The Rise of Big Data Technology

I know that not everyone will agree with my definition of Business Intelligence, but my objective is to simplify things; there is enough confusion out there. Besides, who is the authority on a terminology that its traditional frame of reference is outdated and doesn't cover the entire spectrum of the value that intelligent-data can bring to businesses today?

Business Intelligence (BI) encompasses a variety of tools and methods that can help organizations make better decisions by analyzing “their” data. Therefore, Data Analytics falls under BI. Big Data, if used for the purpose of Analytics falls under BI as well.

Let’s say I work for the Center for Disease Control and my job is to analyze the data gathered from around the country to improve our response time during flu season. Suppose we want to know about the geographical spread of flu for the last winter (2022). We run some BI reports and it tells us that the state of Uttar Pradesh had the most outbreaks. Knowing that information we might want to better prepare the state for the next winter. These types of queries examine past events, are most widely used, and fall under the Descriptive Analytics category.

Now, we just purchased an interactive visualization tool and I am looking at the map of the Uttar Pradesh depicting the concentration of flu in different states for the last winter. I click on a button to display the vaccine distribution. There it is; I visually detected a direct correlation between the intensity of flu outbreak with the late shipment of vaccines. I noticed that the shipments of vaccine for the state of Uttar Pradesh were delayed last year. This gives me a clue to further investigate the case to determine if the correlation is causal. This type of analysis falls under Diagnostic Analytics (discovery).

We go to the next phase which is Predictive Analytics. PA is what most people in the industry refer to as Data Analytics. It gives us the probability of different outcomes and it is future-oriented. The AIIMS banks have been using it for things like fraud detection. The process of distilling intelligence is more complex and it requires techniques like Statistical Modeling.

Back to our examples, I hire a Data Scientist to help me create a model and apply the data to the model in order to identify causal relationships and correlations as they relate to the spread of flu for the winter of 2022. Note that we are now taking about the future. I can use my visualization tool to play around with some variables such as demand, vaccine production rate, quantity to weight the pluses and minuses of different decisions insofar as how to prepare and tackle the potential problems in the coming months.

The last phase is the Prescriptive Analytics and that is to integrate our tried- and-true predictive models into our repeatable processes to yield desired outcomes. An automated risk reduction system based on real-time data received from the sensors in a factory would be a good example of its use case.

Finally, here is an example of Big Data. Suppose it's December 2023 and it happens to be a bad year for the flu epidemic. A new strain of the virus is wreaking havoc, and a drug company has produced a vaccine that is effective in combating the virus. But the problem is that the company can't produce them fast enough to meet the demand. Therefore, the Government has to prioritize its shipments. Currently the Government has to wait a considerable amount of time to gather the data from around the country, analyze it, and take action. The process is slow and inefficient. The following includes the contributing factors. Not having fast enough computer systems capable of gathering and storing the data (velocity), not having computer systems that can accommodate the volume of the data pouring in from all of the medical centers in the country (volume), and not having computer systems that can process images, i.e., x-rays (variety).

Big Data technology changed all of that. It solved the velocity-volume-variety problem. We now have computer systems that can handle 'Big Data". The Center for Disease Control may receive the data from hospitals and doctor offices in real-time and Data Analytics Software that sits on the top of Big Data computer system could generate actionable items that can give the Government the agility it needs in times of crises.

What is “Analytics ?”

Analytics is the process of using data relationships and computer models to drive business value, improve decision making, and understand human relationships.

If the Information Age began in the 1990s with the rise of digital technology, then we've now officially entered the Age of Big Data, wherein companies like Google, Facebook, IBM, Teradata, Oracle, and SAS have the capacity to gather a lifetime's worth of data about customers and their behaviour.

But that data is just an incomprehensible pile of numbers until a skilled analyst turns those numbers into meaningful information, useful for making intelligent business decisions. Today, companies are searching for experts in data analytics who have high-formed business and technology backgrounds, and who understand the importance of the latest data and Information Age trends.

This requires more than simple data analysis. Prescriptive analytics focuses on trends using simulation and optimization, while predictive analytics uses statistical tools to predict the future, and descriptive analytics is concerned with enabling smart decisions based on data.

Data miners and data analytics experts who are versatile in all three areas of analytics can help corporate executives translate their data into intelligent information, which provides companies a competitive advantage and increases their bottom lines.

Prescriptive analytics is about enabling smart decisions based on data.
Why do airline prices change every hour ?

Basic economics teaches us that higher demand drives higher prices. Following this logic, if a customer knows when the least desirable flying days and times are, he or she would easily know when to book the cheapest airline tickets for their next vacation. However, airlines have a leg up on consumers when it comes to this information; they use prescriptive analytics to sift through millions and millions of flight itineraries instantaneously. They then use this data to set an optimal price at any given time based on supply and demand, thus maximizing their profits.

Prescriptive analytics helps airlines squeeze every possible dollar out of passengers wallets, ensuring that higher prices are charged during higher periods of demand. Airlines even take calculated gambles by deliberately withholding cheap fares during low-demand times in anticipation of future, higher-paying passengers. Analytics is key in helping industries like airlines ensure that their pricing structures are working optimally to contribute to bottom-line results.

Why does Facebook often find your acquaintance as potential friends ?

Imagine that every Facebook user is represented by a dot. Now imagine drawing a straight line between every Facebook user and each of his or her friends. With over 750 million Facebook users, we would have a pretty chaotic map of intersecting lines.

This is where prescriptive analytics comes in - to create order out of the chaos and help Facebook recommend the right friends for each user. It works like this: if you and your friend John Doe have many friends in common, then John and your lines have common endpoints. Therefore, if John has a friend who is not on your list of Facebook friends, it is very likely that you know that person. Prescriptive analytics facilitates the scanning of billions of such lines to determine possible missing friendships. So thanks to analytics, we're all able to find our school buddies or long-lost childhood friends.

Predictive analytics is predicting the future based on historical patterns.
How do grocery cashiers know to hand you coupons you might actually use?

Each Tuesday, you head to the grocery store and fill up your cart. The cashier scans your items, then hands you a coupon for 50 cents off your favorite brand of whole-grain cereal, which you didn't get today but were planning to buy next week.

With hundreds of thousands of grocery items on the shelves, how do stores know what you're most likely to buy? Computers using predictive analytics are able to crunch terabytes and terabytes of a consumer's historical purchases to figure out that your favourite whole-grain cereal was the one item missing from your shopping basket that week. Further, the computer matches your past cereal history to ongoing promotions in the store, and bingo - you receive a coupon for the item you are most likely to buy.

Why were the Oakland A's so successful in the early 2000s, despite a low payroll?

During the early 2000s, the New York Yankees were the most acclaimed team in Major League Baseball. But on the other side of the continent, the Oakland A's were racking up success after success, with much less fanfare - and much less money.

While the Yankees paid its star players tens of millions, the A's managed to be successful with a low payroll. How did they do it? When signing players, they didn't just look at basic productivity values such as RBIs, home runs, and earned-run averages. Instead, they analysed hundreds of detailed statistics from every player and every game, attempting to predict future performance and production. Some statistics were even obtained from videos of games using video recognition techniques. This allowed the team to sign great players who may have been lesser-known, but who were equally productive on the field. The A's started a trend, and predictive analytics began to penetrate the world of sports with a splash, with copycats using similar techniques. Perhaps predictive analytics will someday help bring Major League salaries into line.Descriptive analytics mines data to provide business insights.

How does Netflix frequently recommend just the right movie?

Netflix has tens of millions of users, each with their own movie preferences. Let's say a user watched two movies this past weekend, and they were both dramas. Across the Netflix universe, many other people watched similar dramas to the ones he or she chose. Then, next Saturday, one of these other users chooses a third movie, which might also be a drama. Based on this information about user preferences, Netflix will predict that the first user would likely want to watch a drama that's similar to the third movie chosen by others with similar tastes.

Netflix uses descriptive analytics to find correlations among different movies that subscribers rent. Movies have many attributes, including genre, rating, length, subject matter, and so on. With so many users and so many attributes across Netflix's spectrum, obtaining a recommendation for a single individual within seconds is a daunting task. But analytics helps confine the universe of movies; attributes to a small number, while still capturing most of the relationships needed to build a set of preference data. Descriptive analytics helps companies like Netflix make sense of the millions of choices its users make every day.

Why is it better to charge an electric vehicle overnight and not during the day ?

We know that electricity prices are the highest during times of peak energy demand. But when are those times? Intuition might tell us that peak demand often happens in the late afternoon, when everyone comes home from their day and turns on lights, air conditioners, washing machines, televisions, and computers. It might also tell us the lowest demand occurs while we sleep at night.

Descriptive analytics examines historical electricity usage data to confirm our suspicions. This type of data analysis also helps electric companies set prices, which sometimes means that electricity rates during the low-demand night time hours might even be negative! What a great time to charge your electric vehicle - you're not only reducing pollution, but you even get paid for using electricity to charge your car. As electric vehicles gain traction in the marketplace, it will be interesting to see if they create a second peak during the night. Data analytics will help us find the answer.