Data Analytics vs Data Analysis: 5 Key Differences

Data intelligence systems are an indispensable asset in modern business. Organizations of all sizes, from small enterprises to large multinational corporations, rely on artificial intelligence-based, data-driven decisions to improve their operations. However, data science jargon can be confusing. 

Data analytics and data analysis are two terms that frequently stump business owners. While they look and sound similar, the difference between them is meaningful. Data analytics and data analysis are distinct parts of the data intelligence process, and each has its own methods and objectives.

In this article, we'll examine the terms in depth.

What is data analytics?

Data analytics is a broad field in data science in which experts collect data from sources to be standardized, analyzed, and interpreted. Experts use this data to draw actionable conclusions. It represents the entire process organizations undertake, drawing from insights to optimize their decision-making.

The standard data analytics process works like this:

1. Decision-makers and data scientists identify key problems or areas of improvement within a business's process. For example, data scientists might examine low customer traffic on important holidays like Thanksgiving and Christmas or investigate an increase in negative customer reviews online.
2. The team decides what data to collect for the study. In our example, the team would recommend collecting online and offline customer traffic data from the past few years and data from online review sites or feedback forms.
3. Data is collected from several sources. Compile data from point of sale (POS) systems, web traffic metrics, online checkout systems, and in-store CCTV cameras. Data should also be collected from review sites such as Google Business and Yelp, as well as physical feedback forms submitted by customers.
4. This dataset contains different types of data from digital and analog sources. It all needs to be converted into one standard format and checked for wrong or missing values. This process is called data standardization and often involves building data pipelines for efficient data management.
5. Custom algorithms. Once standardized, specialists pass this data through to pinpoint important values, such as the most common complaints in negative reviews, the products bought most often, and out-of-stock products requested most often near the holidays.
6. Draw conclusions. Based on these results, data scientists offer actionable insights. For example, they may recommend stocking up on scented candles and potpourri before the holidays if those products are likely to run out at that time of year.

Apart from solving specific problems, teams also use data analytics to monitor important things like team efficiency, net profits, and customer traffic. They can use these insights to inform business initiatives such as marketing campaigns or dynamic pricing strategies. The results are often presented in interactive dashboards for stakeholders to interpret easily.

Example use case

For instance, if your marketing and operations teams want to understand why customer engagement has dropped during seasonal promotions, you'll want to use data analytics to:

• Identify patterns in customer traffic. Compare in-store and online activity during past promotions to spot dips or spikes in customer engagement.
  
• Analyze customer sentiment. Process reviews from social media to track recurring complaints or praise related to seasonal campaigns.
  
• Measure promotion effectiveness. Assess conversion rates from email campaigns, loyalty apps, and website banners to determine which channels are underperforming.
  
• Correlate sales with inventory. Link product availability data to sales metrics to see if stockouts are causing missed revenue opportunities during high-demand periods.
  
• Segment customer behavior. Treat frequent shoppers differently from first-time buyers to understand how each group responds to holiday promotions.

Through data analytics, a marketing team can determine whether issues stem from ineffective marketing, negative customer sentiment, or insufficient product availability and implement corrective measures. 

What is data analysis?

Data analysis is a subset of the broad discipline of data analytics. It's concerned with the actual treatment of collected data. Data analysis consists of all the processes required to clean and visualize data to draw conclusions from it. It often involves using statistical analysis and data analysis techniques and frameworks to extract meaningful insights from large datasets.

Why is data analysis necessary? Raw data, as collected from business systems, may have little value. How data scientists treat the data determines how valuable it will be for the business. Without some treatment, the information in raw data will likely have confusing signals.

Example use case

Say your teams collect data from your POS systems, CCTV cameras, and retail websites. You'll want to use data analysis to:

• Transform textual data. Convert textual data from your POS system into a format your computer can process.
• Process audiovisual data. The data from your CCTV cameras is purely audiovisual and must also be processed appropriately.
• Standardize data formats. Data then needs to be processed and converted into one standard format.
• Clean and model the data. Once the data is standardized, clean and model it correctly to prepare it for analysis.
• Handle data by source. For example, treat data gathered through a storefront differently from data gathered through your website, recognizing that these sources reflect distinct customer experiences.

Without the treatment and transformation process, your analysis could present heavily skewed or incorrect results. Depending on the size and responsibilities of the data analysis team, data analysis may also include interpreting data and suggesting actionable measures to other teams and stakeholders.

Tip: Data modeling helps you rid your data of any biases to facilitate a better, more accurate analysis.

Types of data analysis methods

Data analysis methods can further be classified into four categories based on the objective of the analysis. They are:

• Descriptive analysis. This step, also known as data mining, is the most common method of data analysis, where large datasets are captured and analyzed for any patterns that can give deeper insight into business processes. This kind of analysis lets specialists find answers to key statistical questions. They might want to know how much revenue the business generates, how many customers visit the business on average, and how much profit it is making.
• Diagnostic or inferential analysis. As the name suggests, diagnostic inferential analysis determines the root cause of current problems. It involves using data to determine how and why a business process failed or excelled. This method often requires advanced problem-solving skills and the use of multiple000 methodologies.
• Predictive analysis. Specialists can use historical data to estimate what will likely happen based on past consumer trends. This analysis often involves machine learning algorithms and regression analysis.
• Prescriptive analysis. This type of analysis helps specialists gain a statistical perspective on an important business decision. Is it the right time to launch a new product? Prescriptive analytics will help answer that question. Can you afford to scale up right now? This type of analysis will help you find out.

All these methods often involve statistical modeling to interpret the data accurately and draw meaningful conclusions.

5 key differences between data analytics and data analysis

Data analysis and data analytics pursue different objectives. Each takes a different amount of time to carry out, and they're used in different circumstances.

While data analytics is a more expansive process consisting of data collection, validation, and visualization, data analysis is a subset of it. It is limited to the actual handling and treatment of the data. 

In the following sections, we explain key differences between the two processes.

1. Data analysis is a subset of data analytics

For all practical purposes, data analysis is a subset of data analytics. As we said above, data analytics is a broad discipline that includes several processes, such as data capturing (data mining), data analysis, insight generation, and communicating recommendations to the concerned business teams.

Data analytics can be seen as one large business intelligence mechanism in which raw data is turned into helpful recommendations. Data analysis is a small part of this mechanism and is concerned solely with cleaning and analyzing data.

Unlike data analytics, data analysis works within limited boundaries of operation where a specific type of input is needed to generate a specific result (in a standard format). Data analysis can be seen as an integral part of the larger machine, which is data analytics.

2. Data analysis looks at past data, while data analytics creates predictive models

Data analysis and data analytics have different objectives and processes. Where data analytics is more open-ended and predictive, data analysis is more quantified and operates within set boundaries.

Much of data analytics attempts to answer the questions: What will happen next, and how can the business use this knowledge to its benefit? Specialists gather historical data and study it to find patterns that could generate valuable business insight for potential future outcomes. Analytics teams use their discretion and business acumen to decide what data can be helpful for the organization and how it should be treated. Finding the answers to these questions helps build predictive analytics models.

Unlike data analytics teams, data analysis teams are given pre-captured and sometimes even lightly processed data to work with. They may also be given information about the organization's operational problems and objectives. 

Analysts use this data to answer specific questions. Data analysis, it could be said, is a diagnostic practice in which data is used to find and fix functional faults that are currently occurring or have already occurred.

3. They are used differently in business

Businesses use data analysis and data analytics in separate contexts.

Data analytics leverages all available data sources and uses them to help the business learn from and adapt to consumer demand. It's used to formulate constructive ways to optimize a company's operational processes to serve customers better and generate more revenue.

On the other hand, data analysis sheds more light on the questions the data analytics team posed. It's essentially used to find solutions to problems identified by analytics teams while operating within the data confines set by those teams.  

4. Different tools are needed for each

Given the difference in the scopes and processes involved in data analytics and data analysis, separate sets of business tools are used to conduct them. Data analytics uses a more comprehensive set of business intelligence tools to help capture, filter, transform, process, interpret, and communicate useful information.

All-round business analytics tools like the Python programming language, Power BI, and Tableau are especially essential to data analytics processes.

Using these tools to produce actionable insights can prove valuable in your career path. You can start by enrolling for certification courses or discovering the programs through how-to guides.

Data analysis tools, on the other hand, are tailored to filter, transform, and process data. These tools are programmed to perform specific analytical operations on large business datasets (also called big data).

Some tools commonly used for data analysis are:

• OpenRefine
• KNIME
• RapidMiner
• NodeXL

5. The process for data analytics is longer

Data analytics is a long process with several steps. A standard data analytics life cycle includes:

1. Data identification. Define the problem you want to solve and determine which data sources are needed.
2. Data acquisition and filtering. Collect raw data from relevant sources, then filter out irrelevant or duplicate information.
3. Data extraction. Pull structured and unstructured data into a usable format for processing.
4. Data validation and cleaning. Ensure accuracy by removing errors, inconsistencies, or missing values.
5. Data aggregation and representation. Combine data from multiple sources and organize it in a logical format.
6. Data analysis. Apply statistical, mathematical, or machine learning techniques to uncover patterns and insights.
7. Data visualization. Present findings through charts, graphs, and dashboards to make complex data easier to interpret.
8. Creating data stories. Translate results into meaningful narratives that help decision-makers understand the implications and take action.

During these steps, business problems are identified and framed as concrete questions, the answers to which are found by capturing and processing gigabytes (sometimes even petabytes) of data.

Data analysis, on the other hand, is a relatively short process compared to data analytics and has three key steps:

1. Data cleaning
2. Data visualization
3. Creating data stories using insights

Data cleaning is an important step in the process. As mentioned, real-world data in its raw form is of limited value to businesses. Raw data is prone to internal biases, discrepancies, and other problems that can hinder effective analysis. Specialists use data cleaning techniques to ensure that all captured data is standardized and ready for further processing.

Industry-Specific Applications

This table shows how data analytics and data analysis operate within the same industry contexts. This side-by-side comparison helps clarify the distinction between the two processes.

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Which one do you need: Data analytics or data analysis?

Both data analytics and data analysis are essential for modern businesses. The two processes shouldn't be seen as competing alternatives but as different forms of the same data intelligence process.

Data analytics is a good option for businesses looking to generate predictive models and needing a more holistic data intelligence system that can help them streamline their operations.

Data analysis, on the other hand, is better suited for businesses that have well-defined problems and want to diagnose them and find solutions using business intelligence systems.

Find professional data analysts on Upwork

Whether you need data cleaned, processed, and standardized or are looking for holistic solutions to broad challenges, Upwork can connect you with the leading data analysts and data scientists. With the right help, you'll be able to make better-informed decisions that will drive growth.

And if you're skilled in either of these areas, find data analysis and data analytics jobs on Upwork. 

FAQs

Interested in data analysis and analytics? In this section, we answer some of the most common questions to help you better understand the broader landscape of data-driven decision-making.

Is business intelligence the same as data analytics?

Not quite. Business intelligence (BI) focuses on collecting, organizing, and reporting data to help businesses make informed decisions. Data analytics goes further by applying advanced methods like statistical modeling, predictive analytics, and machine learning. BI tells you what happened, while data analytics helps you understand why it happened and what to do next.

Do small businesses really need data analytics?

Yes. Even small businesses generate useful data, such as sales, website visits, or customer feedback. With affordable tools, small businesses can use data analytics to improve customer retention, reduce costs, and make strategic decisions without needing enterprise-level resources.

What's the role of machine learning in data analytics? 

Machine learning (ML) takes data analytics a step further by introducing trained algorithms for analyzing data to identify unique patterns. ML can automatically generate predictions such as forecasting sales, detecting fraud, or personalizing product recommendations with little human intervention.

Can you do data analysis without coding?

Yes. Many tools, like Tableau, Power BI, and Google Data Studio, allow users to analyze and visualize data without writing code. However, coding skills in Python, R, or SQL open up more advanced analysis and automation options.

5. What's the difference between data analysis, data analytics, statistics, applied mathematics, and data science?

These fields overlap, but each has a different focus:

• Data analysis is the process of cleaning, organizing, and examining data to find patterns, trends, or insights. It's usually descriptive and focuses on understanding what happened.
  
• Data analytics is broader and often involves predictive or prescriptive techniques. It uses tools, algorithms, and sometimes machine learning to answer why it happened or what might happen next.
  
• Statistics provides the mathematical foundation for both analysis and analytics. It focuses on probability, sampling, and testing hypotheses to make reliable inferences from data.
  
• Applied mathematics is the wider discipline that uses mathematical methods to solve real-world problems, including those in physics, finance, engineering, and, increasingly, data science.
  
• Data science combines all of the above (data analysis, analytics, statistics, and applied mathematics) with programming, domain expertise, and machine learning to build models, automate insights, and generate actionable solutions from large datasets.

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Upwork is not affiliated with and does not sponsor or endorse any of the tools or services discussed in this article. These tools and services are provided only as potential options, and each reader and company should take the time needed to adequately analyze and determine the tools or services that would best fit their specific needs and situation.