What is Data Analytics?

Consider how many times over the past decade that you read an article referring to big data as “the future” or “the new oil.”

In 2021, big data is a big cliche.

To many, big data analytics is just a buzzword—or shorthand for innovative technology that most laypeople don’t fully comprehend. Gartner dropped the term from its hype cycle five years ago and today, big data is just another fact of today’s business landscape. Despite all of the buzz and widespread adoption, few businesses have realized the full potential of big data.

In this article, we’ll take things back a bit and answer the question, “what is data analytics?” We’ll then break down the concept of big data analytics and why, despite the hype, it remains one of the most transformative technologies.

Data Analytics Definition

Before we dig in, let’s first settle on a data analytics definition. While data analytics is a broad category, the term is best described as the science of analyzing raw data to extract insights.

Data analytics is typically used to provide operational insights into a business. This process involves looking back at historical data, then pulling out lessons learned from the past to solve the complex business problems of the present.

Historically, data analysis was conducted by humans who would manually scan through data, looking for relevant information with a specific goal in mind. Today, many of the techniques and processes associated with data analytics have been automated into processes where algorithms and mechanical processes analyze, categorize, and extract information for human consumption.

Why is Big Data Analytics Important?

Thanks to big data analytics, organizations can now leverage their massive data sets and use them to identify new opportunities and incoming threats and to optimize operations.

According to the 2019 New Vantage Partners Big Data and AI Executive Survey, AI and big data adoption are on the rise. 92% of respondents increased spending on AI and big data initiatives, and 62% say they’ve already seen measurable results from those investments. Additionally, nearly half of participants believe their organization competes on data, while 31% consider themselves “data-driven.”

Forrester’s Data Strategy & Insights 2019 Survey found that 58% of respondents say their organization has appointed a chief data officer to lead new initiatives as big data becomes an increasingly critical factor in driving business value.

While big data analytics adds value in several different ways, here are a few of the primary benefits it provides:

• Faster, smarter decision-making. Businesses can analyze large amounts of data instantly and make informed decisions on the fly.
• Cost savings. Big data technologies allow companies to quickly identify better ways to do business and provide a cost-effective data storage solution.
• A deeper understanding of what customers want. Big data unlocks insights that help companies pinpoint customer needs and gauge satisfaction. This gives companies the information they need to develop lasting customer relationships and offer higher-quality products and services.

As more companies adopt Internet of Things (IoT) technologies, cloud-based tools, and omnichannel marketing campaigns, the sheer volume of data stands to increase exponentially.

Successful adopters have a powerful tool at their fingertips for driving profits, making better decisions, and improving customer satisfaction—giving them a significant edge over competitors that fail to make big data a priority.

How Does Big Data Analytics Work?

Big data is a term used to describe these huge amounts of raw, structured, and unstructured data captured from a diverse range of sources.

Big data is typically defined as having four main characteristics known as the “4Vs,” which break down as follows:

• Volume. References the size of the data sets that need to be analyzed, processed, and stored (often larger than terabytes or petabytes).
• Velocity. High-velocity data is generated at a rapid pace, which can put a strain on traditional data processing systems.
• Variety. Big data includes both structured and unstructured data from a wide range of sources. Examples include everything from IoT sensors to marketing metrics, application logs, and credit card swipes.
• Veracity. Veracity describes the quality of the data that’s being analyzed. Low-veracity data offers little value and can bury high-quality insights and take up valuable storage space.

The 4Vs underscore both the promise of big data and the challenges it presents.

For example, in a manufacturing setting, organizations might have data from IoT devices, production equipment, metrics from multiple SaaS tools used by office workers, etc.

To capture accurate, real-time insights from all of those sources, that company will need more computing power and even some support from AI to gather and analyze data on-demand.

Organizations must be able to answer the following questions to implement a strategy that provides real value:

• How do we store the massive volumes of data?
• How do we protect the data?
• How do we extract knowledge from the data?
• How can we ensure the data is accurate?

Big data analytics allow organizations to analyze their data from heterogeneous sources (think sensors, social media feeds, video, application logs, and more) to uncover patterns, correlations, and other insights imperceptible to the human eye—often in real-time.

That said, many tools/capabilities fall under the broader category of big data analytics, including data cleaning, storage, and management, as well as data mining and warehousing.

The common link here is that big data must be analyzed and processed with advanced analytics and algorithms to tackle the challenges of the 4Vs and leverage insights toward improving products and processes.

Types of Data Analytics

There are four main types of data analytics, each of which help organizations find answers to their questions by analyzing unstructured data and turning it into usable information.

4 Types of Data Analytics to Drive Business Value

Each category serves a different purpose, which we’ll explain in more detail below.

• Predictive (The “If”). Predictive analytics helps companies forecast trends based on current circumstances by analyzing a range of co-dependent variables to make predictions. These tools are used to examine hypothetical situations, determine the probability of a future event, or estimate the time for an event to happen.
• Prescriptive (The “How”). Prescriptive analytics takes predictive analytics a step further, applying AI to big data analytics to help predict outcomes and recommend the best course of action.
  By using machine learning technology, prescriptive analytics platforms help answer questions like “what happens if we do X?” and “what is the best action?” without literally testing each variable. Additionally, prescriptive analytics may even suggest new variables that may yield a better outcome.
• Descriptive (The “What”). Descriptive analytics answers essential questions (what, when, where, and how many) that tell users what they’re looking at. They might be used to summarize information about a past event, identify patterns, and interpret historical data to inform better business strategies.
  Descriptive analytics can fall into two categories: canned reports and ad hoc reports. Canned reports are pre-designed templates that contain information related to a specific subject. Think marketing performance or profit and loss reports. Ad hoc reports are “custom builds” and are useful for collecting information about a specific query.
• Diagnostic (The “Why”). Diagnostic analytics is used to examine data to understand why something happened. These platforms often allow users to run queries and drill-downs to get more detail from a report. For example, if you notice that sales were down last month, you might use a drill-down to find out that half of your reps were on vacation, and as a result, fewer deals closed.

What is the Role of Data Analytics in Your Business?

While the buzz surrounding big data has long since passed, with the onset of technologies like 5G, edge computing, and IoT, data analytics or, more specifically, real-time analytics are now more critical than ever. Business intelligence is now embedded in every device, system, and sensor, and those without a solution for managing this massive mountain of information will get left behind. As such, organizations need to invest in a data-driven culture that focuses on finding answers to critical questions and the solutions that serve up insights they can act on.

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