The Science of Your Data: Past, Present, and Future

The Science of Your Data: Past, Present, and Future

Philip Lima
Tuesday March 28, 2017

Data science has been around since the 1920s (though it was not called “data science” at the time), back when Wall Street traders first began trying to use mathematical algorithms to predict future market changes. Of course, only a very few of them were able to foresee the coming of the 1929 Stock Market Crash.

Needless to say, the science of data has come a long way since then.

The Past – The Rise of Data Science The label “data science” did not come into use until decades later when, in 1962, John W. Tukey coined the term in his book The Future of Data Analysis. In that book, he argued that the field of data analysis should instead be considered as a form of “empirical science,” and referred to as such. By 1977, Tukey had taken this idea a step further in his work Exploratory Data Analysis, in which he argued that data should not just be seen as something gathered for the experimentation of hypothesis. Instead, he argued that data could and should be used to help form scientific hypotheses that could then be tested further.

Back then, most scholars considered what we now call “data science” or “data analytics” to be just a form (albeit a very advanced form) of standard statistics. It was widely viewed as nothing more than a complicated numbers study, something used primarily in science labs or engineering projects. Rarely was it seen as a valuable resource by businesses, retail firms, or medical practices. Over the last 30 years, however, advancements in computers and the rise of the internet have brought data science and analytics into the mainstream of our everyday lives.

"Advancements in computers and the rise of the internet have brought data science and analytics into the mainstream of our everyday lives"

The Present – Modern Data Science and Analytics At its core, the goal of data science is to identify opportunities, model human behaviors, and solve problems by taking the data of the past in order to affect the outcomes of the immediate and distant future—for example, trying to identify a future need that does not yet exist, as well as a potential future technology, strategy, or product (which also does not yet exist) that would be able to fill said need.

This is where analytics tools come into play. These solutions take the science of data out of the laboratories and place that power into the hands of today’s business owners, often through a combination of the below three analytical practices:

Predictive Analytics – The practice of extracting information from existing data and using it to determine patterns in order to predict future trends or outcomes. However, it is important to note that “predictive” analytics does not actually predict what will happen in the future. Instead, it provides a model of likelihood and probability for future events or outcomes. Think of it as similar to a weather forecast. The weather forecaster can say, with an amount of certainty, that it may or may not rain on a certain day, and often with a percentage of likelihood. Predictive analytics also forecasts what might happen, based on what has happened, within a certain range of reliability that allows for better management of risk.

Prescriptive Analytics – often used in conjunction with predictive analytics, this area of analytics is dedicated solely to identifying the most favorable course of action for a given scenario based on the data provided and a specified desired outcome.

Machine Learning – this involves the use of artificial intelligence that allows computers to learn from data without the need for additional programming. The field of machine learning focuses on creating programs that have the ability to adapt and evolve when provided with new data. There is a zoo of machine learning approaches and use cases. It’s been said that ten years ago, it was difficult to find software that used machine learning. In ten years, it will be difficult to find software that is not using machine learning.

The Future of Data The sky really seems to be the limit when it comes to the future of how data science and analytics will evolve in the decades to come. As the capabilities of artificial intelligence continuously improve and the availability of big data expands, the possibilities seem to become endless—automated medicine, automated legal aid, real-time decision making strategies for Venture Capitalists, you name it.

The future of data gets brighter every day. And the future of your business can be brighter with analytics.

Contact us if you would like to discuss how we can help make you more successful with data analytics.

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