What is Data Analytics?

Everyone is talking about data analytics, but what exactly is data analytics? What are the key pieces? Whether you're just curious to learn more or thinking about a career in data analytics, getting a grip on the basics is a great first step.

Today, companies are swimming in data, they generate so much data every single day, but raw numbers alone don’t tell much of a story.

That’s where data analytics comes in, helping to turn those numbers into insights that lead to smarter business decisions and more promising revenue outcomes.

If you’re wondering how all of this fits into the bigger picture, you’re in the right place. It breaks down what data analytics is and what a data analyst does on a day-to-day basis.

This article will cover the six key points that will give you a deeper understanding of what it means to be a data analyst and how this role helps businesses grow faster and more predictably.

You’ve probably heard much about data analytics and might wonder what it involves. Companies constantly collect vast amounts of data - from customer interactions, operational metrics, tools like Google Analytics, sales CRMs, Facebook ads, spreadsheets, and more.

However, in its raw form, this data is just a collection of numbers and text, which provides little value. This is where data analytics comes into play.

Data analytics is the process of preparing, analyzing, and visualizing raw data to extract insights that can benefit businesses. These insights are essential for companies to make informed, data-driven decisions, leading to better business strategies and results.

💡 Check out this video about the role of a data analyst, key responsibilities, and the must-have skills to win in the analytics field. Or keep reading this article.

A data analyst's role can be compared to piecing together a puzzle. A data analyst’s primary role is to collect scattered pieces of raw data from various sources and bring them together to form a complete picture.

This role involves more than just interpreting numbers - it’s about uncovering patterns and insights that can inform business decisions.

So, what does a data analyst specifically do?

They explore complex data, interpret it, and transform it into something truly valuable. After analyzing the data, they uncover patterns that reveal important and relevant information about business operations, sales, marketing, and products. These insights help companies make informed decisions based on evidence rather than just a manager’s intuition.

For instance, a data analyst might find out how users interact with a website page or how Facebook ads contribute to sales.

With such data clearly analyzed, companies can make better decisions about their web design, content strategy, advertising efforts, and competitive positioning.

Armed with these insights, businesses can craft strategies that aren’t based on guesswork but are backed by solid evidence, resulting in better outcomes and improved overall performance.

Now that we’ve covered what data analytics is and why it’s important, let’s explore the role of a data analyst and their responsibilities in more detail.

A data analyst’s day involves a variety of tasks centered around extracting, processing, and analyzing data to uncover insights that help businesses make better decisions.

Below are some of the common responsibilities that data analysts take on.

First and foremost, it’s all about data collection. Data analysts gather information from various sources such as databases, CRM systems, online platforms, spreadsheets, and even CSV files.

While you may have heard that this is typically the responsibility of a data engineer, the truth is that many data analysts perform this task daily. It involves using data extraction tools, writing SQL queries, or manually pulling data from different systems.

Next comes data preparation. After extracting the data, it’s often raw and messy, containing errors, duplicates, or inconsistencies. Data analysts first clean the data and normalize it to ensure it’s accurate and reliable, making it ready for analysis.

This process might involve removing duplicates, filling in missing values, and standardizing formats. Once this is done, they may also write queries to automate the process, ensuring the data is prepared on a schedule.

Third, we have data analysis. In this stage, a data analyst examines the data to uncover trends, patterns, and insights. This may involve running regressions, performing cluster analysis, or using other advanced analytical techniques. Through this process, they identify key findings that can directly impact business decisions.

However, if the data is simple, there’s no need for complex methods—just using Google Sheets or Excel to build pivot tables and charts can do the job effectively.

Next up is data visualization. To effectively communicate findings to stakeholders, data analysts create visual representations such as charts, graphs, and dashboards. These visuals help stakeholders quickly understand complex information without getting lost in numbers. Tools like Tableau, Looker Studio, or Power BI are often used for building dashboards that don’t require much manipulation from the user.

Alternatively, spreadsheets like Google Sheets or Excel are also popular. Business users generally prefer Google Sheets because it’s easy to use, has plenty of add-ons and extensions, and keeps data fresh and updated. Excel, on the other hand, is often favored by more technical users or those in companies that rely heavily on Microsoft tools.

Check out our video on building effective pivot tables and charts to make data analysis easy and insightful!

And finally, we have the presentation. It could be also guided or just a delivered report. After gathering and analyzing data, analysts compile their insights into reports and presentations. These presentations highlight key findings, include detailed notes, and provide recommendations based on the data.

The format can vary—sometimes it’s as simple as delivering a text-based report via email, or it might involve using those pivot tables and charts, making them visually appealing and easy to understand.

Conditional formatting can be applied to highlight crucial insights, and the final report can be shared as a link to a spreadsheet or an interactive dashboard. This allows stakeholders to easily access and review the findings.

💡 Learn how to master Pivot Tables and Charts in Google Sheets to quickly organize, summarize, and visualize your data. This article guides you through creating dynamic reports and insightful charts, enhancing your data analysis skills in just a few steps!

Now that we've covered the broader overview, let's get into the specifics of what a data analyst does on a day-to-day basis. This isn’t just theory, but real tasks that you can expect to perform if you're considering a career in data analysis.

Based on common job descriptions, here are some specific tasks a data analyst typically handles:

• Collect and report on user feedback, using data visualization tools to highlight trends and insights.
• Collaborate with various departments to develop requirements for data projects and define key success metrics.
• Plan, manage, and execute data analysis projects, from initial concept to final evaluation.
• Continuously monitor business systems, identifying areas for improvement and suggesting data-driven enhancements.
• Translate business questions into actionable tasks that can be addressed with data.
• Establish and manage data processes, gathering new data to answer pressing business questions.
• Aggregate data from multiple sources to create comprehensive datasets for analysis, often through techniques like data blending or SQL-based joins.
• Set and maintain standards for data quality by implementing processes to ensure accuracy and consistency.

These are just a few examples of the specific tasks you may take on as a data analyst. If you’re interested in exploring further, checking job listings on platforms like LinkedIn will give you a good sense of the wide range of responsibilities involved in this role.

Next, let’s explore how the work of a data analyst directly contributes to a company’s success. Data analysts play an irreplaceable role by providing insights and recommendations that help businesses thrive.

Here's how their tasks make a meaningful impact on business success.

Data analysts provide insights based on hard data, helping businesses make decisions grounded in evidence rather than intuition. This approach leads to more accurate and effective business strategies. Relying on data-driven insights ensures that decisions are well-informed and more likely to lead to successful outcomes.

Data analysts play a key role in identifying inefficiencies and areas for improvement within business processes. By addressing these issues, companies can streamline operations, reduce costs, and boost overall productivity. This not only enhances day-to-day operations but also helps the business scale more effectively over time.

Although it’s a more niche area, refining marketing strategies is crucial for every business. By analyzing customer data and marketing campaign performance, data analysts help businesses fine-tune their strategies. This leads to better targeting, higher customer engagement, and increased return on investment—key factors that positively impact a company’s bottom line.

Data analysts play a vital role in identifying potential risk patterns within a business. By uncovering these risks through data analysis, they enable companies to take preventive measures, protecting assets and minimizing potential losses. This proactive approach helps businesses stay ahead of challenges and safeguard their operations.

Another niche area is product development. By analyzing user feedback and behavior, data analysts provide insights that guide the creation and improvement of products, whether it’s software, clothing, food, or even services.

This ensures products meet customer needs and expectations, making data analytics a crucial part of a company’s ability to use data as a strategic advantage.

In summary, the daily tasks and responsibilities of data professionals not only improve operational efficiency but also drive overall business success. This gives you a glimpse of how data analytics can be applied in real-world scenarios.

Now that we’ve explored the role of data analysts, their specific tasks, and how they contribute to business success, let’s dive into the actual process of data analysis. Understanding this process is crucial for seeing how data analysts approach new projects and extract valuable insights from raw data.

Here are the five main steps a data analyst typically follows when starting a new data project.

The first step in data analysis is to define clear goals and objectives, translating them into specific questions that need answers. It’s essential to understand why the analysis is being conducted and what insights are required.

This involves identifying a clearly defined problem - sometimes through interviews with business users—and then forming a hypothesis or research question to address it.

For example, if the marketing team notices a campaign underperforming, your key question might be: "What factors contributed to the underperformance? Was it the platform, ad placement, type of ad, or an issue with the landing page not converting?" Alternatively, you might ask, "Which marketing channels delivered the best ROI over the last 30 days?"

By clearly stating the problem, defining the goals, and translating them into targeted questions, you set a focused direction for the analysis, ensuring your efforts are aligned with real business needs and challenges.

With your key questions defined, the next step is to start collecting the necessary data. To effectively answer your questions, it’s important to identify the types of data you need and, crucially, where that data will come from.

For example, in the case of analyzing marketing performance, you would need:

• Ad performance metrics from platforms like Google Ads, Facebook Ads, or LinkedIn Ads.
• Website analytics data from tools like Google Analytics 4.
• CRM data to track real conversions.

Data analysts typically gather data from primary sources, such as first-party data that the company already has. Additionally, they may use secondary or external sources, like open data from government portals, tools such as Google Trends, or reports from international organizations.

If you're looking to benchmark your campaign’s performance against industry standards, you might refer to reports from marketing research firms or public data from industry organizations.

By gathering relevant data from these sources, you ensure a comprehensive dataset that will allow you to analyze and uncover meaningful insights to answer your initial questions.

Once you've collected the data, the next step is preparing it for analysis. The data in its raw form may contain errors, duplicates, anomalies, or missing values, so this stage, often called data cleaning or scrubbing, is crucial for ensuring accuracy and consistency.

You might need to remove duplicates, handle missing data, from your original data and normalize data to standardize it across different sources. For example, if you're working with data from Google Analytics in BigQuery, you may need to UNNEST event parameters to make it usable for analysis. This should all be addressed before moving forward with any analysis.

This step should be completed before moving on to the actual analysis. You may need to:

• Remove Duplicates: Ensure that there are no repeated entries that could skew your results.
• Handle Missing Data: Fill in missing values or decide how to handle incomplete data.
• Normalize Data: Standardize formats, such as dates or units, across different datasets.

If you find that you’ll be preparing the same data regularly, it’s a good idea to automate data preparation. You can use scripts or create queries that run on a schedule, ensuring that your data is consistently cleaned and ready for analysis without manual intervention each time.

In our marketing performance example, this might include:

• Standardize data formats across various datasets, as different ad platforms often use varying column names and formats.
• Remove duplicate entries of ad interactions, ensuring everything is accurate and consistent.
• Fill in missing conversion data from your CRM to complete the dataset.
• Map the ad campaign data with online sales and CRM sales data to create a comprehensive view of the results.

With OWOX BI, for example, you can automate the collection and preparation of data from advertising platforms, tracking tools like Google Analytics, and your CRM system.

OWOX BI also offers data preparation templates specifically designed for marketing analytics, allowing you to skip the manual steps and get all the reports you need in one place.

Now that your data is clean and ready, it's time to dive into the actual analysis. The approach you choose will depend on the specific questions you're looking to answer and the type of data you're working with.

Before starting the analysis, it’s crucial to have the right environment for working with the data. Spreadsheets are invaluable for this step—whether you prefer Google Sheets, Excel, or another tool, these are essential for conducting the analysis.

For a marketing performance example, here are some common techniques you might use:

Descriptive analysis involves summarizing the key features of your data, such as average ad spend, total conversions, or click-through rates. This process typically includes building a pivot table, adjusting columns, rows, and filters, and manipulating the data until you uncover useful insights.

It's all about breaking down the data to get a clearer picture of its overall patterns and trends.

💡 Discover the power of Pivot Tables in Google Sheets! This article teaches you how to effortlessly summarize and analyze large datasets, turning raw data into actionable insights with just a few clicks. Perfect for streamlining your data workflows!

Cluster analysis is a technique used to group similar data points together, such as audiences across different platforms. This helps identify various customer segments or patterns in ad performance.

In our example, cluster analysis could be used to determine which customer segments responded most effectively to different types of ads, offering insights into targeting and campaign optimization.

Time series analysis focuses on examining data points collected or recorded at specific time intervals, making it ideal for understanding trends over time. For example, you can analyze the performance of ads across different days, weeks, or months of the year.

These techniques help you uncover patterns and insights, providing answers to your key questions and revealing the factors driving your marketing campaign’s performance.

The final step in the data analysis process is interpreting and sharing your results. This is where your data is transformed into visual language, making it easier for stakeholders to understand the insights. Depending on the type of data, you can present your findings in various formats.

If stakeholders only need to see visuals, tools like Looker Studio, Tableau, or Power BI are ideal. If you think they’d prefer to interact with the data, Google Sheets is a great option.

For example, in a marketing analysis, you might create a dashboard that includes:

• A bar chart comparing conversion rates across different ad platforms.
• A line graph showing trends in ad performance over time.
• A pie chart illustrating the distribution of ad spend across channels.

At this stage, it’s crucial to clearly articulate what the data analysis reveals in relation to the initial questions or business challenges. Afterward, collaborate with stakeholders to discuss the findings and determine the next steps. Whether the report is ready to be automated or requires further tweaks, this is the moment to ensure everything is aligned.

It’s also important to reflect on any limitations within the data and pinpoint areas that might need further exploration. For instance, you might need more detailed customer demographic data or a deeper dive into a specific marketing channel.

By interpreting and sharing your results effectively, you ensure that the analysis leads to actionable insights, driving better business decisions and strategies.

Now that we’ve rounded off the data analysis process, let’s dive into the essential skills you’ll need to succeed as a data analyst. It’s not just about knowing the tools and techniques—having the right mindset is equally important.

In this section, we’ll cover both the hard and soft skills that are crucial for excelling in this field.

Let’s start with the hard skills. These are the technical abilities you’ll need to work effectively as a data analyst. From managing databases to using analysis tools, these skills are essential.

First up is SQL, and it’s absolutely crucial. While Python is also widely used, SQL is essential for managing and querying databases. It allows data analysts to extract, manipulate, and organize data efficiently. It may seem daunting at first, but with practice, anyone can become proficient in it.

💡 Watch the video below to start mastering SQL - it’s not as hard as you think!

Second, spreadsheets are an essential tool for every data analyst. Regardless of the size of the business, spreadsheets like Google Sheets or Excel are invaluable for organizing, analyzing, and visualizing data.

Whether you're handling simple datasets or complex reports, spreadsheets provide the flexibility and functionality needed for effective data analysis.

Next are data visualization tools, which are crucial for presenting data clearly and understandably. Tools like Google Sheets, Tableau, Looker Studio, and Power BI are commonly used to create visual representations of data.

They help analysts share insights through charts, graphs, and dashboards, making complex data more accessible to stakeholders.

Last but not least is mathematics. Data analysts work extensively with numbers, making strong mathematical skills crucial. They often need to perform calculations, interpret quantitative data, and sometimes apply mathematical models to analyze trends or patterns.

A solid understanding of math is essential for making sense of the data.

Soft skills are equally important for a data analyst. Beyond technical abilities, skills like communication, problem-solving, and an analytical mindset help data analysts interpret findings and effectively collaborate with stakeholders.

Data analytics isn’t just about crunching numbers; it’s about uncovering the story behind the data. An analytical mindset enables data analysts to dig deeper, ask the right questions, and find insightful answers that drive informed decision-making. This skill helps connect data with real business outcomes.

Data analytics is fundamentally about solving business challenges. A data analyst needs to be resourceful and creative in finding solutions to complex problems. Whether it’s uncovering inefficiencies or identifying growth opportunities, effective problem-solving skills are essential for interpreting data and applying insights that help improve business performance.

Communication skills are essential for any data analyst. After gathering and analyzing data, it’s crucial to present the findings clearly. Effective communication ensures that insights are understood and acted upon by stakeholders. Working closely with different departments means being able to explain complex data in simple terms to non-technical business users is a must.

To stay competitive in the field of analytics, a commitment to continuous learning is essential. Data analysts benefit from taking online courses on platforms like Coursera and Udemy, reading blog posts, attending webinars, and experimenting with new tools. Exploring new methodologies or technologies is also key.

By combining hard and soft skills with continuous learning, a data analyst is well-prepared to thrive in this dynamic and ever-evolving field. This approach ensures they remain effective and adaptable in their role.

And there you have it! Here, we’ve covered:

• What data analytics is,
• The role of a data analyst,
• How data analytics is applied, and
• The essential skills needed to succeed in this field.

We’ve also walked through the entire process of data analysis—from defining your questions to sharing your results.

If you’ve found this content helpful and want to take your skills even further, be sure to check out the resources linked below.

Thanks for reading! Remember, every data point and report is a step toward smarter, more effective business decisions. So, keep exploring your data, stay curious, and happy analyzing!