Data Analysis vs Data Analytics: Key differences and use cases

Have you ever wondered how many opportunities are hidden in your data? When such important numbers just sit idle, there is a risk of making a completely wrong decision. Data analytics and data analysis come to the rescue here. Although, which tech choices to choose from for your business needs? 

P.S.: The answer is hidden in this article!

What is Data Analysis?

Data analysis is a systematic process of examining information to draw valuable insights and support decision-making (Don’t confuse it with data analytics – the difference between data analytics vs data analysis will be discussed later). 

Unlike predictive analytics or machine learning, data analysis focuses on understanding what happened in the past, identifying patterns, and laying the groundwork for future strategic decisions.

Companies that use data analysis services are more likely to emerge from crises faster and more effectively, because numbers are always honest.

A data analyst in the workspace

Common techniques in data analysis

Data analysis isn’t limited to one tool. It’s a set of approaches, each of which solves a specific problem. Let’s take a closer look at them and examine what’s better for your business (or at least try to):

Descriptive statistics

Descriptive statistics serve as the starting point for any serious data analysis. It provides a quantitative description of the main characteristics of the dataset, allowing the analyst to quickly understand the structure and features of the available information:

• The basis of descriptive statistics is measures of central tendency. 
• The arithmetic mean shows the typical value in a data set, but it can be distorted by outliers. 
• The median, which represents the middle value in a sorted list, is more resistant to extreme values. 
• Frequency indicates the most common value, which is especially important for categorical data.
• The skewness and excess coefficients describe the distribution shape. 
• A positive skew means a “tail” of the distribution that stretches to the right, which is typical for population income or company size. Excess shows the “sharpness” of the peak of the distribution compared to the normal.

Inferential statistics 

Inferential statistics allow for informed conclusions about the population based on the study of the sample. This is critically important in situations where it is impossible or economically impractical to study the entire set.

For example, the company conducts a satisfaction survey among 500 clients from a database of 50,000 people. The average score was 7.2 out of 10 with a confidence interval of [6.9, 7.5]. It allows us to assert with a high degree of certainty that the overall satisfaction level of all customers is within the specified range, which provides a basis for strategic decisions.

Hypothesis testing 

Hypothesis testing is a formalized procedure for decision-making under uncertainty. The process begins with formulating a null hypothesis (usually asserting the absence of an effect) and an alternative hypothesis (asserting the presence of an effect).

Practical example of A/B testing: A website tests two versions of a landing page. Version A showed a conversion rate of 3.2% on a sample of 10,000 visitors, while Version B showed a conversion rate of 3.8% on a similar sample. The two-sample z-test showed a p-value of 0.02, which, at a significance level of 0.05, allows us to reject the null hypothesis of no difference and accept version B as more effective.

Errors of the first kind (false positives) occur when we reject a true null hypothesis. Errors of the second kind (false negatives) occur when we accept a false null hypothesis. The test power (1 – the probability of the second type error) shows the test’s ability to detect real effects.

Data cleaning & preparation 

Handling missing values requires understanding the mechanism of their occurrence. Data may be missing completely at random (MCAR), missing at random (MAR), or missing not at random (MNAR). The strategies include removing incomplete records (listwise deletion), replacing with mean or median values, interpolation for time series, and more complex methods such as multiple imputation.

Practical example: A customer database has 15% missing values in the “income” field. The analysis shows that young clients (MAR) are more likely to miss appointments. A simple replacement with the average would distort the distribution, so a regression attribution based on age, education, and profession is used.

Trend analysis

Trend analysis examines long-term directions of changes in indicators over time, highlighting stable patterns from short-term fluctuations.

Time series decomposition separates observed values into components: trend (long-term direction), seasonality (regular cycles), cyclicality (irregular long-term fluctuations), and a random component. The additive model involves summing the components, while the multiplicative model involves multiplying them.

Let’s imagine: A retail chain analyzes sales over 5 years. Decomposition shows an upward trend (5% per year), pronounced seasonality (peak in November-December, decline in January-February), and a cyclical component related to economic crises. This allows them to plan inventory, staffing, and marketing activities more precisely.

Data visualization 

Effective visualization turns complex numerical arrays into intuitive graphical representations, making it easier to detect patterns and communicate results.

For categorical data, bar charts compare values between groups: 

• The horizontal version is convenient for long category names. 
• Grouped bar charts show multiple data series at once. Pie charts show parts of a whole, but are only effective for a small number of categories.

Analyzing sales by region uses a choropleth map, where the color intensity encodes the sales volume. An additional bar chart shows the top 10 cities, and a time-series graph displays seasonal fluctuations. This combination of visualizations provides a comprehensive understanding of geographical and temporal patterns.

Typical use cases

Amazon can be a great example here. Thanks to data analysis, Amazon understands which products to recommend to you tonight, which promotions to launch on the weekend, and how to plan logistics so that the package arrives in one day. All based on the behavior of millions of customers.

Banks have their own tasks, and Revolut is also focusing on analytics. The company tracks how users spend money, what data analytics services they are interested in, and how often they travel. This data is used to offer the client the exact card, cashback, or investment opportunity that they’ll find beneficial.

Therefore, as you have already understood, data analysis works everywhere: from a small store to an international corporation. 

What is Data Analytics?

Data analytics vs data analysis: that is the question, almost like in a Shakespeare play! Unlike the legendary author, though, we do have an answer for you! 

Data analytics is a way to make any business meaningful, flexible, and sustainable. If data analysis is the process of inspecting, cleaning, transforming, and data modeling to extract meaning and insight, then data analytics is a broader discipline that includes these steps and aims to use data to inform business decisions.

In other words, data analysis is an important part of data analytics. But analytics is the entire machine: from data extraction and cleaning to forecasts and recommendations.

Common data analytics techniques

Descriptive analytics

Answers the question: What happened?

Descriptive analytics captures the moment and shows an accurate picture of what is happening. The system counts all indicators for the required period, groups data by categories (regions, goods, customers), and creates reports and dashboards with key metrics. Its main purpose is to identify trends and patterns in historical data without attempting to explain them.

For example, you find out that sales dropped by 20% in a quarter, 60% of customers came through social media, and peak sales hours are between 6:00 PM and 9:00 PM. These facts become the foundation for all further conclusions.

Diagnostic analytics

Answers the question: Why did this happen?

Diagnostic analytics digs deeper, looks for cause-and-effect relationships, and explains why the numbers are the way they are.

This discipline analyzes correlations between various factors, compares indicators in different segments and periods, conducts detailed data analysis, and tests hypotheses, discarding false assumptions.

Imagine: you know that sales have dropped, but diagnostic analytics shows that the reason is a 2-week delay in deliveries. Or conversion rates decreased after the website design was changed. It may turn out that customers from other cities buy less due to the high cost of delivery, but sales of a specific product increased due to a mention by a popular blogger.

Predictive analytics 

Answers the question: What will happen next?

Predictive analytics uses machine learning development and statistical models for forecasting.

The system builds predictive analysis models based on historical data, predicts customer behavior and market trends, calculates the probabilities of various scenarios, and helps plan resources with budgets.

With this tool, you can learn that 15% of customers will cancel their subscriptions next month, demand for winter jackets will increase by 40% in October, and a new customer is 80% likely to make a repeat purchase.

Prescriptive analytics

Answers the question: what to do about it?

Recommendation analytics sits at the top of the “analytical pyramid”, generating specific recommendations for businesses. The system doesn’t just predict what will happen; it suggests optimal solutions, calculates different scenarios and their consequences, optimizes processes and resource allocation, and automates routine decision making.

The result is ready-made strategies, optimized campaigns, and automatic triggers that work without your involvement.

Data analytics tools

• Python (Pandas, NumPy, Matplotlib libraries) is the number one language for analysts, especially when it comes to data cleaning, charting, and modeling.
• R – another language popular among statisticians and data analysts, especially in the scientific community.
• Big Data platforms: Google BigQuery, Snowflake, and Apache Spark are needed if data volumes exceed the capabilities of local analysis.
• Machine Learning tools: scikit-learn, XGBoost, H2O.ai, and DataRobot allow you to build predictive models without having to write everything from scratch.

Real-world examples

Data analytics is actively used in a wide range of industries, from banking to healthcare.

For instance, Revolut uses data analytics to assess credit risk, build scoring models, and prevent fraud. Algorithms instantly block suspicious transactions by comparing them to typical customer behavior patterns.

Networks like Walmart or Procter & Gamble forecast demand, optimize store shelves, and plan advertising campaigns based on data. Data analytics allows you to accurately understand when and where to launch promotions to minimize write-offs and increase sales.

Clinics and insurance companies use analytics to predict the likelihood of diseases, analyze the effectiveness of treatment, and optimize doctor schedules. For example, the Mayo Clinic uses predictive analytics to assess patient risks at an early stage and take preventive measures.

What is the difference between Data Analysis and Data Analytics

Data Analysis and Data Analytics: Which one do you need

When companies come to our consulting team with the question “What do we need: analysis or analytics?”,  in turn, we always ask: “Do you want to understand what happened, or what to do next?”

If a business is in the start-up or stabilization phase, data analysis is often enough. It includes reports, dashboards, and basic visualizations: everything to understand what’s happening quickly. With this, a company’s marketing team, for example, can easily determine which channels brought the highest traffic, which landing pages were the most effective, and at which stage there was a drop in leads.

But if a business is growing, working with scale, or is in a highly competitive niche, data analytics is a must. Why? Because seeing the present picture is important, but predicting what’s coming in the future comes to the fore. For example, you can predict in advance which customers will leave, which products will be successful in the next quarter, or which pricing strategy to choose in new regions. This requires more advanced models, a broader variety of investigated scenarios, and a complex analysis of customer behavior about business metrics.

When Data Analysis and Data Analytics work together

In reality, successful projects are not about data analysis vs analytics. The secret to success lies in effectively combining the two. When it comes to analysis and analytics, without the other, one only reveals a part of the picture.

Imagine a company that sells electronics. Data analysis will show that TV sales fell in July. This is important, but not enough. By adding data analytics, we will understand that the drop is due to the launch of a competitive model in the same price segment, and that without a promotional campaign by September, it will be even worse. Moreover, analytics will suggest actions: targeting specific segments, reviewing inventory, and adjusting prices. So there’s no room for data analysis or data analytics, only a combination.

The same thing in HR: analysis will reveal that there is a high turnover in the development department. Analytics will suggest that the outflow is not related to salary, but to the lack of career tracks for middle-level developers, and will help predict how many employees will leave in the next six months if nothing changes. 

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Future trends in Data Analysis and Data Analytics

1. First, tools are getting smarter. If before you had to write complex code for models, now many platforms offer AutoML, which automatically generates models tailored to the task. This lowers the barrier to entry, especially for SMBs.
2. Second, there is a growing trend towards real-time analytics. More and more businesses want to make decisions not based on quarterly results, but here and now. It requires data streaming, updated models, and high-performance visualization systems.
3. The third trend is the integration of GenAI into analytics. Imagine being able to “talk” to data in a natural language, asking a question not with an SQL query, but simply asking: “Why did our demand in Poland drop in July?” And get a visual response with hypotheses. 

Hope we’ll see these trends soon and won’t ask ourselves which one is the best: data analytics vs analysis?

Finally, what to choose?

Today, businesses that don’t work with data simply don’t “survive” a tough market competition. Although it’s not the fact of collecting data that’s important, but the ability to understand, interpret, and use it to take action. This is where the power of combining data analysis and data analytics comes in.

The difference between data analysis and data analytics is simple: analysis provides insight into what’s happening in the moment. Analytics is a tool for managing what will happen next. And when they’re utilized in tandem, a company gains a real competitive advantage. 

So, data analysis vs data analytics? Bet you have already decided, but if not, Data Science UA is your number one assistant!

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