Embracing AI through data-first modernisation

Organisations around the world are preparing for an artificial intelligence (AI)-fuelled future that will add business value and edge them closer to their goals. In an effort to position Australia as a global leader in AI technology, the Australian Government plans to strengthen the nation’s capabilities through the $124.1 million Artificial Intelligence Action Plan. Considering the value of AI for businesses, PwC recently predicted that 45% of total economic gains by 2030 will come from product enhancements, envisaging AI will drive greater product variety, increased personalisation and affordability.

There is no doubt that the next wave of technology, driven by greater automation, will rely on data more than any preceding era. To take full advantage of these advancements, data must be well understood, continually analysed for relevance, strategically located where it can add value and tightly regulated for compliance.

If organisations downplay the importance of any of these considerations, they are potentially building their AI future on pillars of sand.

What companies can do to get the most out of AI

1. Consider data as the foundation-stone of AI

Data, and how it is managed, is ultimately the ‘foundation stone’ for AI-based initiatives. Thus, organisations must take great pains to ensure data quality, governance and a modern data platform is the base requirement to drive maximum value from data. While many organisations are investing heavily in big data, and exploring the nuances of AI, machine learning (ML) and deep learning, only a few have the expertise to really leverage these tools.

Software development, having exclusively been a human’s job in the past, is now increasingly becoming the work of ML. ML meticulously selects, consumes and analyses data in a recurrent cycle. Human programmers have become ‘data producers’, curating and monitoring data produced by machines, ensuring consistent quality of input.

This would be a straightforward task were it not for the fact that, during the digital era, there has been an explosion of data — collected and stored everywhere with little planning or structure — much of it poorly governed.

The challenge from inefficient management of data is common. Research from McKinsey shows that 72% of leading Australian organisations noted managing data as being among the top barriers to reaching primary objectives in data and analytics.

2. Cultivate a data-first mindset

Data lakes amassed when organisations have been preoccupied with ‘infrastructure-first transformation’ initiatives need to be carefully evaluated. While digitising business processes, relieving the burden of siloed multi-generational IT and driving cloud-first mandates are useful, it will only get organisations so far on the transformation continuum.

To prepare for an AI-led future, organisations need to mobilise operations around data-centric value creation. Fostering a data-first mindset when approaching technology and investment decisions can be an important step forward. It can help drive customer loyalty through hyper-personalised digital solutions and improve predictive capabilities to futureproof their business.

Organisations with a data-first ethos are a step closer to fully embracing the digital ‘now’ and preparing to capitalise on the AI-powered digital ‘next’.

3. Address the challenge

There is evidence to suggest a blind spot when it comes to data in the AI context. Many organisations focus too heavily on fine-tuning their computational models in their pursuit of quick wins. However, AI success is not about tweaking and recalibrating models; it’s about continually tweaking data.

As data-centric AI evolves, so too should relevant data management disciplines, techniques and skills. These include data quality, data integration and data governance, which are foundational capabilities for scaling AI. Data management activities do not end with the development of the AI model. They have to be kept up with, to combat the triple-threat of bias, mislabelling and poor selection of data.

One way to support this is through embracing cloud-agnostic digital services platforms, which put more control into the hands of data producers and curators as they build intelligent systems. By addressing the compliance considerations that exist for critical datasets, we can gain frictionless access to the data we need, supporting better integration and improved governance.

4. Plan ahead

To ensure that AI programs are a success from the outset, it is important to define the appropriate formats and tools for AI-centric data as early as possible, preventing the need to reconcile multiple data approaches as AI scales.

This will put organisations in a strong position to leverage an ecosystem of AI-centric data management tools that combine both traditional and new capabilities to prepare the enterprise for success in the era of decision intelligence.

With the correct technology, companies can maximise the potential of their data and prepare for an AI-fuelled future.

Image credit: iStock.com/bymuratdeniz