What Is Data Analytics and Why Is It Important?

Whether or not you know it, you’re constantly generating data about yourself: where you go, what you buy, and what you stream or post on social media. But that data doesn’t just sit in a database. Thanks to data analytics—the process of interpreting this raw data and generating usable insights—businesses can now use that data to drive profits and better serve their customers.

Marketing and sales agencies aren't the only industry interested in data analytics. Corporations and manufacturing companies use data analytics to develop products, streamline workflows, increase efficiency, and develop industry awareness. Medical researchers and healthcare systems may rely on data insights to help make life-or-death decisions too.

Want to know more about data analytics and why it’s important? Then keep reading.

Data analytics follows this four-step process:

Data Collection

Once data is collected into a database, it’s often then grouped according to a defined value. That could be something like your age, gender, income, or where you live. Oftentimes the way in which data is grouped is dependent upon the industry you’re in. So if you’re in manufacturing, let’s say, it may be grouped according to plant location, production time, or safety concerns. Medical researchers or healthcare providers may group according to disease, clinical trials, side effects, or death and survival rates. 

Data Processing

The second step is to clean and convert the raw data into information that can be easily accessed and understood. A data analyst will check for duplicate information, missing information, correlations, or inaccuracies. When completed, the data is ready to be evaluated by a person or entered into software programs.

Data Visualization

Data analysts look for trends, relationships, variations in sampling, bias, patterns, or several predetermined values based on the source and outcome desired. To make the data accessible, data analysts create spreadsheets, visual charts, or infographics using data analytics tools.

Data Analytics Visualization Tools

There are several data analytics tools used to develop insights. The tools are typically software-based applications analysts use once the data has been processed and is ready for visualization.

• Statistical Analysis System (SAS)

  SAS is one of the most popular tools most people interact with daily. This software can gather and report data instantly by reading spreadsheets and interpreting databases. With SAS, you can generate graphical reports, develop applications, improve the quality and efficiency of outcomes, perform research, and more.

• Microsoft Excel

  Excel is more than just columns, rows, and cells. The data that can be inputted and generated can allow for real-time data aggregations, visually represent data, calculate outcomes, and populate software and websites.

• Microsoft BI

  Microsoft Business Intelligence (BI) is a significant step up from Excel data management. It incorporates artificial intelligence, allowing analysts to ask questions through natural language or apply algorithms to data sets for deeper insights than aggregation and statistical calculations.

• R Analytics

  R is a go-to, free, open-source program for computing statistics, visualizing data, and data simulation. Data scientists are drawn to R because it is a language or code-based tool that can be manipulated to analyze data in unique ways. Billion-dollar companies like Google, Facebook, Bank of America, and Pfizer use R for their cutting-edge graphical ability, fast results, and statistical tools.

Informed Decisions Based on Data

Through data visualization and interpretation, refined and easily-digestible information can be broken down into actionable results. Some real-world changes include new marketing strategies, business resource reallocation, big tech advancements, updated financial algorithms, enhanced healthcare diagnostics, and improved safety laws.

Once the data is collected, processed, and visualized, there are four different ways of categorizing your findings.

1. Descriptive Analytics — Think of this as the "what happened" stage of the data. Whether there's an increase, decrease, or no change, this approach focuses on the changing variations in the data sets.

2. Diagnostic Analytics — Digging a little deeper, diagnostic analytics looks at why the changes did or didn't happen. Analysts look at factors such as groups, sources, and influences to determine a possible cause.

3. Predictive Analytics — One of the most popular techniques when making decisions, this approach takes into account the possible futures. Weather forecasting, stock market outlooks, and even your daily horoscope use predictive analysis to anticipate outcomes.

4. Prescriptive Analytics — This form of data analysis provides suggestions, advice, and conclusions that help decision-makers determine the best course of action.

As an advanced data analytics technique, artificial intelligence (AI) is transforming the world. AI is a part of your daily life, from apps and streaming platforms to smart home devices and how you spend your money. AI uses data analytics in real-time to influence your social media habits, identify and alert you to fraudulent activity, create personalized choices, and much more.

The rapid integration of AI-driven tools like machine learning, image recognition, intelligent bots, advanced algorithms, and natural language processing is powering new data analysis methods. AI algorithms can instantly perform all four types of analytics—descriptive, diagnostic, predictive, and prescriptive—and could eventually lead to automated decision-making.

Artificial intelligence cannot fully replace data analysts, even with data mining, text and video analytics, and intelligent behavior. AI can rapidly collect, process, visualize, and present outcomes, especially when it comes to video and text analytics. But people are still firmly in the driver's seat when it comes to applying data analytics insights.

The roles of data analysts and scientists are often confused, but there are differences between the two. Data analysis utilizes statistics, outcomes, and strategy to generate insights. Typically, data scientists are hands-on with hardware and engineering. But the biggest difference is what they do with that data. A data analyst interprets the data model while a data scientist creates new processes for data modeling.

Data Analytics plays a role in every major industry. Gaining insights through collection, processing, and visualization has led to incredible technological advancements in a short period of time. Financial companies, the healthcare industry, manufacturing, government, entertainment, and more use data analytics to create smart products, better opportunities, safer communities, and life-saving medicines.

Big data is growing, and people who can process, interpret, and generate insights are in high demand. The Bureau of Labor and Statistics (BLS) expects the field to grow 25% between 2020 and 2030. Now is the time to pursue a career in data analytics, and the University of South Florida (USF) has one of the top programs in the country.

The USF CTPE Data Analytics Bootcamp, in partnership with Microsoft, will have you job-ready in six months. You'll learn technical skills like fundamental business statistics, analytics, and visualization tools, advanced analysis techniques, and a skill hiring managers say is lacking in many analysts—strategic thinking. Plus, you'll have a weekly one-on-one mentorship with an industry expert.

For more information about the program, check out the USF Bootcamps FAQs and consider enrolling in one of the most sought-after professions today.