Machine Learning and Big Data: Real-World Applications

By Jyoti Nigania |Email | Apr 27, 2018 | 7725 Views

The amount of data that companies collect and store today is staggering. However, it's not the volume of data being gathered that's most important it's what companies are doing with that data that matters most. With both unstructured and structured data streaming in from everywhere at an unprecedented rate, making connections and extracting insight is complicated work that can quickly spiral out of control.

Modern businesses know that big data is powerful, but they're starting to realize that it's not nearly as useful as when it's paired with intelligent automation. With massive computational power, ML systems help companies manage, analyze, and use their data far more successfully than ever before. According to Gartner, worldwide business value derived from artificial intelligence powered technologies reached 1.2 trillion in 2018.

Machine Learning and Big Data Real-World Applications:

Machine learning the branch of artificial intelligence that gave us self'driving cars is helping businesses analyze bigger, more complex data to uncover hidden patterns, reveal market trends, and identify customer preferences for faster, more accurate results. By automating analytical model building, the insight gained is deeper and derived at a pace and scale that human analysts can't match.

Healthcare: Machine Learning capabilities are impacting healthcare in profound ways, improving diagnostics and personalizing treatment plans. Predictive analysis enables doctors and clinicians to focus on providing better service and patient care, creating a proactive framework for addressing patient needs before they are sick.

Wearable technologies and sensors use data to assess patient health in real time, detecting trends or red flags that could potentially foresee a dangerous health event such as cardiac arrest. Advancements in cognitive automation can support a diagnosis by quickly analyzing large volumes of medical and healthcare data, identifying patterns and connecting the dots to enhance treatment and care.

Retail: In retail, relationship'building is critical for success. Technologies powered by ML capture, analyze, and use data to personalize the shopping experience in real time. Algorithms discover similarities and differences in customer data to expedite and simplify segmentation for enhanced targeting.
Based on learned preferences, deeper analysis is reaching individuals and pushing undecided visitors toward conversion. For example, ML capabilities can present online shoppers with personalized product recommendations while adjusting pricing, coupons, and other incentives in real time.

With customer experience top of mind, Walmart is working to develop its own proprietary machine'learning and artificial'intelligence technologies. In March of 2017, the retail chain opened Store 8 in Silicon Valley, a dedicated space and incubator for developing technologies that will enable stores to remain competitive in the next five to ten years.

Financial Services: In the financial sector, predictive analytics help prevent fraud by analyzing large historical datasets and building forecasts based on previous data. ML models learn behavior patterns and then with little human interaction anticipate events for more informed decision'making.

Banks and financial institutions use ML to gather real'time insights that help drive investment strategies and other time'sensitive business opportunities.

Automotive: In the face of stiff competition, the automotive industry is taking steps to differentiate itself by leveraging ML capabilities and big data analytics to improve operations, marketing, and customer experience before, during, and after purchase.

Applying statistical models to historical data helps automakers identify the impact of past marketing efforts to define future strategies for improved return on investment. Predictive analytics lets manufacturers monitor and share vital information regarding potential vehicle or part failures with dealerships, reducing customer maintenance costs.

By identifying trends and patterns from large datasets on vehicle ownership, dealer networks can be optimized by location for accurate, real'time parts inventory and improved customer care.

Conclusion: As machine learning technologies hit new levels of maturity in 2018, smart businesses are shifting their approaches to big data. Across industries, companies are reshaping their infrastructures to maximize intelligent automation, integrating their data with smart technologies to improve not only productivity, but also their ability to better cater to their customers.

Source: HOB