As data management becomes hard to derive results and determine fast and appropriate decision-making this is where Big Data come into the act to simplify and automate data analytics. As data is the king to drive technology and business similarly Machine Learning and IOT in integration with cloud computing, will be highly in use to enhance customer experience and better business organizational capabilities.
Huge data sets available in an unstructured, semi-structured or structured manner from various sources and of varied sizes can be systematically analysed by the use of Big Data analysis. Where traditional data analysis is helpless when analysing a high volume of data for capturing, managing and processing with low latency; Big Data is the solution for it.
Characteristics like high volume, velocity or variety, Artificial Intelligence, Mobile, Social and IOT are associated with Big Data analysis. For eg., the source of data can log files, transactional applications, social media, web apps, devices, sensors, networks, video and audio files and that to real-time at a very huge scale.
Most of the unusable data which researchers, analysts and business users were not able to access are not accessible by the use of Big Data analysis. Techniques like text analytics, machine learning, data mining, statistics and predictive analytics are used for gaining access to untapped data sources.
Big Data Applications:
Better integration of Customers
You can improve your marketing strategies based on customer behaviour, and motivation data accumulated from Big Data analytics
Identify and Mitigate Fraud
Identifying abnormalities in behaviours of real-time transactions leading to fraudulent activity by the use of Big Data with a combination of predictive analytics and comparing it with past historical transactional data.
Achieve and Drive supply chain efficiencies
Analysing the journey of each package delivered to customers will help in determining the costs and time involved in logistic operations, this is achieved by Big Data. Sensors, data and logs help in tracking important information from warehouse to destinations.
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Machine learning is part of Artificial intelligence which ensures self-learning from the data rather than detailed programming. Being a complex process, the machine learning algorithm follows a direction towards ingesting training data, which gives an outcome of a more precise model based on data accumulated. When the machine learning algorithm learns from training data it starts to give desired outcomes. For example, when an algorithm is running on a predictive model it will yield that the data you get will be based on prediction-based data that trained model.
There are various approaches to machine learning as follows:
How to correlate business needs with machine learning:
Companies can better understand behaviour, preference and customer satisfaction with the help of machine learning. Simply querying is not enough to understand all the aspects of the organization. It becomes very vital for businesses to understand hidden patterns and anomalies which are hidden in form of data.
IoT - Internet of Things
Connecting any device to the internet is IOT in layman's terms. As we progress in the field of technology the internet network is well connected with people and devices which enables data sharing procured by the usage of connected devices for applications derived by people.
IoT connects all types of applications like smart devices like microwaves, self-driving cars, fitness devices, and sports equipment like footballs which can track distance and speed for future training requirements.
Any devices which are having inbuilt sensors and are connected to the internet of things platform would be able to send vital information which would address a specific need. Such information can be filtered into important and generic which would help translates into solutions which will address a specific problem before it happens.
For example, using a sensor to identify important or not-so-important accessories for a car manufacturer would prove vital for inventory management, seeing which components are selling better. Such data will help us make a smarter decision and save time and money.
Similar to the above IoT can be used for Homes and in the Transportation industry.
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