Data Science, Big Data, & Predictive Analytics – A Combination Revolutionizing the World

About 70.5% of research and development organizations use Data Science on a consistent basis. This is complemented by the estimation that places the market value of Big Data at USD 103 billion dollars by 2027 — almost double from what it is at the time of writing this article. 

Naturally, this data is being used to predict patterns for better outcomes and improved customer satisfaction. However, such analytics can’t be grouped under one umbrella, for it suffices a combination of different technologies and altogether distinct subjects working as one. 

That’s precisely what this article emphasizes and aims to discern with data science, big data, and predictive analytics at the heart of the discussion.

Data Science, Big Data, and Predictive Analytics — A Primer

In concrete terms:

Data Science is a group of related disciplines, including statistics, hypothesis testing, data mining, machine learning, pattern recognition, and knowledge discovery. It involves using both computers and mathematics to extract knowledge from data and subsequently use that knowledge to make better decisions. By doing this, it seeks to provide the answers to questions that would be difficult or impossible to answer otherwise. 

Big Data refers specifically to the massive quantities of data generated by almost every industry worldwide. This is a growing phenomenon (think: Moore’s Law of Exponential Growth), and with it, so too are the types of questions that can be answered by analyzing the data.

Predictive analytics, on the other hand, is the use of data to predict future events. This data can be gathered in a variety of ways, including machine learning algorithms. Machine learning, for example, employs a software system that learns from experience to make decisions on its own. As a result, it sets in motion a process whereby it performs mathematical calculations based on the data provided. These calculations often help predict future actions and outcomes.

How Do Data Science, Big Data, and Predictive Analytics Work Together?

First off, data science is a broad field — one that consolidates accessing large data sets (Big data) and leveraging those data sets to make predictions (Predictive Analytics). In that light, Data Science can be understood as the Engine, Big Data as the driving force behind this engine, and Predictive Analytics as the approach to deliver the desired outcome of this engine.

Data Science and Big Data

Now, let’s understand the proficiency and prominence of the above combination with the use of examples. 

Example #1

A machine learning algorithm can be used to determine the value of a customer from various variables. Such variables could include age, gender, marital status, the average income in the zip code, and even if the person owns or rents their home. The algorithm can then use this data to predict the customer’s value and somehow impact its profitability (for example: whether or not to issue them a credit card). This task could be difficult for humans to accomplish.

Consider this; a typical human brain cannot intelligently process all the variables. To compound the issue, the variables change over time (e.g. mortgage rates are currently at historic lows). The algorithm must take into account every variable and then determine how to put this data together into an accurate prediction that is constantly changing and updating itself.

Example #2

In order for a machine-learning algorithm to give meaningful results, it requires both input data and output data. In other words, the data must be both “open-loop” (input) and “closed-loop” (output). The open-loop is data that the algorithm uses to learn its mathematical calculations. To use a simple example: if you are trying to predict the optimal temperature for baking a cake, your input data might consist of such things as the type of cake, brand of flour used, quality of eggs, etc.

While not all of these variables will be necessary for predicting the temperature, they can provide an overall sense of what is essential in determining the temperature (since each variable varies in some way and may have some level of correlation with other variables).

Contrarily, the closed-loop data is the information that is required to make a decision. For example, in our cake baking example, the closed-loop data would be the final temperature used to check whether or not there was enough heat in the oven. This would represent how different variables affect the final result. 

Benefits of the Combination of Data Science, Big Data, and Predictive Analytics

Data Science has brought about a revolution in the way that we understand things. The biggest example of the same is the use of Data Science in combating the COVID-19 crisis. 

The focus of data scientists is, unsurprisingly, data science — that is to say, how to extract useful information from a sea of data and how to translate business and scientific informational needs into the language of information and math.

—Kathleen Walch, Forbes

 

1. Information-Based Optimisation

The accuracy of the data preparation and analysis that leads to data-driven decision-making is directly proportional to the quality of the output. If we have a poor understanding of our intended outcome, the result will be inaccurate results, wasted resources and time, and a lack of confidence in the product. 

2. Scalability

The combination of data science, big data, and predictive analytics to achieve a more accurate result is scalable. The process can be repeated again and again to achieve the same results. For example, when we collect data from all previous years’ sales of a product, we can correlate this data with different factors (such as time of year or weather) and find the best way to sell our product in the future as well as identify what times will be most profitable for us. 

3. Cost-effectiveness

The process of collecting data, using these tools to manipulate it, and then making predictions is comparatively cost-effective. For example, data scientists use mathematical models to identify patterns in the data. The accuracy of these patterns is determined by how relevant the data set is (i.e., the quality of the information). The more relevant and accurate this data, the better results we get from our predictive analytics — and with that accuracy comes more targeted production (cost-effectiveness).

Know the Challenge As Well

It is important to understand the underlying complex algorithms are underpinned by a straightforward concept: list the variables and then determine whether or not they have any correlation with one another. However, while this may sound easy enough on paper, it is in practice difficult for many algorithms to deal with a large number of variables.

In a Nutshell

Data Science, in this day and age, combines the methods, processes, and techniques of scientific research with those of engineering to produce new knowledge and innovations. As a result, it paves the way for quantification of technicalities and leveraging of data to predict accurate patterns — a concept that is, indeed, revolutionizing the world. 

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