What Is Data Analytics? An Overview for Beginners

There are many aspects to understanding data analytics, so where does one even get started?

Some may dive right into the programming languages used for analysis, others may look at how analytics are used to solve business problems.

For a simpler starting point, we’ll first explain the types of data being analyzed. Then, we’ll look at the process of data analysis, identify sources where data is collected, break down the different types of analytics, and finish with some trends.

But before we can get into any of the above topics, we first need to define data analytics.

What is data analytics?

Data analytics is the use of processes and technology, typically some sort of analytics software, to extract valuable insight out of datasets. This insight is then applied in a number of ways depending on the business, its industry, and other unique requirements.

This is important because it helps businesses become data-driven, meaning decisions are supported through the use of data. Data analytics is also helping businesses to predict problems before they occur and map out possible solutions.

While more businesses turn to data analytics to identify gaps, there are still plenty of people who could use some clarification. That’s why we’re starting with the root of data analysis: discerning qualitative data from quantitative data.

Qualitative and quantitative data

Data analytics is comprised of both qualitative and quantitative data. The makeup of these data types is important, considering it’s how it will be analyzed later on. Let’s start with qualitative data.

Understanding qualitative data

Qualitative data asks “why,” and consists of characteristics, attributes, labels, and other identifiers. Some examples of how qualitative data is generated include:

• Texts and documents
• Audio and video recordings
• Images and symbols
• Interview transcripts and focus groups
• Observations and notes

Qualitative data is descriptive and non-statistical, as opposed to quantitative data.

Understanding quantitative data

Quantitative data asks “how much” or “how many,” and consists of numbers and values. Some examples of how quantitative data is generated include:

• Tests
• Experiments
• Surveys
• Market research
• Metrics

Quantitative data is statistical, conclusive, and measurable, making it a more optimal candidate for data analysis.

With a grasp on the two types of data, it’s now time to see why data structures make such a difference as well.

Structured and unstructured data

Next, onto structured and unstructured data. How data is structured will determine how it is collected and processed and which methods will need to be used to extract insight. Let’s start with structured data.

Understanding structured data

Structured data is most often categorized as quantitative data. It is, as you may have guessed by its name, highly structured and organized, so it can be easily searched in relational databases. Think of spreadsheets and tables.

Some examples of structured data include:

• Names and dates
• Home and email addresses
• Identification numbers
• Transactional information

Structured data is generally preferred for data analysis since it’s much easier for machines to digest, as opposed to unstructured data.

Understanding unstructured data

Unstructured data actually accounts for more than 80 percent of all data generated today. The downside to this is that unstructured data cannot be collected and processed using conventional tools and methods.

To harness unstructured data, more modern approaches like utilizing NoSQL databases or loading raw data into data lakes will need to be considered.

Some examples of unstructured data include:

• Emails and SMS
• Audio and video files
• Social media
• Satellite and surveillance imagery
• Server and weblogs

Making sense of unstructured data isn’t an easy task, but for more predictive and proactive insights, more businesses are looking at ways to deconstruct it.

The data analysis process

Now that we know the anatomy of data, it’s time to see the steps businesses have to take to analyze it. This is known as the data analysis process.

Step 1: Define a need

The first step in this process is defining a need for analysis. Are sales dwindling? Are production costs soaring? Are customers satisfied with your product? These are questions that will need to be considered. Additionally, it's important to have a data management plan set in place. This will ensure that all the data coming in and out of your system is organized and accounted for. Many businesses rely on a data management platform (DMP) to store all data in one centralized hub.

Step 2: Collect data

A business will typically gather structured data from its internal sources, such as CRM software, ERP systems, marketing automation tools, and more. There are also many open data sources to gather external information. For example, accessing finance and economic datasets to locate any patterns or trends.

Step 3: Eliminate duplicates and inconsistencies

After you have all the right data, it’s time to sort through and clean any duplicates, anomalous data, and other inconsistencies that could skew the analysis.

Step 4: Analyze data

Now for the analysis, and there are a number of ways to do so. For example, business intelligence software could generate charts and reports that are easily understood by decision-makers. One could also perform a variety of data mining techniques for deeper analysis. This step depends on the business requirements and resources.

Step 5: Take action

The final step is putting analysis into action. How one interprets the results of the analysis is crucial for resolving the business problem brought up in step one. Your results should paint a clear picture of how to move forward. If not, this is the right time to re-evaluate your data analysis method and see where there could be gaps in your process.

Types of data analytics

Not all analyses are created equal. Each has its level of complexity and depth of insight they reveal. Below are the four types of data analytics you’ll commonly hear about.

1. Descriptive analytics

Descriptive analytics is introductory retrospective and is the first step of identifying “what happened” regarding a business query. For example, this type of analysis may point toward declining website traffic or an uptick in social media engagement. Descriptive analytics is the most common type of business analytics today.

2. Diagnostic analytics

Diagnostic analytics is also retrospective, although it identifies “why” something may have occurred. It is a more in-depth, drilled-down analytical approach and may apply data mining techniques to provide context to a business query.

3. Predictive analytics

Predictive analytics attempts to forecast what is likely to happen next based on historical data. This is a type of advanced analytics utilizing data mining, machine learning, and predictive modeling.

The usefulness of predictive analytics software transcends many industries. Banks are using it for clearer fraud detection, manufacturers are using it for predictive maintenance, and retailers are using it to identify up-sell opportunities.

4. Prescriptive analytics

Prescriptive analytics is an analysis of extreme complexity, often requiring data scientists with prior knowledge of prescriptive models. Utilizing both historical data and external information, prescriptive analytics could provide calculated next steps a business should take to solve its query.

While every business would love to tap prescriptive analytics, the amount of resources needed is just not feasible for many. Although there are some analytics trends, we can expect to take shape soon.

Data analytics trends

As data science becomes more commonplace in business, analytics will surely shift from being retrospective to more proactive and predictive. To validate this, we asked 10 industry experts who work with data for their opinions.

Here are some noteworthy highlights:

• One expert says real-time analytics are on the rise and have the potential to transform the way professional services operate. Real-time data delivers insight into what is happening right now and is optimal for predictive analytics.
• Another expert claimed machine learning will become so prominent, that it will leave human users to devise creative treatments to fix problems or maximize profits. Machine learning is incredibly trendy in data analytics today.
• An expert in social media listening technology spoke about the unique bond between data analytics and big data, and how companies analyze photos on social media for sentiment.

Wrapping up

So, what can you take away from this overview of data analytics?

We know that data can be descriptive and sentimental, or it can be conclusive and numerical. The way data is structured also plays a key role in how it’s analyzed.

When it comes to analysis, there is a general five-step process of defining the need, collecting data, cleaning it, analyzing it, and then interpreting it. Depending on the business requirements, interpretation can vary immensely.

Then, there are four types of data analytics. Some are retrospective, and others are predictive and proactive. The latter will become more commonplace with advances in artificial intelligence, machine learning, statistical modeling, and other data science disciplines.

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