Here are 4 ways data analytics will revolutionise business outcomes
- Supply chain analytics has become a top focus area for
businessleaders to effectively forecast future demand.
- Healthcare providers leverage
data analyticsto predict disease outcomes and treatment plans.
- Data science and analytics provide major tailwinds for the global efforts to address climate change issues.
Across sectors, data analytics programs focus largely on improving customer experience, product optimisation, risk management and so on. Here are a few disruptive use cases of data analytics that organisations are actively exploring to get the most out of their data.
The global pandemic revealed some glaring supply-chain inefficiencies that impacted businesses around the world. A Mckinsey study says that before the Omicron variant emerged, supply-chain challenges briefly replaced the pandemic as the top risk. The silver lining to all this – supply-chain conversations have gained prominence, becoming a board room priority for businesses of all nature. Modernisation and digitization of supply chains were identified as immediate concerns.
Optimising supply chains with analytics and AI is an inevitable step in this transition to achieving better efficiencies and productivity. With data analytics, businesses can
have dynamic logistic systems and real-time delivery controls. Supply-chain analytics has become a top focus area for business leaders to effectively forecast future demand, identify inefficiencies and boost innovation. Predictive analytics help organisations build agile supply- chain practices that help them to be in control. Advanced analytics (AA), artificial intelligence (AI) and data science are predicted to further
For any healthcare provider, the transition to data-driven processes and systems is not a choice but a necessity in the post-pandemic world. Healthcare providers have always been producing large volumes of data. In fact, over 30 percent of the world’s data volume is generated by the healthcare sector. However, COVID-19 has brought data into sharp focus. Disease prevention has come up as one of the most important use cases of data analytics in recent times. Data, models, and analytics serve as critical decision making tools to effectively improve outbreak response to COVID-19. Infectious disease modelling and analytics have become priorities for governments and healthcare providers across the globe.
Healthcare providers are leveraging data analytics to predict disease outcomes, treatment plans, benefits of drugs and patient load etc. to be more responsive and agile.
Real-time analytics in retail
Retail players are building on the ability to process data as soon as it is generated, which allows them to quickly identify customer needs and offer hyper-personalised experiences. Retailers today must offer connected and customised buying experiences to consumers across their physical and digital touchpoints.
As the usage of internet of things (IoT) becomes widespread in retail outlets, brands are increasingly relying on real-time, in-store analytics to capture upsell and cross-sell opportunities. Real-time customer analytics help retailers to adopt dynamic staffing and reduce queue times.
In e-commerce, real-time analytics help players to monitor conversion rating and address customer fraud more effectively. Correlation analysis is critical for reducing the time to detection (TTD) and time to remediation (TTR) for e-commerce players.
Weathering climate change
Data science and analytics have been providing tailwinds for the global efforts to address climate change issues. For example, research from Arizona State University explores how data analytics can predict global warming trends, heat waves and other extreme weather events. The ability to predict allows researchers and scientists to understand how certain actions can worsen or prevent weather changes. Data analytics also empowers organisations to be more resilient to climate change impacts while helping them explore renewable sources.
Many organisations leverage data analytics to reduce their carbon footprint by drawing data from their sensors/ IoT devices. It also helps entities to monitor the waste produced and energy consumed, and to develop insights on how they can become more environmentally responsible.
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