What is Data Analytics and How is it Used?

Data analytics is the science of analyzing data in order to draw insights and make decisions.

While related, it is important to note that data analytics is different from data mining. Where data mining extracts information from large data sets, data analysis inspects, cleans, transforms and models the data to identify patterns and insights that influence business objectives.

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What are the Types of Data Analytics?

The field of data analytics is fairly extensive in its range of applications, but there are primarily four types of analysis used across the majority of industries and disciplines.

While each of the four main types of data analytics is unique in its focus, they each represent a step in the process of data analysis as a whole. The following table shows the steps of data analysis techniques in order.

Step Type Description

1. Analyze what happened

Descriptive Analytics Provides insights into the past performance of key business metrics
2. Analyze why it happened Diagnostic Analytics Supplements descriptive analytics by taking those findings and digging deeper to understand the root causes
3. Project what will happen Predictive Analytics Builds on the root causes of findings and past events in order to forecast what may happen in the future
4. Determine what should happen Prescriptive Analytics Reviews all analysis findings to understand what happened, why it happened and whether it’s likely to happen again, but most importantly to prescribe what to do about it

These types of data analysis techniques each uncover insights that many businesses need in order to be effective. However, when they are used in combination, they provide a big picture view of a business’ needs and areas of opportunity.

How is Data Analytics Used?

Data analysis is can be used in multiple ways that help companies and organizations succeed.

Applying data analysis within a business setting is known as business analytics. The main goal of business analytics is to leverage insights in order to inform strategy and achieve business objectives.

Data analytics can have influence on the following areas of business:

  • Budgeting and forecasting
  • Risk management and mitigation
  • Marketing and sales
  • Research and product development

The insights gained by creating and testing theories using a business’s data can result in increased operational efficiency and improved business performance. For example, identifying trends in manufacturing downtime can assist with capacity planning. Similarly, proving what works best for consumer engagement can help improve customer satisfaction.

Here are a few more examples of areas of business that can be influenced by data analysis:

  • Targeted marketing campaigns based on user engagement
  • Risk management and threat mitigation through systems auditing
  • Supply chain logistics to help optimize inventory, shipment schedules and so forth
  • Product development according to audience analysis, market demand and the competitive landscape

Why is Data Analytics Important?

Data analytics is an important field in modern business. Now that organizations can track almost everything digitally, they have to find ways to deal with big data. Knowing how to extract value from data is a modern business challenge, making the data analysis field more attractive than ever.

Data analysts are needed to help businesses properly store and understand their data both consistently and efficiently. With skilled analysts working behind the scenes to understand their data, businesses can make better decisions and improve customer satisfaction.

What is the Future of Data Analytics?

There have been many exciting advancements in data science and information technology that have allowed data analysis to become what it is today.

Here are a few trends that will likely impact data analytics in the future:

  • Innovation in AI and machine learning technology
  • Increased integration of diverse data sources
  • Operationalizing data analytics and business intelligence
  • A shift from big data to small and wide data

What Jobs are Available in Data Analytics?

If you’re interested in pursuing a career in data analytics, the education and hands-on training you can receive through an accredited1 school like DeVry University may help you as you prepare to enter this field.

Often, a certificate program is a great place to start when you’re just beginning your path toward earning a degree. DeVry offers an Undergraduate Certificate in Data Mining and Analytics that can be completed in as little as 1 year and 4 months or even faster with qualifying transfer credits2.

Graduates of the Data Mining and Analytics certificate program may consider but are not limited to, the following positions:

  • Software application developer – A software developer is responsible for developing and modifying code.
  • Software programmer and analyst – Programmer analysts are responsible for developing, testing, patching, updating and otherwise maintaining software and systems.
  • Data analyst – The data analyst role uses data to solve business problems like streamlining logistics.
  • Data systems developer – Data systems developers develop and implement applications for back-end systems.
  • Data modeler – A data modeler creates visualizations of data flows, models and integrated elements.
  • Data miner – Data miners identify hidden patterns or anomalies in data and how they relate to the business.

Build Your Foundation in Data Analytics at DeVry

If you’re an inquisitive, methodical problem solver and an outside-the-box thinker, then you may be well suited to a career as a data analyst. DeVry's Undergraduate Certificate in Data Mining and Analytics is a great way to start learning the principles and skills you need to become proficient in data analysis.

Find out how DeVry can help you take the first step in your journey.


1DeVry University is accredited by The Higher Learning Commission (HLC), www.hlcommission.org. The University's Keller Graduate School of Management is included in this accreditation.
2Not including breaks. Assumes year-round, full-time enrollment.