Advancing AI: The Emergence of a Modern Information Lifecycle

I appreciate the zen that envelops me when I get lost in a good puzzle. But, truth be told, that ‘zen’ doesn’t happen until I’m attuned to the puzzle’s colours, shapes, and patterns. Once I am, the border falls into place, colours get grouped, images form, and I eventually become familiar with the intricate details and shapes of the remaining pieces. 

Although less complex than the “4 Vs” of big data (velocity, veracity, volume, and variety), orienting to the variety and volume of a challenging puzzle is similar to what CIOs face with information management. 

As data skyrockets—especially unstructured data—organisations need an intentional plan and ongoing approach to protect, manage, and optimise data for monetisation. Once focused solely on reducing search and retrieval times, information lifecycle management (ILM) is now critical to workflow automation, identifying and tracking performance metrics, and harnessing the burgeoning potential of AI.

Operationalising data to drive revenue

CIOs report that their roles are rising in importance and impact. The vast majority (85%) of CIOs say they are becoming organisational changemakers. And 68% of CIOs state that creating revenue-generating initiatives is now among their job responsibilities. What’s changed?

It’s now clear that CIOs hold the keys to unlocking the untapped value of data–and all assets– and by doing so, can catalyse a “make-or-break” business advantage. CIOs can best navigate by understanding and embracing the modern information lifecycle that is emerging from the chaos of the digital age. 

Tomorrow’s market leaders will recognise how to curate and enrich the value of all assets, which will train AI models, generate insights, and drive automation. CIOs must recognise this paradigm so that all relevant, timely, and accurate data trapped in physical objects and formats is proactively identified, enriched, operationalised, and accessible to AI-driven automation, generative AI, and immersive experiences.

Changing this paradigm means re-evaluating the generally linear and static information lifecycle of old, which is, roughly, “create, store, use, share, archive or destroy.” In contrast, the new lifecycle is distinctly digital, agile, and recursive. When you enrich, structure, and operationalise data on the way in, there is no limit to the efficiencies, value streams, and insights unlocked by readily available powerful cloud platform tools.

A modern information lifecycle management approach

Today’s ILM  approach recognises the enterprise value of all digitised and enriched assets, avoiding the habituated, narrow reliance on traditional structured data. The ways modern data is used, processed, and analysed are continuously evolving as machine learning technology becomes better at these tasks. With constant advances in intelligent document processing, compute power, DevOps workflows, and AI, the content, context, and value of unstructured data is rapidly increasing. 

A modern ILM approach helps CIOs and their teams align processes to business objectives and regulatory requirements. And it does so while accommodating the increasing emphasis on security, governance, and environmental sustainability. Here is a high-level overview of the ILM steps and structure.