Data analytics vs. data analysis: Know the difference

Today, most businesses run on data. And with so many data types, formats, tools, and uses, there are also a lot of technical terms to describe them. Many of these terms are related or similar but with crucial differences. A common example involves "data analysis" and "data analytics."

Not understanding the difference between the two may lead you to make mistakes with marketing campaigns or IT process planning. You might even end up purchasing the wrong data management tools as a result.

So what is the difference between data analysis and data analytics? Let's answer that question by first defining what data analysis is.

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Data analysis is one of those terms whose definition seems obvious. In data analysis, we take raw data and transform it in a manner in which we can interpret it so that it can be evaluated to determine what steps we should take in response. This type of analysis typically focuses on historical data that a business has.

With data analysis, the transformation includes cleaning and organizing the data and modeling it for interpretation and evaluation, such as patterns, trends, correlations, and outliers.

Types of data analysis

While many types of specialized analysis are specific to certain data types and industries, data analysis typically falls within four distinct categories: descriptive, diagnostic, predictive, and prescriptive.

Descriptive data analysis is concerned with identifying and chronicling the main features of a data set. In a business context, descriptive data analysis usually includes exploratory data analysis and inferential statistical analysis.

Exploratory data analysis is used to evaluate and summarize the main characteristics of a data set and draw a hypothesis.

Inferential statistical analysis is a form of data analysis that uses statistics to draw conclusions about relationships found in the data.

Diagnostic analysis explores why a particular phenomenon occurred—the cause and effect. For example, if a business's third-quarter performance were weaker than the preceding quarter, you'd use diagnostic data analysis techniques to determine why.

Predictive data analysis can extrapolate trends in past data to predict future performance. Businesses rely on these forecasts to help them manage inventory, plan new product launches, craft promotional events, and make other key decisions.

Prescriptive data analysis leverages descriptive, diagnostic, and predictive analyses along with huge data sets to make informed decisions. Today, prescriptive data analysis is the province of AI-powered business applications. 

Finally, there is text analysis, also known as text mining or natural language processing (NLP). This is where insights and meaning are pulled from unstructured textual data. It includes techniques such as sentiment analysis, topic modeling, and text classification to analyze and interpret large volumes of text data.

What are common types of data analysis techniques?

Data may be either qualitative or quantitative. And many types of qualitative and quantitative techniques are available to evaluate different data sets. 

Some of the most common qualitative data analysis techniques include:

• Content analysis

• Conversation analysis

• Discourse analysis

• Grounded theory

• Interpretive phenomenological analysis (IPA)

• Narrative analysis

• Sentiment analysis

• Text mining

Quantitative data techniques include:

• Cluster analysis

• Cohort analysis

• Conjoint analysis

• Factor analysis

• Gap analysis

• Hypothesis analysis

• MaxDiff analysis

• Regression analysis

The above list is not exhaustive. Moreover, many business research studies use qualitative and quantitative data techniques to generate insights.

Data analytics is a broader term that includes analyzing the data in a manner that helps you identify the best decisions for growth. It includes data analysis but also includes collecting centralized data, creating data summaries in text or visual form, identifying patterns and trends, and other processes that help inform effective decision-making.

What are the main differences between data analytics and data analysis?

Data analytics is a broad set of processes involving transforming raw data into insights that can lead to better decisions. On the other hand, data analysis is one of the specific processes involved in data analytics. 

Data analytics practitioners are more concerned with diagnostic, predictive, and prescriptive techniques. At the same time, data analysis users generally use descriptive, exploratory data, and inferential statistical analysis to discover what the data is saying. 

Businesses use different software applications for each type of data examination.

Examples of data analytics vs. data analysis

When you're engaged in data analysis, you're looking to examine the data to understand the relationships you can find in it. You may be engaged in descriptive data analysis and inferential statistical analysis to summarize the data and its insights.

For example, you might evaluate consumer purchasing behavior of consumers exposed to various ads on social media to determine whether the latter influenced the former to any and to what degree.

From a data analytics perspective, you might evaluate those findings in context to your advertising spending to determine whether continued, increased, or decreased social media ad spending would likely yield better performance.

Data analytics is the approach you take to determine whether you should take a specific action. Data analysis is the first step in data analytics and tells you what performance is like from existing data.

In marketing, you use data analysis to determine user preferences for a new product based on focus groups and survey results. However, data analytics would also examine financial data across the business, economic conditions, consumer sentiment, and other relevant factors to help determine whether launching the new product would be a sound decision.

As data analysis is a subset of data analytics, both analytics and analysis concern themselves with transforming data for evaluation. 

Analysis transforms the data and tells you what the data says. Analytics takes things one step further and helps you evaluate your performance if you take a specific action based on what the data says.

Business intelligence predicts future performance by analyzing historical performance. Conversely, analytics predicts future performance by creating models based on data from relevant variables.

Four of the most common data patterns in business are cyclical, horizontal, seasonal, and trend.