How Big Data and Machine Learning revolutionized analytics


We’re living in a data-driven world, which makes analytics one of the most in-demand fields in the technology industry. In this blog post, we’ll look at how analytics has evolved, the cycle of data analysis and the most popular analytics tools.

What are analytics?

Analytics allow businesses to collect, analyze and use data so they can make better, more informed decisions.

In the past, companies were mostly concerned with collecting and storing data, such as customer information, accounts and business models, but they weren’t necessarily using this data to help inform decision making.

Business are now using that strategic information more than ever to improve products and services. This is where analytics comes in. Analytics skills have an increasingly larger role in companies and with the boom of Big Data and the Internet of Things, the field will continue to evolve even more.

The evolution of analytics

The foundation of Analytics is Statistics, which began as a formal field around the year 1700. It was initially an arduous task, where the collection, exploration and further analysis of data was a long and manual process.

With the creation and evolution in computer technology, a new enhanced area was discovered. Larger samples, complex simulations and new statistical models were introduce and tested.  However, it was still a profession mostly reserved for statisticians.

Advances in other technology fields, like the introduction of Databases and Data warehouses saw the creation of a new exploration area that combined statistics and computer science—Business Intelligence. Business Intelligence is a process for analyzing data and presenting that information to make more informed business decisions. Data analytics now encompasses much more than statistics—it’s the combination of statistics, computer science and mathematics.

The advances in fields like Big Data, Machine Learning and Predictive Analytics means there’s now much more data that you can extract, analyze, and find correlations.

Cycle of analytics 

There are five stages to the Analytics process that are generally followed.

  1. Explore
    At this stage, you’ll engage in data mining. You’ll explore the data to understand what data you’re looking at, what’s stored there and what data you need to clean. Data is never perfect. There’s always a transformation and cleaning that you have to do in the exploration phase.
  2. Analyze
    At the Analyze stage, you look more closely at the data and report on your findings as well as analyzing probability and correlations.
  3. Predict
    With predictive analysis, you look at what has happened so far, and predict what will happen from the data, using regression models and other predictive tools. You analyze your data, your results, and determine your predictions. At this stage, you’re forecasting and trending what will potentially happen.
  4. Learn
    The next stage of the data analysis cycle is Learn. At this stage, you already have some results and predictions. However, you now have to be able to learn from your findings and adjust your model accordingly. Whatever tool or software you’re using, you have to put that machine learning or artificial intelligence into place.
  5. Reporting – the key to success
    Once the analytics cycle is complete, the key to success is in your reporting. This means that as much as you analyze, think, find and predict, if you don’t know how to present your analysis and make it readable, you won’t achieve the results you’re looking for.


The three post popular analytics tools 

There are several analytics tools that you can use to analyze data and achieve results.


SAS is one of the industry leaders in analytics platforms. In fact, SAS has 33% of the market share on advanced analytics.


R is a popular open source statistical and graphic programming language that has a variety of capabilities.


Python is an open source programming language that is also widely used for analytics.

All three provide limited visualization capabilities. To improve your reporting, data visualization tools like Tableau and Qlikview allow you to put their tool on top of your clean data so you can report from there.


If you’re thinking about an analytics career, Sheridan’s Faculty of Continuing and Professional Studies offers the SAS Base Programming. Visit for more information.



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