“Machine intelligence is the last invention that humanity will ever need to make.” – Nick Bostrom. various Forms Of Data Analysis can be challenging sometime, knowing that it involves complex information and larger data sets which may be difficult to manage manually. This is where machine learning comes into play.
thus, It is changing the way consumer-based companies are dealing with the larger data that they generate
It is alos based on Artificial Intelligence (AI) on the idea that systems can automatically learn. thus from given data, identify patterns and make decisions. so, with some human interference involve in it, and is useful for automate analytical model building.
Machine learning reduces efforts, reduces time and also a cost-effective tool which replaces time of group of people in a team working on analysing, processing and performing regression testing on the data. so, It gives accurate results most of the time and helps organisations build statistical models based on real-time data.
Forms of Data Analysis
Organisations collect a large set of data for fulfilling their business goals. According to the Data Dilemma Report, 12.5% of staff time is lost in data collection. That’s five hours a week in a 40-hour workweek.
There are different ways of how data could be analysed, used and implemented that would help in scaling the organisation.
thus, There are broadly three different Forms Of Data Analysis:
1. Descriptive analytics – Insight into the past
It is the most primary stage of data processing that records a summary of past data to yield useful information that might be useful for future insight.
It informs organisations about “What has happened earlier”. also, how they can learn from their past actions to make better decisions in the future.
2. Predictive analytics – Understanding the future
Predictive analytics is a thus trending strategy for companies lead generation, enhance sales, and drive higher ROI (Return on Investment).
so, It uses different statistical modelling and mL algorithms to analyse past data and predict future.
3. Prescriptive analytics – Solutions on Possible Outcomes
this is a relatively new form of data analysis which uses a combination of mL, computational modelling and business rules.
thus, the best course of action for any possible pre-defined outcome. It uses algorithms as optimisation and simulation to guide organisations towards a safer path by suggesting useful solutions.
Companies Transforming How Machine Learning is Used
Analytics has been changing the way business models work; it is transforming marketing strategies, sales plans, customer acquisition methods and revenue models as well. With companies turn up in large datasets to increase efficiency, be more competitive, modifying data analysis to their benefit.
That’s why companies are focusing on Machine learning algorithms through which they can enhance a comprehensive analytics strategy to achieve more of business goals. It is essential to learn how ML can affect your business and can improve it in ways conventional methods could not do more easily.
According to Forbes, Amazon uses mL for its same-day shipping processes that has reduced the ‘click-to-ship’ time by 225%.
Here are some examples of smart implementation, insights from experts, and business use cases to give you a fair idea of how you can use machine learning to benefit your organisation:
- In its inner content management system, HubSpot utilises Kemvi’s DeepGraph machine learning and natural language processing technology to define trigger events, pitch prospective clients better and serve current customers.
- Pinterest acquired a machine learning company, Kosei, which specialises in commercial applications of machine learning. It now uses the technology in its business operations, including content delivery, advertising monetisation, churn reduction and spam moderation.
- Twitter utilises machine learning technology and AI to assess and rank tweets in real-time using different metrics to show tweets that have the potential to drive the most engagement.
- Machine learning with software such as IBM Streams and DataTorrent enables companies to uncover anomalies so that they can take immediate action to analyse fraud or obtain greater understanding into online behaviour.
however, Google’s AI, AutoML, which helps the company build other AIs projects, learnt to replicate itself in October 2017.
Benefits of Using Machine Learning for Data Analysis
Some of the major benefits of using ML for data analysis are:
- Reducing Customer Churn.
- Detects Fraudulent Transactions..
- Customer Acquisition.
- Customer Experience.
Written By: Nikesh Maurya
Reviewed By: Krishna Heroor
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