Learn how data discovery helps businesses make informed decisions through insights from collected and analyzed data.
Data discovery is the process of gathering data from various sources, preparing and analyzing it, and extracting valuable insights. The ultimate goal of data discovery is to understand the data on a deeper level and use it to make better decisions. Insights extracted from the data discovery process can help companies with fraud detection, business planning, churn prediction, risk assessment, lead generation, and more.
In this article, you’ll learn what data discovery is, why it’s important, and what the most common steps of the data discovery process are.
What Is Data Discovery and Why Is It Important
According to estimates, the amount of data generated every day will reach 181 zettabytes in 2026. Such massive volumes of data can be incredibly valuable; however, organizations need effective methods to extract actionable insights from it. This is where data discovery becomes essential. By combining information from various sources and analyzing it comprehensively, companies can enhance their decision-making processes and refine their business strategies.
The Data Discovery Process
Several key steps are typically involved in the data discovery process, including defining your objective, data collection, data preparation, data visualization, data analysis, and interpretation and action:
It’s important to note that data discovery is a highly iterative process; you may jump from any step of the process to a previous one if you find that it improves the end result.
1. Define Your Objective
Often overlooked, establishing clear goals should be the first step in the data discovery process. Your objective determines what data you need to collect. Once you understand what you’re trying to achieve, you’ll have better clarity on what data to gather, how to prepare it, how to analyze it, and how to derive valuable insights from it.
2. Data Collection
After defining your objective, you need to identify data sources and collect the information. Multiple methods exist for this purpose. Most organizations already possess substantial amounts of useful data, commonly referred to as first-party data. This information can be stored in databases, data lakes, data warehouses, or similar systems. With internal data, sourcing is straightforward, and first-party data is generally reliable.
However, internal data alone often isn’t sufficient to generate meaningful insights. You typically need to collect information from various external sources as well. One approach is using APIs, which many companies and organizations provide to share their data. Popular examples include the Google API, Instagram API, Zillow API, Reddit API, and YouTube API. While some APIs are free, many require payment. Before exploring other collection methods, check if the source offers an API, as it can greatly simplify your process.
Most web data isn’t available through APIs. In such cases, you can still gather information using web scraping, which allows you to extract data from web pages and store it in formats more suitable for analysis, such as CSV.
You can perform web scraping yourself by writing custom scripts that extract the needed data. However, this requires specialized skills and can be time-consuming. You also need to handle anti-scraping mechanisms employed by websites. An alternative is using pre-built instant scrapers, such as specialized web scraper APIs. These tools are straightforward, don’t require coding skills, and can effectively handle anti-scraping mechanisms.
For an even simpler solution, you can search for ready-made datasets available for purchase. Such datasets are carefully collected from reliable sources, analyzed, cleaned, and structured in user-friendly formats. Many providers offer hundreds of ready-to-use datasets from popular data sources like Amazon, Instagram, X (Twitter), LinkedIn, and Walmart. Some platforms also allow you to generate custom datasets using automated systems.
Generally, you’ll use a combination of these data sources or even additional ones not mentioned (such as real-time data, public datasets, or surveys). This is because no single source typically contains all the data you need.
3. Data Preparation
Once you have your data, the next step is preparing it for analysis. Usually, data gathered from various sources doesn’t come in the exact format you need. You must unify formats, parse the data, handle missing values, remove duplicates, deal with outliers, process categorical data, standardize or normalize the data, and resolve any other issues you identify.
Raw data typically comes with certain flaws, such as missing information. You can choose to simply discard instances where some data is missing. However, a more common approach is to impute the missing values, especially when you don’t have abundant data.
Various missing value imputation methods are available, including median imputation, mean imputation, or more sophisticated techniques like Multivariate Imputation by Chained Equations (MICE). Another potential issue with numeric data involves variables with different ranges. In such cases, it might be beneficial to normalize (scale data to a range between 0 and 1) or standardize (scale data to a mean of 0 and standard deviation of 1) the information. The choice between these methods depends on the statistical techniques used during analysis and your data’s distribution.
Low-quality data leads to poor results and insights. This step aims to process raw data and output clean, high-quality information ready for analysis.
4. Data Visualization
Once the data is cleaned, you can create various charts that help explore the information. Data visualization is valuable because insights are sometimes easier to identify in visual formats compared to tables. Countless chart types exist, each showcasing different data aspects. Popular options include bar charts (good for comparing values), line charts (good for showing trends over time), pie charts (good for displaying category structures), box plots (good for summarizing data and identifying outliers), histograms (good for inspecting data distribution), and heat maps (good for analyzing correlations).
Many tools can assist with these visualization techniques. Popular options include Power BI and Tableau. These tools are user-friendly, ideal for creating dashboards and reports, and excellent for collaboration and sharing.
If you need highly customized visualizations, consider Python libraries like Matplotlib or seaborn. These libraries require coding skills and have steeper learning curves compared to Power BI and Tableau. However, they allow specific visualization types and extensive customization.
Essentially, data visualization helps you better understand your working data, including hidden patterns, variable relationships, and data anomalies.
5. Data Analysis
Data analysis is closely related to data visualization. These two steps are often performed simultaneously in a comprehensive process called exploratory data analysis.
Data analysis allows further data exploration, creation of descriptive and summary statistics, and compilation of comprehensive reports. Similar to data visualization, this step aims to identify trends, patterns, relationships, and anomalies.
Many techniques exist for extracting insights from data. Statistical analysis is popular and typically analyzes data through descriptive statistics (good for summarizing data characteristics) and inferential statistics (good for making predictions based on samples). Machine learning (ML) is also widely used and utilizes supervised learning (works with classifications and regressions based on labeled data), unsupervised learning (uses techniques like clustering and dimensionality reduction on unlabeled data), and reinforcement learning (learns through environmental interactions). You can perform these analyses using Python libraries such as pandas, NumPy, and scikit-learn.
6. Interpretation and Action
After data analysis, summarize all identified patterns and interpret them. Based on the analysis and visualization steps, valuable insights should be extracted from the data. These insights should be actionable and lead to improved decision-making. You can achieve this by identifying patterns relevant to your business goals, understanding why they occur, prioritizing them, and continuously monitoring how patterns evolve.
At this point, review your defined objectives and whether they’ve been fulfilled. If not, you can return to previous steps and improve them. This may involve gathering more data, preparing it differently, or conducting further analysis to find additional insights.
Data Discovery Methods
The data discovery process can be either manual or automated. Both approaches have distinct advantages and disadvantages.
Manual Data Discovery
As the name suggests, manual data discovery involves human execution of the entire process. This means a person collects data, unifies formats, prepares it for analysis, and visualizes and analyzes the information. For success, the person conducting manual data discovery should be familiar with data analysis tools and techniques, various statistical methods, and data visualization tools; should possess technical skills like coding; and should have domain expertise in their working field.
With manual data discovery, humans can extract valuable insights that machines might miss, such as variable relationships, certain trends, or reasons for anomalies. When anomalies appear in data, humans can research underlying causes, while machines typically only report them. However, manual data discovery requires complex skill sets and is much slower than automated approaches.
Automated Data Discovery
With significant advances in artificial intelligence (AI) and ML, much of the data discovery process can be automated. In automated data discovery, AI software performs many of the previously discussed steps.
AI tools like DataRobot, Alteryx, and Altair RapidMiner can automatically prepare data, including unifying formats, handling missing values, and detecting anomalies and outliers. Such tools are faster than manual discovery and require significantly less expertise.
Keep in mind that AI tools can be complex, expensive, highly dependent on quality data, and often require maintenance; additionally, AI tool results can be more difficult to interpret. Consider all these factors when choosing between automated and manual data discovery.
Data Classification
A concept related to data discovery is data classification. Through data classification, information can be categorized using predefined criteria and rules. Common ways to categorize data include dividing them by data type (structured, unstructured, semi-structured), sensitivity level (public, internal, confidential), usage method (operational, historical, analytical), and data source (external and internal). This helps companies track the large amounts of information they collect.
Various techniques can be used for data classification. Simpler methods involve rule-based classification, where data is classified based on specific keywords or patterns. More sophisticated approaches use popular ML algorithms like neural networks, decision trees, or linear models.
Security and Compliance
Security and compliance with regulations like the General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), or Health Insurance Portability and Accountability Act (HIPAA) are critical for data-handling companies. However, as organizational data volumes grow, achieving security and compliance becomes more challenging.
Data discovery can assist by identifying security risks and compliance gaps. Through data discovery, organizations can help locate sensitive data in unsecured locations, detect anomalies, or find data stored longer than necessary. Some tools can help with data security, such as Varonis, Collibra, and BigID.
As mentioned previously, data classification can aid compliance. This is achieved by training AI classification models to flag security risks and non-compliant data. AI models can include supervised models like neural networks and gradient boosting machines, but also unsupervised approaches like anomaly detection. By integrating into existing security frameworks, AI can enhance threat detection, response capabilities, and security posture. AI can also analyze large data volumes and identify patterns humans might miss; it can predict potential vulnerabilities and detect unusual behavior.
Tools for Data Discovery
Numerous tools are available to assist with data discovery. Such tools enable even individuals without coding experience to perform the data discovery process. These tools can help with automated data preparation, analysis, or automated visualization. However, data discovery tools can also significantly improve the data gathering process, mainly through automated web scraping.
For instance, specialized web scraper APIs allow you to scrape popular websites. They are easy to use, highly scalable, and come with all features expected from instant web scrapers. If you prefer pre-built datasets, you can choose from hundreds of available datasets from various providers.
Your chosen data source depends on data availability as well as your needs and preferences. If you can find a pre-built dataset containing the needed data, obtaining that dataset is faster than gathering the information yourself. If datasets aren’t available, check if the data is accessible through an API, as this is generally faster than scraping. However, if no API exists, you’ll probably need to scrape the data yourself, either manually or using an automated web scraper.
Conclusion
In this article, you learned about the importance of data discovery and how to approach the data discovery process. You also learned about various data discovery methods and some tools you can use for data discovery.
Webparsers has several solutions for data discovery, such as proxy services, web scraper APIs, and datasets. These tools can significantly help you in the data collection step of the data discovery process. Try out Webparsers for free today!