Transformation of BI through AI and ML Democratisation
Since the dawn of the time until 2018 Humans has created data around 22000 Exabytes of Data. But what is.....
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Since the dawn of the time until 2018 Humans has created data around 22000 Exabytes of Data. But what is an Exabyte? How much amount of data does it mean by? Well, let us start from the ground up.

BYTE The letter A takes up an exactly one-byte size.

KILOBYTE A page of text which contains around 1000 letters can have around kilobyte of space.

MEGABYTE The data present in a book of around 500pages can have around 1 Megabyte of space.

GIGABYTE once a whole human genome is coded it can fit into a space of 1Gigabyte.

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TERABYTE When the person is filmed every single day of their life for every hour, minute and second for 70-80 years, it can be fitted into one terra byte of space.

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PETABYTE – Here is the Amazon rainforest which constitutes about 1.4billion acres of land of trees around 700billion trees. If one chops down all the trees and turn them into paper and fill both sides of papers with letters it will occupy the space of around one petabyte.

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EXABYTE – Exabyte is 1000 times the Petabyte.

Machine Learning & Artificial Intelligence:

So when there is a huge amount of data, how can the analysis of the data help? This gives the answer to Machine learning. Machine Learning is a method of analyzing the data which can automate the building of analytical models. The main theme of the machine learning is to create the algorithms which can receive the input data and do the analysis depending on the statistics and provide the output. The algorithms are designed in a way such that they can self-learn and improve over time when new data is exposed. So Machine learning will be more effective after more iterations and when it has a large set of data.

Artificial Intelligence enables the machines to act like humans by replicating their behavior and nature. The machines make their response based on new inputs thereby performing tasks like humans. Artificial Intelligence is a training to accomplish specific tasks by processing large amounts of data and recognizing patterns in them.

Let us take a small daily life example which uses AI and Machine Learning.
Have you ever booked a ticket on IRCTC?

IRCTC has enabled a new feature called confirmation probability. It predicts the confirmation probability chance by getting the previous data and shows the percentage of confirmation. This gives the user the freedom to book the tickets or not which are in waiting list.

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Likewise, many other organizations are using Artificial Intelligence with machine learning to provide the user’s feasibility and reduce the manual efforts also. This world of high-tech innovation can change the destiny of industries seemingly overnight. Advanced, predictive analyses are required for calculating future trends and predicting potential outcomes and making recommendations. This makes to go well beyond the traditional queries and BI which defined the BI in the past. This shifts the gear of BI (What Happened?) to the character of AI (What will happen next?). This is a change from reactive analytics to proactive analytics. The software alone doesn’t make an impact. A huge amount of data and process is required for this evolution. One can say poor quality of data is the enemy of Machine learning.

 

EXCELLING IN THE BUSINESS ARENA:

Rapid Transformation: AI is changing rapidly in heavily regulated industries like financial and banking services, life sciences, health care department and supply chain industry etc. For instance, in the medical field, AI takes the role of clinical assistant which helps the physicians make quicker and apt diagnoses. It also helps in medication and discovery of new drugs.

Modern Decision Making: This science is impacting all modern day business aspects. Before the renaissance of AI, leaders had to depend on inconsistent and incomplete data. But nowadays AI feeds on Big Data, chews it and then breaks it down into actionable insights that aid executives in the process of making decisions. For example, a marketing manager must understand their customer ever-changing needs and align the products and services according to them.

Offering better Insights: AI is automation of the maximum sequence of decisions originating from analytics. This intelligence provides the ability to give real-time feedback data to develop the prescriptive models. This should make sure that the next prescribed decision will have a better output than the previous. This exceptional ability which can adapt and learn provides AI to execute actions following automated decisions. As organizations continue generating more data, the AI will work more effectively providing better insights and profitability.

 

Who’s using it?

Financial Services: Banking and Financial industries use Machine learning technology to identify important insights into data and preventing frauds. The insights can generally help in identifying the investment opportunities or help the investors when to trade. This can also identify clients with high-risk profiles or to detect pinpoint warning signs of fraud.

Government: Governments can use in public safety and in the efficient functioning of the government. Generally, the government has multiple sources of data from which they can identify the ways to increase efficiency and save money. They can be mostly used in controlling traffic like when there are no vehicles near the signal, then the signal automatically goes red and give way for the side where there are vehicles.

Health Care: Health care industry is the most dominant industry where this Machine learning is used. The advent of wearable devices and sensors can use data to assess the patients’ health in real-time. They can be used in improving diagnoses and treatment.

Natural Resources: Finding new energy sources and analyzing the minerals in the ground. Predicting the sensor failures in refineries. Streaming oil distribution to make it cost effective and more efficient.

Marketing and Sales: Amazon and Flipkart companies use machine learning to analyze the buying history based on previous purchases and promote other items in which people are interested in.

Transportation: Making routes more efficient and predicting potential problems which increase profits. The public transportation can be more effective by collecting the data and analyzing them.

 

Challenges:

The black box challenge is how we trust in the decision of the machines which are made based on all the supervised machine learning. The AI model is not superior to humans in a decision process. The model generally stores its knowledge in digital format which cannot be deciphered through human logic. Let’s say, when the machine learns to recognize a picture of a turtle or whether or not a hot dog is present on the image, which stores the data in a matrix that describes multidimensional correlations at the level of individual pixels. Ofcourse, we can teach the machine to recognize the presence of specific body features and turn into detailed inferences. But this is labor-intensive and may result in an ML system which is not easy to modify.

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Comments (1)

This seems to be one of the best article I have read regarding AI and ML.Esspl really doing a good effort in providing the new solutions.Highly ppreciated for the Esspl work and Author for writing a such blog..

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