The Process of Analytics can be applied and studied in many different ways. The Process of Analytics can be studied through the lens of an analytics adoption model and with modern technology, it can be studied and applied through and with machine learning. The Process of Analytics has become continually merged with technology and emerging technology. This is because, with machine learning, artificial intelligence, natural language processing, and other new and innovative technology, efficiency shoots up, thus giving organizations a chance to be more innovative and have better returns.
Why is Machine Learning So Important in Process of Analytics?
Many industries that have to deal with analyzing big data (which is ever growing) also have to analyze the data and machine makes these processes easier, faster, and more efficient. The Process of Analytics helps to identify hidden insights in data and provide solutions that will make business work more efficiently. Big data enables businesses and enterprises to identify trends and patterns that can then be used for predictive analytics and also automate this analysis for future data sets. Put simple, Machine learning helps companies identify trends that can be used to make profitable business decisions in the long run. In order for companies to develop and effectively use machine learning through the process of analytics for their data purposes, they are going to need the following:
- Superior data preparation capabilities
- Knowledge of basic and advanced algorithms
- Automation and iterative processes
- Knowledge of ensemble modeling
- Experienced Data Analyst
- Effective Strategy Implementation
- Properly Functioning Teams
- An Innovative Mindset
Uses of Process of Analytics in Machine Learning
Machine learning has various uses no doubt. Let’s discuss some of these uses and applications below–
Image recognition technology has been around for a while now but it has started to make huge leaps and bounds with the application of machine learning in image recognition software. Machine learning can be used for more advanced face detection, character recognition, handwriting recognition, and this can be applied to both colored and white and black images. Machine learning can observe the intensity of pixels and use it to make identifications within an image. In color images, each pixel provides 3 measurements of intensities in three different colors – red, green and blue (RGB). Machine learning then takes its understanding of red, green and blue (RGB) color intensities and applies it to other images.
Just like in image recognition, machine learning can be used for speech recognition. This means being able to translate spoken words into text. Computer speech recognition is a huge area that is being developed and already being applied in everyday technology use. Devices like Google Home and Alexa function mainly through the voice user interface and speech recognition as that is how they receive commands. Phones, computers, and tablets can also be used using only voice commands. The devices receive these voice commands and then convert them into written commands for the device. Speech recognition can also be used a simple data entry and the preparation of structured documents.
There are many many ways that machine learning is being applied in the healthcare industry and in healthcare process of analytics. In fact, the healthcare industry is one industry that has a lot to gain from the applications of machine learning and artificial intelligence technology. One of those ways is through an improved medical diagnosis. Machine learning can be used to aid and better improve the diagnosis of diseases in patients.
Machine learning takes existing data, a combination of prognosis, symptoms and other medical knowledge stored in the available data to make a diagnosis. This improves the accuracy and speed of patient diagnosis. Machine learning can also be used for patient monitoring and extended patient care management. And of course, in the process of analytics, machine learning can be used to better analyze healthcare data through improved healthcare analytics.
Financial Services and Arbitrage
The financial industry is usually one of the first industries to jump on new technology and apply it to their operations. Machine learning can be applied to financial operations in ways such as automated trading strategies and extensive deep-dive data analytics. Machine learning can be trained using existing algorithms and a set of data quantities like historical data, and then the technology uses it to make future predictions on data trends, profitable trades and stock market fluctuations. Machine learning can also be trained to do extensive calculations like linear regressions, WACC (Weighted Average Cost of Capital), and arbitrage strategies in currency exchange. Machine learning has a lot of applications is not just the financial sector but also in the banking sector.
The financial industry and Wall Street is very competitive with a dog eat dog mentality. Thus an opportunity to get ahead using technology is quickly latched unto. Machine learning can be trained to track trading patterns and optimize parameters. It can also be trained to track spending patterns and show a correlation over time to analyze the spending or trading habits of a stockbroker.
This can then be used to see f they are aggressive with their trades or if they shy away from risk and how either of these has affected their effectiveness as a trader. when it comes to trading stocks, there is not one answer and so being able to track a traders behavior gives them insight into how the can teak or adjust their trading habits to be more profitable and be better traders.
The application of machine learning to learning association spans across different industries. Machine learning can be trained to track the association between otherwise unrelated products. This is used a lot in the consumer and retail industry. Tracking purchase patterns over time, machine learning can then predict the buying patterns of different products of customers. So when a customer makes a purchase, they are given a recommendation of other products that are frequently bought together and they are prompted to make the same purchase pairing. E-commerce companies like Amazon use this application of machine learning.