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 2025. Such large amounts of data can be incredibly useful; however, you need a way to extract actionable insights from it. This is where data discovery comes in. By combining data from various sources and analyzing it, companies can improve their decision-making and their business strategy.
The Data Discovery Process
Several steps are commonly taken as part of 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
Sometimes overlooked, defining your goals should be the first step in the data discovery process. Your objective is what determines the data you need. Once you know what you’re trying to achieve, you’ll have a better idea of what data you should collect, how to prepare it, how to analyze it, and how to gain valuable insights from it.
2. Data Collection
After you’ve defined your objective, you need to identify the sources of data you want to use and collect it. There are many different methods to do this. For instance, most organizations already possess a lot of useful data, often referred to as first-party data. This data can be stored in databases, data lakes, data warehouses, or something similar. With internal data, sourcing the data is straightforward, and generally speaking, first-party data is trustworthy.
However, internal data often isn’t enough to generate useful data insights. You usually need to collect data from various external sources as well. One option is to use APIs, which many companies and organizations provide to share their data. Some well-known examples are the Google API, Instagram API, Zillow API, Reddit API, and YouTube API. While some APIs are free, many require payment. Before exploring other methods of data collection, it’s a good idea to check if the source offers an API as it can greatly simplify your process.
However, most web data isn’t available via an API. If that’s the case, you can still gather data using web scraping, which allows you to get data from a web page and store it in a format that’s more convenient for data analysis, such as CSV.
You can perform web scraping yourself by writing custom scripts that extract the data you need. However, that requires web scraping skills and can be time-consuming. You also have to deal with antiscraping mechanisms employed by websites. An alternative is to use already-made instant scrapers, such as the Bright Data Web Scraper API. Tools like this are fairly straightforward, don’t require any coding skills, and can be highly successful in dealing with antiscraping mechanisms.
If you’re looking for an even easier solution, you can try to find ready-made datasets that are available for purchase. Such datasets are carefully collected from reliable sources, analyzed, cleaned, and structured in a user-friendly way. For instance, Bright Data offers over a hundred ready-to-go datasets from some of the most popular data sources, such as Amazon, Instagram, X (Twitter), LinkedIn, and Walmart. It also allows you to generate a custom dataset using an automated platform.
In general, you often use a combination of these data sources or even some that aren’t mentioned (such as real-time data, public datasets, or surveys). That’s because no single source of data typically contains all the data you need.
3. Data Preparation
Once you have your data, the next step is to prepare it for analysis. Usually, data gathered from various sources doesn’t come in the exact format you need. It’s up to you to unify the format, parse the data, handle missing values, remove duplicate data, deal with outliers, handle categorical data, standardize or normalize the data, and resolve any other issue you identify.
Raw data generally comes with certain flaws, such as missing data. If that’s the case, you can choose to simply discard the instances where some data is missing. However, a more common method is to impute the missing values (especially in cases when you don’t have a lot of data).
There are various missing value imputation methods available, such as median imputation, mean imputation, or more sophisticated methods, such as the Multivariate Imputation by Chained Equations (MICE). Another potential issue with numeric data is variables with different ranges. In that case, it might be beneficial to normalize (scale the data to a range between 0 and 1) or standardize (scale the data to a mean of 0 and a standard deviation of 1) the data. The choice between the two depends on the statistical technique you are using during the data analysis step as well as on the distribution of your data.
Low-quality data can lead to low-quality results and insights. The goal of this step is to ingest the raw data and output clean, high-quality data, ready to be analyzed.
4. Data Visualization
Once the data is cleaned, you can create various charts that can help you explore the data. Data visualization is helpful as it can sometimes be easier to see insights from visualized data, as opposed to data in tables. There are countless chart types, all of them being able to showcase different aspects of the data. Some popular ones are the bar chart (good for comparing values), line chart (good for showing a trend over a certain period), pie chart (good for showing the structure of a category), box plot (good for summarizing data and identifying outliers), histogram (good for inspecting the data distribution), and heat maps (good for analyzing correlations).
Many tools can help you with the data visualization techniques mentioned earlier. Some popular ones are Power BI and Tableau. These tools are user-friendly, ideal for creating dashboards and reports, and great for collaboration and sharing.
If you need highly customized visualizations, you may want to turn to Python libraries, like Matplotlib or seaborn. These libraries require coding skills and have a much steeper learning curve compared to Power BI and Tableau. However, they allow you to use specific types of visualizations and allow for extensive customization:
In essence, the visualization of data helps you better understand the data you’re working with, including the hidden patterns in it, the relationships between the variables, and the anomalies in the data.
5. Data Analysis
Data analysis is closely related to data visualization. In fact, these two steps are often done at the same time in a comprehensive process referred to as exploratory data analysis.
Data analysis allows you to further explore the data, create descriptive and summary statistics, and summarize all that into comprehensive reports. Similarly to data visualization, the goal of this step is to identify trends, patterns, relationships, and anomalies.
There are many techniques for extracting insights from the data. Statistical analysis is a popular one that generally analyzes data through descriptive statistics (good for summarizing data characteristics) and inferential statistics (good for making predictions based on a sample). Machine learning (ML) is also popular 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 interactions with the environment). You can perform all these using Python libraries, such as pandas, NumPy, and scikit-learn.
6. Interpretation and Action
After data analysis, it’s time to summarize all the identified patterns and interpret them. Based on the data analysis and the data visualization steps, there should be valuable insights extracted from the data. These insights should be actionable and lead to better decision-making. You can reach those insights by identifying the patterns relevant to your business goals, understanding why they’re happening, prioritizing them, and continuing to monitor how the patterns evolve.
At this point, you can look back at the defined objectives and whether they are fulfilled. If they’re not, you can iterate back to any of the previous steps and try to improve them. This may mean getting more data, preparing it differently, or analyzing the data further and looking for additional insights.
Data Discovery Methods
The process of data discovery can be either manual or automated. Both methods come with their own pros and cons.
Manual Data Discovery
As the name suggests, manual data discovery implies that a human being performs the data discovery process. This means that a human collects the data, unifies the formats, prepares it for further analysis, and visualizes and analyzes the data. For this to be successful, the person performing manual data discovery should be familiar with data analysis tools and techniques, various statistical methods, and data visualization tools; should have some technical skills, such as coding; and should have domain knowledge in the field they’re working in.
With manual data discovery, a human has the ability to extract some valuable insights from the data that a machine may miss, such as some relationships between the variables, certain trends, or reasons for anomalies. If there’s an anomaly in the data, a human is able to research the reasons behind it, while a machine may usually only report on it. However, performing the data discovery process manually requires a complex set of skills and is much slower than automated data discovery.
Automated Data Discovery
With the huge advancements in artificial intelligence (AI) and ML, the process of data discovery can, in large part, be automated. In the case of automated data discovery, AI software performs many of the steps discussed earlier.
AI tools, such as DataRobot, Alteryx, and Altair RapidMiner, can prepare data automatically, including unifying the formats, handling missing values, as well as detecting anomalies and outliers. Such tools are also faster than manual data discovery, and they don’t require nearly as much expertise.
Keep in mind that AI tools can be complex, expensive, highly dependent on quality data, and often require maintenance; additionally, the results from AI tools can be more difficult to interpret. All these factors should be taken into account when choosing between automated and manual data discovery.
Data Classification
A related concept to data discovery is that of data classification. With the help of data classification, data can be categorized using predefined criteria and rules. Some of the common ways to categorize data based on these criteria are to divide them based on the data type (structured, unstructured, semistructured), the sensitivity level (public, internal, confidential), the way data is used (operational, historical, analytical), and the source of data (external and internal). This can help companies track the large amounts of data they collect.
There are various techniques that can be used for data classification. Simpler methods are to use rule-based classification, where data can be classified based on certain keywords or patterns. A more sophisticated method would be to use some of the popular ML algorithms, such as 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 companies that handle data. However, as the amount of data in an organization grows, it gets more difficult to achieve security and compliance.
Data discovery can help with this as it’s able to spot security risks and compliance gaps. Through data discovery, organizations can help identify sensitive data in unsecured locations, detect anomalies, or detect data stored for longer than necessary. Some tools can help with data security, such as Varonis, Collibra, and BigID.
In the previous section, it was mentioned that data classification can help with compliance. This can be achieved by training AI classification models to flag security risks and noncompliant data. The AI models can be supervised models, such as neural networks and gradient boosting machines, but also unsupervised, such as anomaly detection. By integrating into existing security frameworks, AI can enhance threat detection, response capabilities, and security posture. AI can also help analyze large amounts of data and identify patterns a human may miss; it can predict potential vulnerabilities, as well as detect unusual behavior.
Tools for Data Discovery
There are plenty of tools available to help with data discovery. Such tools even enable 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 process of gathering data, mainly through automating web scraping.
For instance, the Bright Data Web Scraper API allows you to scrape popular websites. It is easy to use, it is highly scalable, and it comes with all the features expected from an instant web scraper. If you’d rather get a prebuilt dataset, you can choose one of the over one hundred datasets Bright Data has available.
The source of data you choose depends on the availability of data as well as on your needs and preferences. If you can find a prebuilt dataset that contains the data you need, it’s faster to get that dataset compared to trying to gather the data yourself. If datasets are not available, you can see if the data is available through an API as that is generally faster compared to scraping the data. However, if there’s no API, you probably have to scrape the data yourself, either doing it manually or using an automated web scraper.
Conclusion
In this article, you learned what the importance of data discovery is and how to go about the process of data discovery. You also learned about a few data discovery methods and some of the tools you can use for data discovery.
Bright Data has several solutions when it comes to data discovery, such as proxy services, the web scraper API, and datasets. These tools can significantly help you in the data collection step of the data discovery process. Try out Bright Data for free today!
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