Big Data and AI are now near-universally accepted as important pillars of business growth. They have managed to capture the attention and imagination of senior business decision-makers, grabbing a seat at the Board and C-Suite levels.

It is difficult to imagine today, but there was a time a few years back when data analysts were relegated to the hidden recesses of most corporations. Before information technology (IT) emerged as a critical business function, data was considered something that firms filed away in vaults to comply with regulators and not a business asset that must be mined to unlock critical business insights. Firms perceived data as the purview of those who were sometimes derisively referred to as data geeks or “propeller heads.” They overlooked the potential of using big data opportunities to guide business planning and strategies.

Not long ago, data analysts were rechristened as data scientists, data engineers, and data architects, with new connotations – architect, engineer, and scientist – that reflected achievement, technical competency, distinction, and respect. This change in perception, with the elevation of the data professional, has occurred within a single decade. Today, as data has become the lifeblood of firms, analytics has become an unquestioned mantra to beat the competition, as firms have now appointed Chief Data Officers to occupy the C-Suite.

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While these are the visible manifestations of the recognition and acknowledgment of data as a critical business asset to insight, measurement, and differentiation, it is interesting to note the way data has gained acceptance at the Board level and within the C-Suite. Once data experts learned to communicate and translate complex technical or scientific jargon in terms of basic outcomes – win, lose, grow, fast, outsell – the business value and benefit of data gained appreciation and traction.

Organizations today embrace Artificial Intelligence (AI) with similar zeal. AI has been around for many decades in various forms – natural language, robotic process automation machine learning, and deep learning. What has changed is the coalescing of the various forms of computer-assisted activities. This, along with the massive and inexpensive computing power, defines AI as a capability that lies at the core of business transformation for the coming years.

It does not matter that the terms Big Data and AI may be misused or used with significant technical imprecision. What matters is that Big Data and AI have managed to capture the attention and imagination of senior business decision-makers at the Board and C-Suite levels. As a result, organizations have made significant commitments to elevate these activities, giving them business primacy – through Big Data and AI Labs, Centers of Excellence, and Moonshot Initiatives.

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