The 101 Executive Guide To Data Science and Artificial Intelligence 

data science and Big data explained for executives

Data Science and Artificial Intelligence (AI) are two of the hottest fields right now.

And so, everybody is talking about these technologies. From boardrooms to factory floors, from call centers to logistics fleets, and from governments to venture capitalists. 

Does that mean Data Science and AI are just the latest buzzwords? Certainly not. In fact, Data Science and AI are going to change businesses in ways we can’t imagine. 

Therefore, in this guide, we are going to give a walkthrough for executives and other business professionals who are collaborating with data teams. Or want to learn how AI works or would work in their organizations. 

What else? We’ll start with data science and then move on to Artificial Intelligence. We will explain how these technologies work in layman’s terms and discuss how various businesses are utilizing them.

Executive Guide to Data Science 

Data science is the process of using algorithmic techniques on a set of data in order to discover meaningful patterns in that data. 

For example, a data scientist will look at the data on how many people use an app. And their age, gender, location, etc. to figure out if a targeted marketing campaign is working. 

Now, you must be thinking — what’s all that jargon that’s thrown around Data Science. For example, Big Data, deep learning, and predictive analytics. How do they relate to each other, and what exactly do you need to know as an executive? 

So Data Science and use cases are evolving every day. However, most of the work and development only revolves around a few key data-related topics. 

The goal is to explain these topics in a brief and simple language — covering the examples also of how they are used in Data Science. Let’s start with Big Data. 

What is Big Data 

By now, almost everybody has read about Big Data for business. And how it’s changing the way we capture, store and analyze information. But what is big data anyway?

In short, it’s the term used to describe a collection of data sets so large and complex that it becomes difficult to process using traditional database management tools. 

Essentially, it refers to data that is complex & huge for regular data-processing software to handle.

Now, Big Data can be both structured and unstructured. Which is…?

Structured data refers to any data that resides in fixed fields within a record or file. This includes data contained in relational databases and spreadsheets. Unstructured data includes information that doesn’t reside in fixed fields, such as e-mails, documents, and multimedia files.

The idea is that analyzing all this information can uncover new correlations to help companies make better business decisions and predict what will happen in the future. 

For example, in healthcare, doctors could combine medical records with genomic data to predict the likelihood of how a patient would respond to certain treatments. Or a retailer could analyze foot traffic with weather conditions and inventory levels to optimize staffing schedules and stock levels.

Anyway, we keep talking about storing and analyzing big data and how traditional tools can’t do it. Well, that’s why we have tools like Hadoop and SQL. 

What are Hadoop and SQL?

Hadoop and SQL are two of the most widely used technologies for data processing and analysis. 

These names are often used together, and both represent the present and future of big data. But one is a platform while the other is a programming language. So what’s the difference? And how do they work so well together?

Both Hadoop and SQL originated from different needs but eventually combined to provide a unified big data ecosystem. Let’s understand each. 

Hadoop Explained

Imagine there’s a big file that’s very important to your company, but it’s too big to fit on a single computer. 

This very important file takes a lot of space on a lot of computers to keep it… Hadoop clusters are like that. They let you store and use a big file on multiple machines when it’s too big to fit on one.

Hadoop is a framework for data storage and processing. Its goal is to simplify the process of gathering and analyzing large data sets across multiple computers.

Moreover, it’s an open-source framework that allows for the distributed processing of large data sets across clusters of commodity hardware using simple programming models.

Hadoop enables businesses to quickly gain insight from massive amounts of structured and unstructured data. Such as social media content, mobile application data, sensor data, machine-generated log files, and web server traffic.

SQL Explained 

SQL stands for Structured Query Language and it is used to communicate with databases. 

It is a powerful yet simple language that can be used to create, delete, modify and extract data from databases.

SQL is an incredibly useful tool for any business with a database. It lets you manipulate information in your database quickly, easily, and efficiently. 

Which is why it’s so popular in established businesses and businesses that don’t have high volumes of data. In fact, SQL has been around since 1979 — that’s just three years after the first personal computer was sold. 

How are Businesses Using Big Data

The amount of data we now have can be processed to find insights to help us make better decisions in a number of industries.

Image: CHEN JADE, CC BY-SA 4.0 via Wikimedia Commons

Healthcare – Big data helps doctors and scientists understand the human body better. They can use the information extracted from patient data to make more informed decisions about diagnosis, treatment, and prevention of diseases. This can help save thousands of lives every year.

Retail – The retail industry uses big data to improve customer experience. They do this by analyzing customer purchase patterns and their feedback on products. With this information, they can offer personalized discounts and offers which encourage the customers to buy more than they otherwise would have done.

Banking – The banking industry has been using big data for a long time now. They use it for fraud detection purposes as well as for improving their existing services and offering new ones to potential customers.

Transportation – The transportation sector uses big data for traffic management as well as for increasing safety on roads. The former is achieved by gathering real-time traffic information from multiple sources such as CCTV cameras, congestion monitoring systems, etc. And providing it to commuters so that they can take the best possible route based on this information. 

Oil & energy industry – There are several ways companies can use big data to save money in the oil & gas industry — from detecting leaks and other maintenance issues to managing fuel costs more efficiently throughout an organization’s supply chain.

Cyber Crime – Big Data is used in the analysis of cybercrime to understand the patterns and trends of cybercrime activities. It helps law enforcement agencies to deal with cybercrime and improve cyber security

Disaster Management – Big Data helps to predict disasters like earthquakes and floods so that necessary measures can be taken before these disasters happen. Also, it helps in disaster response management by analyzing a large amount of information about the disaster area to cope with it effectively.

Space Exploration – The space exploration industry uses Big Data analytics to process a large amount of data related to extraterrestrial life forms, space probes, etc. The data gathered by telescopes can be analyzed by Big Data analytics tools to get more details about what’s happening beyond the Earth’s atmosphere.

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Executive Guide to Artificial Intelligence 

Artificial Intelligence explained to executive

Just like Data Science, getting started in AI can be overwhelming. The excitement surrounding AI is equally intriguing, given the rate at which disruptive innovation is taking place as well as the bottom-line results.

To start with, Artificial Intelligence (AI) is defined as intelligence exhibited by machines. 

In computer science, an ideal “intelligent” machine is a flexible rational agent that adapts to its environment and acts in a way that increases its chances of achieving a goal.

The term Artificial Intelligence is applied when a machine mimics cognitive functions that humans associate with other human minds, such as learning and problem-solving

Alright, that’s fine. But how does AI help businesses? 

Essentially, AI offers computer vision, language, virtual agents, robotics, self-driving vehicles, and machine learning to solve complex problems. 

Why Artificial Intelligence Matters Now More Than Ever

According to AI experts, Artificial Intelligence is one of the most daring and important innovations of mankind. 

It’s a man-made intelligent being that combines and accelerates important future technologies. AI can also help us unravel the greatest problems of our world. 

Already today, algorithms using AI are firmly integrated into our everyday life. Google searches use AI to refine their results, Netflix uses your viewing history to suggest what you might want to watch next, and so on. Moreover, we use AI every day when we interact with our smartphones.

Therefore, AI adoption is growing faster than ever before, thanks to the proliferation of data and the maturity of other innovations in cloud processing and computing power. Organizations today have a massive amount of data — including the dark data which only now companies have discovered. 

Because of all these reasons, AI is revolutionizing businesses as well as the interaction between people and technology at large. Making AI one of the most important innovations currently. 

How Does AI Work

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AI makes tasks easier by allowing machines to learn from their past experiences. 

It also maps efforts & actions to outcomes, discovers & fixes errors, responds to new & random input values, and performs human-like tasks with ease. 

For this, AI employs Natural Language Processing to decipher natural human communication and convert it into a code that robots can understand. 

Deep Learning is also used by AI to complete this task. AI educates computers to accomplish certain tasks with the least amount of human interaction by processing enormous data volumes and recognizing familiar or new patterns in data using these technologies.

Every time an AI system processes data, it tests and measures its own performance and gains new knowledge.

Because AI never needs to rest, it can run through hundreds, thousands, or even millions of tasks in a matter of seconds. Learning a great deal in a short period of time and becoming extremely capable at whatever you throw at it. 

What I’m trying to say is, don’t assume that AI is just a single computer program. It’s so much more than that. It’s an entire discipline on its own. 

Ultimately, the goal of AI is to create a computer system capable of modeling human behavior and using human-like thinking processes to solve complex problems. 

AI vs Machine Learning vs Deep Learning 

Image: CC BY-SA 4.0 via Wikimedia Commons

Most of the time, people casually use all three keywords in the same context and think they mean the same. 

Which is not entirely true. 

So we already explained that AI is the simulation of human behavior in terms of intelligence processes involved in problem-solving.

Now the thing is, Machine Learning is a subset of Artificial Intelligence that helps in the development of AI-driven applications. 

While Deep Learning is a subset of machine learning that trains a model using massive amounts of data and complex algorithms.

So you can say that machine learning and deep learning evolved because of AI but they branched off to evolve on their own. 

Let’s briefly discuss more machine learning and deep learning. 

Machine Learning Explained

Machine learning is the science of getting computers to act without being explicitly programmed. 

In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. 

Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress toward human-level AI.

As per McKinsey & Co., machine learning is based on algorithms that can learn from data without relying on rules-based programming.

Deep Learning Explained 

Deep learning is a subset of machine learning in Artificial Intelligence that has networks capable of learning unsupervised from data that is unstructured or unlabeled. Also known as deep neural learning or deep neural network.

Deep learning owes its popularity to its ability to automatically extract features from raw data. 

For example, if you’re working with images, deep learning can find edges, corners, and other distinguishing features that humans could easily recognize but are very hard for us to describe.

This approach leads to state-of-the-art results in fields as diverse as computer vision, automatic speech recognition, natural language processing, audio recognition, and bioinformatics.

How are Businesses Using Artificial Intelligence

AI can be used to analyze data and make decisions to improve business outcomes. Overall, it can be implemented in several ways to help businesses offer better service, increase productivity and gain a competitive advantage. 

Personalization – AI can scan a large number of variables to identify patterns and trends, allowing companies to suggest products or services that fit customers’ needs or interests. For example, Netflix uses AI to recommend movies and TV shows based on a user’s preferences. Other companies also use AI to predict market research. All of these recommendations are generated by machines that analyze data and learn from interactions to create a personalized experience. 

Process Automation – AI can do many of the routine tasks that humans currently do much faster, more efficiently, and without error. This will free up workers’ time so they can focus on more creative endeavors. For example, an Artificial Intelligence system can help decide which applicants should be called back for an interview, or whether certain content should be published or not. It’s also being used for automated hiring, with some companies using chatbots to conduct initial interviews with candidates. 

Customer Service – Chatbots are already in use by many companies to answer common questions and respond to common requests from customers. They’re typically more responsive than human customer service agents, making customers happier — and helping human employees get more done. 

Increase Output – AI can perform certain tasks better than humans ever could. Such as analyzing data to identify trends or draw conclusions from it — which may be beyond human abilities. Or writing basic news stories or legal briefs based on the data available on them. All of this will help increase efficiency and boost productivity overall. 

Data Analysis – There’s so much data available today that it’s difficult for anyone to make sense of it all — even data scientists. AI helps sift through this data and identify patterns and insights that humans might not see due to cognitive biases or lack of context. For example, medical professionals are using AI to analyze health data from millions of patients to identify patterns that could lead to earlier diagnoses or better treatments.

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