make new value from data and analytics clearly defined and sustainable.

So how might DataOps overcome these challenges? Whereas standard DevOps may function to break up silos between software development and operations, DataOps practices break up silos between data scientists, data stewards and the business stakeholders that rely on their output. An expertly crafted production and deployment pipeline for the core building blocks of data operations can give business leaders the feedback and visibility needed to make rapid and informed strategic decisions.

This enables executives to shift from a mindset of “We have problems with data” to “We have problems with decision-making; what role does data play?” This approach to building a more rigorous data strategy can help manage the complexity of modernizing data budgets, architectures and operations.

For companies where machine learning plays a critical role in their strategy, MLOps represents a next level of modern data operations, designed to break down silos between data scientists and machine learning engineers. MLOps leverages and enriches DevOps principles to address the specific challenges introduced by the complex interlink of code, data and machine learning models that drive AI applications.

When we help customers establish MLOps capabilities, it is never simply about establishing new capabilities for machine learning; it is always about validating how enhanced collaboration between machine learning engineers and data scientists can play a critical role in driving business outcomes. These could be, for example:

• Up to 25x faster break-even time from use case investments in machine learning
• Direct mapping of machine learning operations to key business metrics, providing quantifiable performance data on machine learning investments
• Tracking and improving the ability to solve novel problems with AI
• Reduced cost of AI operations
• 10x more robust models and 5%–15% increase in profit

For CDOs and business leaders at the intersection of data strategy and corporate strategy, the implementation of DataOps can be remarkably accelerating and illuminating. If you can achieve reduced deployment times for system or platform enhancements, visibility into investments in data strategy and a resilient data culture supported by personalization and automation, you can future-proof your data operations into a foundation that is agile yet integrated with the core IT estate.

—Industry view from Luxoft

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This article originally appeared on Business Reporter. Image credit: iStock id625686298