Gartner believes it’s time for a shift in the direction data-hungry, AI-powered organizations are taking. Its latest report, “Top Trends in Data and Analytics for 2021: From Big to Small and Wide Data,” states that by 2025, almost three-quarters (70 percent) of organizations will shift their focus from big data, to small and wide data.
That way they’ll be able to provide more context for analytics, and make AI less hungry for data.
Gartner argues that big data is just too cumbersome. AI-powered applications are too data hungry, yet plenty of data businesses are gathering is out of context and often becomes obsolete faster than it can be used.
“Disruptions such as the Covid-19 pandemic is causing historical data that reflects past conditions to quickly become obsolete, which is breaking many production AI and machine learning (ML) models,” said Jim Hare, distinguished research vice president at Gartner. “In addition, decision making by humans and AI has become more complex and demanding, and overly reliant on data hungry deep learning approaches.”
So instead of big data, data and analytics teams will shift their attention towards techniques known as “small data” and “wide data”. Hare explains their synergy as “using available data more effectively, either by reducing the required volume or by extracting more value from unstructured, diverse data sources.”
Small data, a technique where businesses try to extract more insights from less data, includes time-series analysis techniques, or few-shot learning, synthetic data or self-supervised learning.
Wide data refers to the analysis of various small and large, unstructured, and structured data sources. It aims to find links between data sources and different data formats, including tabular, text, image, video, audio, voice, temperature, or even smell and vibration.
“Both approaches facilitate more robust analytics and AI, reducing an organization’s dependency on big data and enabling a richer, more complete situational awareness or 360-degree view,” said Mr Hare.
“D&A leaders apply both techniques to address challenges such as low availability of training data or developing more robust models by using a wider variety of data.”
Retail, Gartner believes, stands to gain a lot from this approach, as it can forecast demand or improve customer experiences. Physical security and fraud detection have also been mentioned as areas that can be improved.