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If you’ve ever wondered how to turn scattered data into strategic insights, you’re in the right place. Welcome back to our series on mastering the Data Analytics Process.
Today, we’re diving into Stage 2 of our 4-Stage Data Analytics Roadmap - Data Collection, where the real action starts.
In this article, we’ll guide you through the essentials of collecting the right data, deciding where to store it, and effectively managing it to set your business up for impactful analysis. Get ready to take the first actionable steps toward making your data work for you.
This article is Part 2 in our series “The Data Analysis Process.” If you’re following along, check out the previous article in the series:
Continue your journey with the next parts:
💡 Struggling with data collection? Watch #2: Data Collection – What, Where, and How to ensure you’re gathering the right data and storing it effectively. Streamline your process and lay the foundation for impactful data-driven decisions!
The Data Analytics Roadmap is a structured process that helps businesses navigate their data analytics journey with precision and clarity.
It ensures that every piece of data collected serves a purpose, contributes to actionable insights, and supports business analytics by enabling better decisions and problem-solving.
The Data Analytics Roadmap consists of four major stages:
Data Collection is not just about collecting any data, but gathering the most relevant and reliable information needed to answer your business questions.
This stage focuses on understanding what kind of data you need, where to source it, and best practices for collecting it in a way that keeps it trustworthy and useful for analysis.
Data Collection is all about gathering the right pieces of information and ensuring they are complete, trusted, and ready to fit perfectly into your analytics puzzle.
Developing a data collection plan is essential to guide the process, ensure consistency, and achieve effective results.
This stage involves identifying the types of data required, where to store it, and understanding the best practices for gathering it effectively.
Data collection involves gathering information from multiple areas of your business. Selecting appropriate data collection types and understanding the different types of data collection methods is essential for aligning with your research objectives. Including both qualitative and quantitative data ensures a complete view of your business landscape. But what exactly should you collect? Start with these three essential types.
User behavior data is generally collected through platforms like Google Analytics. It tells the story of how users interact with your website – whether it's page views, session durations, or bounce rates. Online tracking tools like cookies and pixels help collect behavioral data across websites, offering deeper insights into user interactions and preferences.
Even if your business goes beyond marketing, understanding user behavior is vital for any company with an online presence, as it helps you optimize user experience and improve engagement.
External data comes from sources that are outside your direct control but still influence your business significantly. Examples include advertising data from tools like Facebook Ads and Google Ads, which show how much you spend, impressions, clicks, and conversions.
External data is important for understanding how external forces, like marketing campaigns, are driving traffic and engagement.
Internal data is all about the information from within your company, like CRM or ERP systems, POS systems, finance data, and product details. These are your exact sales figures, client details, supplier information, and everything related to the internal workings of your business.
This type of data forms the backbone of your operations. Without accurate internal data, any analysis built on top of it is on shaky ground.
Choosing the right data collection method is essential for gathering information that truly answers your research questions and supports your business objectives. Data collection methods refer to the various processes used to collect data from different sources, and they can be broadly categorized into primary and secondary data collection.
Primary data collection involves collecting data directly from the source. This means gathering new, original data tailored to your specific needs. Common primary data collection methods include surveys, interviews, focus groups, and observations. For example, you might use online surveys to collect quantitative data from a large group of customers, or conduct in-depth interviews and focus groups to gather qualitative data that provides richer insights into customer motivations and experiences.
Secondary data collection, on the other hand, involves collecting data from existing sources. This could include analyzing research articles, government reports, internal documents, or online databases. Secondary data is often used to supplement primary data, providing context or background information that helps inform your analysis.
The choice between these data collection methods depends on your research question, the type of data required (quantitative or qualitative), and the resources available. Quantitative data collection methods, such as structured surveys and experiments, are ideal for collecting numerical data that can be analyzed statistically. Qualitative data collection methods, like open-ended interviews and focus groups, are best for exploring attitudes, opinions, and behaviors in depth.
No matter which data collection method you choose, ensuring accurate data collection is important. High-quality, reliable data is the foundation for meaningful analysis and actionable insights.
The tools you use to gather data have a direct impact on its quality, consistency, and usefulness. Choosing the right data collection tools ensures smoother workflows, fewer errors, and better analytics outcomes.
Let’s put these concepts into practice by looking at an example of an e-commerce store. Imagine you’re running an online shop, and you need to gather the right data to drive growth and improve customer experience.
Here are the types of data you need to focus on collecting:
Gathering complete data across all categories helps you generate reports that drive informed decisions and reflect insights from every part of your business.
Once you’ve collected your data, the next question is where to store it to work with it efficiently. Choosing the right data storage solution can impact how effectively you can analyze your data. It’s important to store data in a way that allows you to integrate all the data collected from various sources, ensuring maximum value and enabling advanced analytics.
Let’s explore some of the most common data storage options and how they align with your business needs:
Spreadsheets are best suited for very specific ad-hoc tasks. They are widely used for various data tasks, but are generally not ideal for long-term data storage due to their limitations. For example, even personal finance and taxation can sometimes be managed more efficiently in a data warehouse, which offers more robust capabilities.
💡Ready to unlock the true potential of your data? Dive into our article on Data Transformation Basics! Discover essential techniques for cleaning, structuring, and enriching your data to drive smarter decisions. Transform your data into actionable insights and elevate your analytics game today!
Pros: Databases like MySQL or PostgreSQL offer more robust storage solutions compared to spreadsheets. They can handle larger volumes of data and are better suited for row-oriented tables like transactions.
Cons: Traditional databases may not be ideal for real-time analytics, especially with massive datasets. They're more suited for storing data rather than conducting in-depth, real-time analysis.
Data Warehouses, such as Google BigQuery or Amazon Redshift, are designed to handle and analyze large volumes of data efficiently. Unlike spreadsheets or traditional databases, Data Warehouses provide both storage and speed, allowing businesses to make informed decisions quickly. Centralized data warehouses serve as a single source of truth, making it easier to perform analytics on collected data.
They streamline the integration of data from multiple sources and support statistical analysis, enabling organizations to uncover trends and generate valuable business insights.
This is especially important for user behavior data from platforms like Google Analytics 4 (GA4), where data volume can grow rapidly.
For example, SEMrush leverages a data warehouse to analyze website data in real-time, providing valuable insights within minutes. This agility is something traditional methods cannot match.
Moreover, modern cloud-based data warehouses are accessible and easy for data analysts to manage, even without deep technical expertise. They are also cost-effective, providing significant value through actionable, real-time insights.
💡 Curious about Google BigQuery? Our latest article covers everything you need to know to make the most of this powerful data warehouse! Learn how BigQuery can help you analyze vast datasets quickly and efficiently, empowering your data-driven decisions like never before.
In fact, there is almost a free option from Google to store Google Analytics data in their warehouse, BigQuery. We have several articles on this topic that explain how to set up GA4 BigQuery Export.
It's a smart move for any business, whether you're ready to analyze the data immediately or not. I highly recommend checking out our detailed guide to learn how BigQuery makes analyzing user behavior data at scale easier and more powerful.
Data collection is not just about getting data – it’s about ensuring it’s accurate, well-structured, and ready to support meaningful analysis. To make your data collection process smoother and more efficient, consider the following tips.
When collecting internal data, focus on the most critical data points first. Overcomplicating your data structure from the outset, collecting too much data, can lead to overwhelm and confusion. Begin with core elements like IDs and two to three essential details for each entity.
For example, if it's an order, capture the Order ID, sales amount, and order date. You can always gather more information as needed, but keeping it simple initially will make the process smoother.
By simplifying your data structure early on, you enable faster implementation with fewer errors. Missing deadlines can make business users unhappy and unsure about the reports.
To avoid this, start with less data, focus on one thing at a time, and ensure reports are delivered on schedule. This approach builds trust and leads to insights that genuinely support the business.
Depth Over Breadth. When dealing with external data sources like advertising platforms, prioritize collecting data at the most granular level possible. This means capturing detailed information, such as specific ad creative performance, keyword data, and user engagement metrics.
The more granular your data, the more precise and actionable your insights will be. Granular data collection also allows you to slice and dice the data in various ways, making your analysis flexible and making it easier to identify trends.
This depth of data empowers your business to make more data-driven, informed decisions, improving the overall effectiveness of your strategies.
Here is the rule about tools: efficiency over complexity. While custom-built connectors can be powerful, they require ongoing maintenance to keep up with API changes and updates. Instead, focus on using third-party tools that require no support from your team.
Even though you might have to pay some dollars for each of those connectors, this approach reduces the technical burden on your team.
When you load data from multiple external sources, the structure will be different. This is why integrating external data into a Data Warehouse (DWH) from the very beginning allows for seamless cross-data-source analysis in the future.
Combining data from various external sources into a single DWH creates a unified dataset that is easier to analyze, compare, and report on.
How do you do this? That's what we'll talk about specifically in the next article – Stage #3 of our Roadmap: Data Preparation.
To avoid confusion and ensure smooth analysis, keep your data well-organized. Use consistent naming conventions, clear data hierarchies, and standardized formats to make the data easy to navigate and understand.
Maintaining an easily manageable data structure can extract actionable insights faster, directly impacting your personal speed and performance.
A well-structured dataset not only improves efficiency but also reduces the likelihood of errors, ensuring that your analysis is reliable and the outcomes are meaningful. The more organized your data is from the start, the easier it will be to scale your analysis efforts as your business grows.
For user behavior data, consider implementing server-side tracking. This approach allows you to truly own the data, rather than relying solely on an analytics tool. Outdated client-side tracking is often prone to inaccuracies due to ad blockers, cookie restrictions, and privacy concerns.
Moving to server-side tracking provides greater control over the data collection process, significantly reducing the likelihood of data loss and improving the overall quality of your analytics.
Server-side tracking not only enhances data accuracy but also ensures compliance with privacy regulations such as GDPR, as it mitigates risks associated with client-side tracking issues.
💡 Learn how to monitor user behavior without relying on pixels in our latest article, Server-Side Tracking: Monitor User Behavior without Pixels. Discover alternative tracking methods that offer valuable insights while respecting user privacy.
Collecting data isn’t just about volume; it’s about quality, accuracy, and security. Below are some of the most common challenges in data collection and actionable ways to overcome them effectively.
⚠️ Issue: Poor data quality often results from using inadequate data collection methods, insufficient training, ora lack of proper validation. This can compromise the reliability of your analysis and lead to misguided decisions.
✅ Solution: Select robust data collection techniques, pilot your surveys or guides before rollout, and ensure data collectors follow standardized procedures to support accurate data collection.
⚠️ Issue: Missing data occurs when responses are incomplete or information is lost during the data collection process, affecting dataset reliability.
✅ Solution: Apply statistical methods such as imputation or weighting to fill in gaps and maintain the integrity of your dataset for meaningful analysis.
⚠️ Issue: Inaccurate data can stem from manual entry errors, flawed collection tools, or collection bias, weakening the validity of your insights.
✅ Solution: Use data validation techniques like data cleaning and transformation to detect and correct errors early, ensuring consistent and reliable data.
⚠️ Issue: Maintaining data integrity is difficult without secure systems, especially when integrating data from various sources.
✅ Solution: Store data in encrypted databases or cloud-based platforms with strict access controls to ensure integrity and security throughout the data collection process.
⚠️ Issue: Large and complex datasets can overwhelm traditional systems, slowing down processing and increasing the chance of errors.
✅ Solution: Use specialized data analytics platforms or high-performance computing resources to manage and analyze data efficiently at scale.
⚠️ Issue: Ensuring data security, especially for sensitive customer data, is critical and often overlooked during collection and storage.
✅ Solution: Implement firewalls, intrusion detection systems, and conduct regular audits to protect sensitive data throughout the data collection lifecycle.
Data collection is more than just gathering information; it’s about setting the stage for a powerful narrative your business can rely on.
By simplifying internal data structures, collecting external data at a granular level, and reducing technical complexity, you ensure your data is meaningful and actionable. Effective data collection also lays the foundation for advanced analytics, enabling you to transform collected customer data into actionable insights and drive business outcomes through sophisticated analysis.
In the next part of our series, we will dive into Stage 3: Data Preparation. This stage will ensure that your data is ready for analysis by integrating and transforming it into a usable format.
By continuously analyzing data, companies can predict future outcomes and make better decisions, leveraging insights to drive business growth and innovation. Stay tuned to learn how to make your data ready to deliver valuable business insights.
