Data is everywhere; it comes in different sizes and from different sources. There’s data in each second of recorded security camera footage, every open-ended survey question response, and even in the quarterly grades of all the students in a given school district. However, until this data is sorted and analyzed, none of it is actionable.

Each day, an estimated 2.5 quintillion bytes of data are created. The past two years alone accounted for 90% of this data creation. But what to do with it all? This is where data analytics comes in.

Definition of Data Analytics

Data analytics is the set of techniques used to analyze raw data (unprocessed data) in order to extract relevant information, trends, and insights. This process includes collecting data, organizing it, and storing it, then performing statistical analysis on the data.

Once the information is collected, conclusions can be drawn from it, which can be used for problem-solving, business processing, decision-making, and predictions that can inform what a company’s next steps should be. This process relies on disciplines like mathematics, statistics, and computer programming.

Big Data Then & Now

Big data is not a new concept. As early as the 1950s, before the use of the term “big data,” companies and organizations used basic analytics, such as manually examining numbers on a spreadsheet, to identify trends and key insights.

As the field of data analytics evolved, so did the speed and efficiency by which data was examined and useful insights applied to immediate decision making. The faster an organization could extract and use information from data, the more of an edge they would have over competitors. Today, a variety of techniques and methods exist for analyzing data, which depend on the aim of analysis and the industry in which the insights will be applied.

Preparing Data for Analysis

Working with raw data is an intensive process that has many steps before the actual data can even be analyzed.

  1. Set guidelines. Before you do anything else, it’s important to discern the data requirements, as well as how the data is grouped.
  2. Collect the data. Data collection can be done through many sources, such as cameras or computers.
  3. Import the data. After the data is collected, it has to be organized on a spreadsheet or other form of statistical data software.
  4. Clean the data. This entails scrubbing and checking it to make sure no error or duplication is present, and to confirm that it is complete.

The 4 Types of Data Analytics

Generally speaking, there are four types of data analytics.

  • Descriptive Analytics measures data trends that occur over a designated time period.
  • Diagnostic Analytics is concerned with why an event happens and any hypotheses can be used to explain this causality.
  • Predictive Analytics suggest what events may unfold in the near future.
  • Prescriptive Analytics is an advanced type of analytics that involves suggesting a course of action or solution based on a noticeable trend.

Why Do Businesses Need Data Analytics?

A background in data analytics has many pragmatic and timely uses. It can provide a detailed view of customers, which eliminates the guesswork from content creation, product development, and marketing campaign planning. Increased audience awareness means that customers can be reached more effectively with targeted marketing efforts.

This focus on audience expectations allows businesses and organizations to streamline operations, cutting down on the time wasted creating irrelevant content or ads and ultimately increasing conversions, subscriptions, or ad revenue.

The field of data analytics is quickly evolving as the vast amount of raw data continues to grow. Data analytics has applications in the security sector, for detecting, predicting, and ultimately preventing fraudulent activity at financial institutions. It can provide risk management solutions, increase the efficiency of supply chains, and even increase customer engagement on social media outlets.

In fact, data analytics factors into many of the services we use on a daily basis. Popular streaming services like Netflix use data analytics to improve user experience. Personalized recommendations on Netflix constitute over three-quarters of viewer activity.

Methods of Data Analytics

Several methods are currently used for analyzing data:

  • Cluster Analysis involves arranging the elements in a dataset around their similarities to glean hidden patterns or trends.
  • Cohort Analysis relies on historical data in order to study and compare one segment of users’ behavior that can ultimately be combined with others that have similar characteristics.
  • Monte Carlo simulation is often used for risk analysis to model the likelihood of different outcomes occurring.
  • Regression Analysis is performed to understand the relationship between variables and to decide how changes to certain variables may affect the others.
  • Factor Analysis allows those working with a large set of data to reduce it to a more manageable size.
  • Time Series Analysis examines trends or time-series data and has applications in economic or sales forecasting.
  • Sentiment Analysis attempts to organize qualitative data into themes in order to classify and interpret it.

What Careers Are Available?

Professionals with a background in data analytics have high-paying career prospects.

Those who pursue the Sports Data Analyst track gather insights on the performance of teams and individual athletes. This increasingly popular field is transforming industries like the NFL and NBA.

Customer Data Analysts who collect information about customers and use it to inspire new ideas for generating revenue are currently in demand in many sales and marketing professions. In addition, another popular industry right now is Healthcare Data Analysts. As patient records are being digitized, healthcare organizations, pharmaceutical companies, and health insurers seek out those with data analytics training to sort through this large mass of data.

Salaries for those with a background in data analytics are typically high. Senior Business Analysts earn between $63K and $115K, and Quantitative Analysts make $58K-$131K. In 2020, the mean salary for an Operations Research Analyst was $86K, according to the Bureau of Labor Statistics (BLS).

The Future of Data Analytics

As the amount of data used on various platforms continues to increase, challenges are anticipated in the future with visualizing it all. Augmented Analytics, or AI-Driven Analytics, is already helping automate data management by incorporating technologies such as natural language generation, analytics, and machine learning.

In addition, deep learning, a kind of machine learning, allows users to create a complex mathematical structure, or neural network, which is able to learn from a data structure. Deep learning can pinpoint anomalies as well as offer predictions. These changes in the field of data analytics are increasing the speed at which data can be sorted, organized, and transformed into stories that provide valuable information for companies and organizations.

In the future, new departments devoted to data visualization will likely be developed to meet the increasing demand for data extraction. Emerging technologies, along with artificial intelligence, are likely to be used more frequently to deploy machine learning and automation for data analysis. More than ever, there is a need now for those with training in Data Analytics to help take this field into the future.

Start Learning Data Analytics with Hands-On Classes

Are you interested in learning data analytics? If so, check out Noble Desktop’s Data Analytics courses available live online or in NYC. In-person classes are available at Noble’s NYC location, and live online Data Analytics classes are also offered from anywhere in the world.

You can also locate other Data Analytics courses in your area using Noble Desktop’s Classes Near Me tool. These classes cover topics such as Python, R, Tableau, Excel, and other important tools for analyzing data.