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BIG Data AnalyticsIdentify the valuable insights from data
"Why is BIG data analytics important?"
Big data analytics helps organizations harness their data and use it to identify new opportunities. That, in turn, leads to smarter business moves, more efficient operations, higher profits and happier customers.
How Is Big Data Analytics Using Machine Learning?
Makes Sense Of Big Data
Big data refers to large sets of structured and unstructured data that cannot be handled with traditional methods. Big data analytics can make sense of the data by uncovering trends and patterns. Machine learning can accelerate this process with the help of decision-making algorithms. It can categorize the incoming data, recognize patterns and translate the data into insights helpful for business operations.
Compatible With All Elements Of Big Data
Machine learning algorithms are useful for collecting, analyzing and integrating data for large organizations. They can be implemented in all elements of big data operation, including data labeling and segmentation, data analytics and scenario simulation.
Carrying out market segmentation
Enterprises need to carry out market research that can delve deep into the minds of potential customers to facilitate insightful data. Machine learning's supervised and unsupervised algorithms to interpret consumer patterns and behaviors accurately. Media and the entertainment industry use machine learning to understand the likes and dislikes of their audiences and target the right content to them.
Exploring customer behavior
Machine learning then helps businesses explore audience behavior and create a solid framework of their customers. This system of machine learning, known as user modeling, that mines data to capture the mind of the user and enable business enterprises to make intelligent decisions. Facebook, Google, etc. rely on user modeling systems to know their users inside out and make relevant suggestions.
Businesses need to offer personalization to their customers. Be it a smartphone or a web series, companies need to establish a strong connection with their users to deliver what's relevant to them. Big data machine learning is best put to use in a recommendation engine. It combines context with user behavior predictions to influence user experience based on their activities online. This way, it can empower businesses to make correct suggestions that customers find interesting. Netflix uses machine learning-based recommender systems to suggest the right content to its viewers.
Machine learning algorithms use big data to learn future trends and forecast them to businesses. With the help of interconnected computers, a machine learning network can constantly learn new things on its own and improve its analytical skills every day. In this way, it not just calculates data but behaves like an intelligent system that uses past experiences to shape the future. An air conditioner brand can depend on machine learning to predict the demand for air conditioners in the next season and plan its production accordingly.
Machine learning uses a technique called time series analysis that is capable of analyzing an array of data together. It is a great tool for aggregating and analyzing data and makes it easier for managers to make decisions for the future. Businesses, especially retailers, can use this ML-boosted method to predict the future with commendable accuracy.
Machine learning can be highly efficient to decipher data in industries where understanding consumer patterns can lead to major breakthroughs. For example, sectors like healthcare and pharmaceuticals have to deal with a lot of data. Machine learning can help them analyze the data to identify diseases in the initial stage among patients. Machine learning can also allow hospitals to manage patient services better by analyzing past health reports, pathological reports and disease histories. All of these can lead to better diagnoses at healthcare centers and boost medical research in the long run.
BACHcode uses Big Data
to AI Health-Check a Person:
Big Health Data
AI Health Check algorithms by analysed around 50,000 people health profiles
Data Mining and Modeling
With the algorithms, end-user input their (1) anthropometric (weight, height, age, etc) and (2) habitual data (hours of sleep, exercise, water drink, alcohol), then a AI Health Check report will be provided instantly.
Health Check results: user bio-data such as blood pressure, fasting blood glucose, cholesterol level, triglycerides, etc. And Long term chronic risk summary.
Insurers can understand health risk of end-users and enrich the end-user profiles, which can further works with the recommendation system to recommend a suitable insurance product
Analysing the Relationship between
Bio Data (Blood Pressure, Blood Glucose, Cholesterol, etc.) and Health Factors(Anthropometrics, Physical Activities, Lifestyle, Medical History)
Projects that ride on BACHcode Big Data Analystics
Eat Rite by ReHealthier
BACHcode award-winning mobile app has equipped with AI Health Check for client to understand their health risk any time and to start their healthy lifestyle today!
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