Logan Wilt, Chief Data Scientist, Luxoft
Business leaders already understand that AI is woven throughout our lives and is critical for competing in increasingly digitalised markets. Intentionally or not, chief data officers and executive leaders in data and engineering have found they are key pillars of their organisation’s transformation journey.
A successful CDO equips their business leaders to establish a “data-to-insights, insights-to-action” value chain where needed and worth the investment. After enabling this with modern data platforms and data culture, many can still fall short in actually utilising “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 your 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 modernising data budgets, architectures and operations.
For companies where machine learning plays a critical role in their strategy, MLOps represents a further 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 per cent to 15 per cent 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 personalisation and automation, you can future-proof your data operations into a foundation that is agile yet integrated with the core IT estate.
For more information, please click here.