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How analytics can unlock value for businesses across sectors

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Analytics Value

Analytics Value

Analytics Value-Being a champion requires talent, skill, expertise, hours of practice, dedication and a secret ingredient: analytics.

This is at least true for Italian motorcycle racing team Ducati Corse. When the Ducati team wanted to enhance its bike configuration testing to make it more efficient and insightful along with reducing the time, cost and effort involved in the process, they turned to Accenture.

Through an intelligent analytics solution which combined Artificial Intelligence and Internet of Things (IoT), Accenture created a mobile application that could simulate and monitor a motorbike’s performance under varied track and weather conditions. The resultant insights are helping them improve their performance and race ahead of competitors.

AI without Analytics is not as smart

Today, Analytics plays a significant role across different sectors because, as they say, “AI is only as smart as the insights that fuel it.”

In a report “AI Momentum, Maturity and Models for Success” by SAS, Accenture Applied Intelligence and Intel with Forbes inputs, most AI leaders who were surveyed said analytics and AI are inextricable from one another. Among survey respondents, 79 percent of companies that reported having experienced real success after deploying AI-based technologies, added that analytics has played a “major” role in this.

In an increasingly connected world, where new technologies are leading the way, analytics is an essential ingredient to make enterprises more intelligent by unlocking valuable insights. Now more than ever, companies need actionable insights to improve performance, increase efficiency and gain competitive advantage.

By applying analytics to the millions of data points they already have, these companies can make better business decisions at a faster pace. In turn, this helps reduce costs and risks.

Broadly, analytics can be classified as:

Descriptive Analytics: Offers similar data points with a common classification that explain what happened

Diagnostic Analytics: Runs statistical models or correlations to explain what happened

Predictive Analytics: Looks at current and past data to find trends and patterns to help forecast the probability of the situation occurring again in future; and

Prescriptive Analytics: Suggests decisions, actions and implications from predictive models to improve decision-making.

All of these are incrementally more sophisticated and complex, and with each step, the value delivered by that type of analytics goes up commensurately.

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Article Credit: YS

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