What Is Data Analytics? A Clear Guide to Methods, Steps, and Use Cases

In the business world, the term "data analytics" is heard more and more often. However, many people have not quite grasped "how it differs from data analysis" or "what specifically to do." This article clearly explains everything from the meaning of data analytics to representative methods, the steps for actually proceeding, commonly used tools, and use cases in business.

What Is Data Analytics?

Data analytics refers to the series of tasks of organizing, processing, and verifying collected data, drawing meaningful insights from it, and putting those insights to use in decision-making. Its essence lies not merely in collecting numbers but in clarifying "what can be said from that data" and "what to do next."

For example, "last month's sales were one million yen" is mere data (a fact), but clarifying it to the point of "last month's sales grew to 120% of the previous month, and the factor was an increase in new customers" is data analytics. It helps to think of it as the process of turning data into "information" and then into "grounds for action."

The Difference Between Data Analytics and Data Analysis

"Data analytics" and "data analysis" are often used in roughly the same sense, and there is no strict distinction. That said, they are sometimes used differently with an awareness of their nuances.

• Analysis: Breaking things down into elements to clarify their structure and relationships. It carries a strong nuance of "dividing to understand."
• Analytics: Unraveling complex data with statistical and mathematical methods to find regularities and meaning. It carries a nuance of "deciphering" by making full use of computation and methods.

In practice, there is no need to use the two strictly differently; it is fine to consider both as referring to "the work of obtaining valuable insights from data." In this article, the term "data analytics" is used in this sense.

The Four Levels of Data Analytics (Classification of Methods)

The methods of data analytics are wide-ranging, but organizing them into roughly four levels based on "what you want to clarify" makes them easier to understand. The later levels are more advanced, and the value to business also grows.

Descriptive Analytics (What Happened)

This is analytics that aggregates facts that happened in the past and grasps the current state. It centers on aggregation and visualization, such as "how much were last month's sales" and "how many of which products sold." It becomes the starting point of all analytics.

Diagnostic Analytics (Why It Happened)

This is analytics that explores the "cause" of a fact that happened. It explores "why sales grew" and "why churn increased" by combining multiple data sets. Once the cause is known, it becomes easier to take effective measures.

Predictive Analytics (What Will Happen)

This is analytics that learns patterns from past data and predicts the future. It predicts "how much demand there will be next month" and "which customers are likely to churn" using statistical models and machine learning. It becomes material for judgment to take proactive measures.

Prescriptive Analytics (What Should Be Done)

This is analytics that, based on predictions, derives "what the best action is." It is the domain of optimization, such as "which measure to allocate a limited budget to." It is the most advanced, and in recent years it is a domain where the use of AI and optimization algorithms is advancing.

How to Proceed with Data Analytics (Basic Steps)

Rather than suddenly touching a tool, data analytics is more likely to lead to results when proceeding through the following steps.

1. Clarify the Purpose and Question

The most important thing is to decide "what you want to clarify" first. If you start touching data with a vague purpose, only a list of numbers is produced and it does not lead to action. Make the question you want to solve concrete, such as "I want to lower the churn rate" or "I want to know which measure worked for sales."

2. Collect Data

Gather the necessary data according to the purpose. Sources vary, including in-house sales data and customer data, web access logs, and surveys. The point is to gather data by thinking about "which data is needed to answer the question."

3. Preprocess and Clean the Data

Collected data often contains "dirt" such as missing values, duplicates, and notation inconsistencies, and correct analytics cannot be done as-is. By removing unnecessary data and aligning the format, you prepare it into a state where it can be analyzed. Of the data analytics process, this work is also said to take the most time.

4. Analyze and Visualize

For the prepared data, use aggregation, statistical methods, graphing, and so on to draw out insights. Putting it into graphs and tables makes trends and outliers easier to see and makes "what is happening" easier to understand. Visualization is also effective when conveying findings to others.

5. Interpret and Connect to Action

Derive "so what should we do" from the analytics results and connect it to decision-making and measures. This is the goal of data analytics. Furthermore, it is important to verify the results of measures with data again and turn the cycle of connecting to the next round of analytics.

Representative Methods Used in Data Analytics

In actual analytics, various methods are used according to the purpose. Here are a few representative ones.

• Cross-tabulation: Aggregate multiple items in combination to see trends by attribute. The most basic and easy-to-use method.
• Regression analysis: Examine how much a certain value (e.g., sales) is influenced by other factors (e.g., ad spend). Also used for prediction.
• Cluster analysis: Divide data with similar characteristics into groups. Used for segmentation such as classifying customers by type.
• Association analysis: Find relationships among events that occur simultaneously, such as "things bought together." Applied to recommendations and the like.
• Time-series analysis: Analyze the transition of data along time to grasp trends and seasonality. Used for demand forecasting and the like.

There is no need to use advanced methods from the start; it is realistic to begin by grasping the current state with a basic method such as cross-tabulation.

Tools Used in Data Analytics

The tools used in data analytics are chosen according to the purpose, data volume, and skill. Here we organize the representative ones.

• Spreadsheet software (Excel, Google Sheets): The most familiar; widely usable for aggregation, graph creation, and basic analysis.
• BI tools (Looker Studio, Tableau, etc.): Connect multiple data sources into dashboards to make visualization and regular reporting efficient.
• Access analytics tools (GA4, etc.): Analyze website user behavior and inflow paths.
• Programming languages (Python, R): Used for advanced and flexible analytics such as processing large volumes of data and statistics/machine learning.

Keep in mind that tools are merely means, and they are chosen with "which question you want to solve" coming first.

Use Cases of Data Analytics

Data analytics is used in a wide variety of operations and industries. Here are some representative examples.

Optimizing Marketing

Analyze the effect of each ad and measure and judge which channel to allocate budget to in order to maximize results. Visualizing with data how multiple touchpoints contributed to sales and effectively allocating a limited budget—the optimization of the marketing mix—is a representative use case of data analytics.

Demand Forecasting and Inventory Management

By analyzing past sales data and seasonality to forecast demand and maintain appropriate inventory, you prevent stockouts and excess inventory. It is widely used in retail and manufacturing settings.

Preventing Customer Churn (Cancellation)

By analyzing customers' usage and predicting "customers likely to churn" to follow up in advance, you prevent churn. It is a domain especially emphasized in subscription-type services.

Improving Sites and Services

Through website access analytics, grasp where users are dropping off and which paths lead to conversion, and use this for improvement. A/B testing, which sets up hypotheses and verifies them, is also a kind of data analytics.

Precautions When Proceeding with Data Analytics

To connect data analytics to results, there are several points to watch out for. First, do not confuse "correlation" and "causation." That two pieces of data are related (correlation) and that one is the cause and the other the result (causation) are different. Judging only by apparent correlation risks reaching mistaken conclusions.

It is also important not to fall into "analytics for analytics' sake." In organizations, it often happens that a fine dashboard is built but no one looks at it. Data analytics produces value only when it connects to "decision-making and action." Do not make analytics an end in itself; always be conscious of "so what should we do."

Furthermore, attention to data quality is necessary. If the original data is inaccurate or biased, no matter how advanced the analytics, the conclusion will be mistaken (expressed as "garbage in, garbage out"). It is important to carry out preprocessing carefully and ensure the reliability of the data.

Summary

Data analytics is the series of tasks of organizing and processing collected data to draw out insights and putting them to use in decision-making. Organizing it into the four levels of descriptive, diagnostic, predictive, and prescriptive helps you see what you yourself want to clarify.

When proceeding, rather than suddenly touching a tool, it is important to be conscious of the steps of "clarifying the purpose and question, collecting, preprocessing, analyzing and visualizing, interpreting and acting." Start with basic methods such as cross-tabulation and choose tools and methods according to the purpose. And while watching out for points such as not confusing correlation and causation and not making analytics an end in itself, turning data into "grounds for action" is the key to connecting data analytics to value.