What is Intelligence?

Intelligence is the ability to think and act at a much higher level than any other species in nature. It is the intelligence that imparts a special status to us humans, in the food chain.

Can machines too become intelligent enough to replace humans? Let’s keep that for a separate discussion and focus on how with the use of machine intelligence we can get strategic information from pile of data.

What is Artificial Intelligence and how it is different from Data Science?

Artificial intelligence (AI) idea is as old as a computer, probably much older, where the thought of machines acting smarter like humans were envisaged. Although research work was done in this direction, however, not much attention was given after a point.

Robots – this is the first thing that comes to mind when someone hears artificial intelligence performing human works as seen in some cyborg sci-fi movies and science fiction novels. No one wanted to pump in money in this novel out of the world concept of robots in the earlier days, where the goal was to develop machines as if they were intelligent.

One of the best examples of artificial intelligence these days is Autonomous driving.

Along with artificial intelligence other terms used these days are Data science (DS), Machine learning (ML), deep learning (DL), and Neural network.

Along with Artificial Intelligence, Data Science is also one of the two most important technologies today. Data Science makes use of Artificial Intelligence in its operations, it does not completely represent AI.

Data science (DS) as it says is all about data and how one can arrive at critical business decisions processing such data.

Everyday business big or small is a data business. For e.g auto manufacturing companies are generating huge amount of data about their business processes every day. To process and analyse such huge amount of data they implement data processing and smart data systems by making use of Artificial Intelligence (AI).

Thus Data Science uses AI to crunch data however it is NOT Artificial Intelligence completely. Although people still use DS and AI interchangeably but when you compare the actual job description of an artificial intelligence engineer and a data scientist, they are bit different in the real world with crossover.

Data Scientist defines a problem statement, understands the underlying business requirements and uses various data analysis models and Machine Learning algorithms to arrive at a solution.

Some of the data science tools used for processing data and make meaning out of it are:

• SAS2
• Tableau
• Apache Spark
• MATLAB

With the help of these tools, data scientists, explore data, create machine learning models, and do predictive analysis.

An Artificial Intelligence engineer focuses on defining new algorithms, employing existing deep learning neural networks to process and generate business insights from huge amount of data. 

Skills required to become an AI engineer are:

• experience with machine learning models
• AI models
• programming experience in
  • Python
  • R
  • Java
  • C++
  • TensorFlow, PyTorch (for Deep Learning)
  • Spark / Scala

As they say, data is gold so the more data a business has about their operations, the better they can make intelligent business decisions. This is possible through data science where data is processed to generate greater business insight and meaning out of the raw data.

Machine learning and deep learning are widely been used these days among programmers, data scientists, and tech nerds. What is Machine learning (ML) and deep learning (DL)?

Machine learning is a subset of artificial intelligence (AI) where a set of techniques and algorithms are applied to data set to achieve certain AI goals.

Extending further from AI, let us focus on the impact of artificial intelligence in business. Business leaders, forward thinkers, and entrepreneurs alike are thinking of how AI can help their business become competitive through data science or big data processing.

With AI based applications, businesses can gain a competitive edge and dominate the market with faster and accurate data-based intelligent decisions. Data scientists, use high-end technologies to churn actionable insights out of huge amount of data as more and more companies are opening up doors for big data.

Modern corporates are sitting on huge pile of data, which could be processed to unlock the competitive business intelligence. Traditional businesses have data however they are rather static and descriptive.

Between 60% and 73% of all data within an enterprise goes unused for analytics

FORRETER

Does Data Science Offers Competitive Advantage?

Executives, management, and investors alike assume it’s possible to gain competitive advantage by collecting huge amount of customer centric data to analyse and offer better product recommendations. But is this as simple as that?

Had this been the case than businesses with huge money power to gather more data and with super computers to crunch it much faster and better, can shut out and marginalise their competitors with better product and customised offerings. Isn’t it?  

While businesses need data-enabled learning to gain competitive advantage, they also need their regular network to make their position strongest and shut out their competitors. What happens to the businesses that are purely web based? In their case, customer generated data can help gain competitive advantage provided they have effective data mining process to make sense out of the data collected from their customers. 

Credit card companies have huge amount data about their customers, their shopping habits, their spending patterns, online purchasing preferences etc. A Data Scientist can analyse data and based on the same can suggest a customised offers that will not only benefits a customer but also encourages them to shop more and earn more benefits in return. After all this is what all credit card companies wants from their members. 

Leading E-commerce portals like Amazon, eBay and others are breaming with data from customers and they can gain visible advantage by effectively mining data and stay close to them. 

For instance Data scientists at Amazon are tasked with devising algorithms such that customers are recommended items they are likely to buy based their past orders, search patterns and items they added in their wish list.

Through Data science, Amazon platform gathers Big data to harnessing insights and thus have built predicting models for products most likely to be sold quickly, optimising pricing dynamically based on order frequency, availability, and that offered by competitors etc to stay ahead in the business as a leader. 

Amazon has been using innovation through technology to stay ahead of it’s competitors and has in place recommendation-based-system (RBS) to collect customer data to shorten the buying cycle. Data mining is very important for customer satisfaction, increasing sales and reducing costs for customer acquisition and finally keeping up with the market trends.

Yes another, the e-commerce and auction giant eBay, also uses big data to understand the behaviour of millions of it’s customers. As they operate in over 30 countries, big data analytics helps them offer localised offerings respective to each country.

Be it complex A/B testing to understand the response of users to the features and thus based on the response, the design and algorithm changes are done. 

eBay is known to keep at least nine quarters of historical data to provide insight for smarter decisions with real-time access. It relies on Hortonworks Data Platform (HDP) – Hoodop platform due to being open source, scalable and capabilities to process large, multi-source data to help. 

To conclude, as modern day enterprises rely more and more on the big data to arrive at smarter decisions in real-time, Data scientists thus play a crucial role in giving their organisation a competitive edge as they help accelerate decision making and innovation much faster using robust analytics.