As AI enthusiasm skyrockets, with 79% of corporate strategists seeing AI and analytics as critical to their success over the next two years, one major hurdle remains: data health and readiness. In fact, 70% of our discussions with CDAOs revolve around this challenge.
Assessing Data Health and Readiness
To gauge data health and readiness for AI, CDAOs must evaluate:
- Foundational Data Capabilities: Efficient data storage, ingestion, processing, security, governance, and cataloguing form the basis for a sound, productive AI implementation.
- Data Consumption Capabilities: Business intelligence, AI use cases, platforms for AI/ML, and data monetization dictate how data is consumed in AI models.
- Organizational Construct: Alignment of data and AI strategy, organizational structure, and data literacy ensure right, ethical handling of AI solutions in any business context.
If any of the above referenced aspects are lacking or incomplete, it is advised that these shortcomings be addressed first, before launching into an AI-based solution on which critical business outcomes depend.
Enhance Results and Minimize Waste with a Holistic Approach
A framework that offers a holistic approach to gauge data health, covering foundational data capabilities, data consumption capabilities, and organizational construct, will ultimately optimize impact and minimize waste in any AI initiative. By starting with a clear, holistic framework, teams can focus conversations on addressing key areas of concern. Benchmarking data health across each facet of the plan becomes easier. Then, once the initiative is launched, useful feedback can be pinpointed to effectively assess progress across the areas of concern.
These structured conversations and workshops can be used to kickstart any data strategy program. Input from these discussions can enlighten organizations on the key success factors of data health and readiness for AI and establish best practices. For optimal results, involve stakeholders from both technology and business sectors. Gather their input and buy-in to formulate a practical roadmap from initial concept to ultimate deployment of prioritized initiatives.
Ride Data Health and Readiness to a Winning AI Deployment
It takes a solid foundation of data health and readiness to ride any AI-based deployment toward winning results. To get there, employ a holistic data readiness strategy, built on a practical framework that gauges and addresses data health up front.
Account for business requirements from a consumption standpoint, to align your AI journey with its intended stakeholder objectives. This will minimize stop-start problems on your way to scaling AI going forward. Moreover, by getting your organizational construct right up front, you can then rely on quality data analytics to steer your AI journey and achieve long-term success.
Deepa Behl Wipro's Data Governance Practice Head
Deepa Behl, Wipro's Data Governance Practice Head, is a technology leader with experience in various aspects of Data Management, including Data Integration, Data Quality and Observability, Data Catalog, Data Retention and Data Migration. With over 20 years of experience, she excels at strategic planning, conceptualizing, developing, and implementing solutions in Data Management that enable business growth and improve operational efficiency. In her current role, Deepa is focused on developing and executing data management strategies that align with and fuel customers’ business objectives.
