The global Big Data and analytics market is worth around $274 billion in 2023, and it’s no surprise that businesses value data-driven insights. But, before you can get any insights from the data, you’ll have to filter and organize it.
That’s where data exploration comes in. Data exploration is the first step in data analytics and it involves grouping and organizing the data to facilitate the analysis process.
Think about it. If you have hundreds of thousands of data variables from multiple data sets, how would you be able to get insights without organizing the data first? With many businesses handling thousands or millions of data variables daily, you can imagine how complex this process is!
In this article, we’ll look at tools that simplify data exploration, helpful tips on how to choose the right data exploration tool, and future trends in data exploration.
Let’s get started!
Key Takeaways
Data exploration forms the base of data discovery. Manually doing this process may affect employee’s productivity and result in data processing errors.
Data exploration tools can help improve employee productivity, increase the accuracy of data analysis, and present the results with clear visuals.
When choosing a data exploration tool, you’ll have to consider the ease of use, integration options, and visualization capabilities, amongst other factors.
Understanding Data Exploration
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Before we look at tools to simplify the data exploration process, let’s first explore what data exploration involves.
According to a recent study, workers waste half of their time preparing data before analysis.
Data exploration comes before data analysis and involves defining data characteristics and identifying the relationships between variables. Think of it as laying the groundwork for data analysis.
You can use data exploration to uncover immediate insights, which you can then use for more detailed analytics. For example, you can use data exploration tools to identify anomalies in data sets. You can then use more detailed analysis to determine why the anomalies occur.
It’s pretty simple – without clearly defining data sets and establishing relationships between variables, it’s hard to get any meaningful insights, especially if you’re dealing with big data sets.
The Role of Data Exploration in Industry
If you’re thinking that data exploration is limited to academics, think again. More than 64% of companies are using data to improve efficiency, and over 94% recognize the importance of data analytics in business growth.
With data exploration tools, you can define data characteristics and do basic analytics in real-time. This allows analysts to make better decisions, leading to an improvement in business performance.
Data exploration is a simple process, but skipping it can affect the quality and scope of your data analytics.
We can go on about how different industries rely on accurate data exploration nowadays, but the crux of the matter is that data exploration tools have almost become compulsory if you want to get your analytics right.
Key Features to Look For
Like with choosing data discovery tools, there are many factors to consider when choosing the right data exploration tool. There’s the technical part, where you’ll consider the tool’s capacity, types of analytical processes, ability to determine data relationships and visualization capabilities.
Then, you have the practical part, where you determine whether the tool can be integrated with your business requirements.
Here are some key factors to consider when choosing data discovery tools:
Data Visualization Capabilities
The first thing to consider when choosing a data exploration tool is its data visualization capabilities. Do you get the data summarized in charts, graphs, heat maps, and other visuals? Can the tool handle multiple data sets and correlate data from multiple sources?
You’ll also have to consider whether the process is partially or fully automated. There are manual tools, but what’s the point in investing in a tool if you have to do everything manually?
User Friendliness
Having a powerful tool is great, but what’s the use if the data visualizations are too complex to understand? In addition to a tool’s capabilities, always check that it presents the data in an easily understandable way.
Integration Options
Like usability, this is a make or break for implementing data exploration tools. Some tools require specific formats for integration, while others will allow for integration across multiple formats. Always check that the tool supports your business’s data collection format for easy integration.
Scalability
As your business grows, so will the amount of data you handle. If you’re already dealing with thousands of data sets, it may be millions in a few months. This is why your data exploration tools should be scalable.
Check how many diverse data sources the tool can handle and whether it offers real-time data exploration.
Top Data Exploration Tools
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As we mentioned before, employees waste 50% of their time organizing data, which can be saved with data exploration tools.
Here are the top 3 tools for data exploration:
Microsoft Power BI
Power BI is one of the more popular data exploration tools. You can use it to explore multiple data sets across different formats. Power BI can integrate data from Excel sheets, cloud-based applications, and even directly from your business’s databases.
Power BI allows you to do both categorical and continuous data analysis where you explore the relationship between continuously changing data sets.
PwC, Chevron, and many popular brands use Power BI for data exploration and analysis. PwC is one of the world’s largest accounting firms, and they have successfully used Power BI to implement dynamic and complex data analysis.
They also use interactive dashboards to share the data analysis with customers in an easy-to-read format. The result? PwC increased its customer retention rate, and employee productivity skyrocketed.
Looker
Looker is a browser-based data exploration tool that helps with dynamic data analysis. It offers seamless integration and can be used along with Excel sheets, Power BI, and other databases. What makes Looker unique is that it can be easily integrated with both on-premises and cloud-hosted databases.
Setting up Looker is difficult, so this is where you can use professional data compliance solutions. You’ll need to integrate the semantic model based on your business profile and operations, implement it in cross-functional teams, and train employees to use the database.
Looker was acquired by Google in 2019 and is used by companies like Square and Typeform that manage millions of data sets daily. Compared to competitors like Tabluea, you don’t need to be a data scientist to use Looker, and it allows you to link data from over 50 diverse databases.
Tableau
Tableau is another popular data exploration tool that provides advanced data exploration and can be on-premise and with cloud-based databases. What’s nice about Tableau is that it provides more visualization options, including charts, graphs, video visuals, and even heat maps.
They provide a free option, but it’s not enough for actual insights. Tableau is also more difficult to use if you don’t know the basics of data exploration. But it’s perfect for data protection officers, data scientists, and data compliance specialists.
It’s also perfect for small business data protection solutions, especially if you don’t have the resources for Looker and other tools.
This doesn’t mean that Tableau isn’t scalable. Some of the largest data-centered companies, like Verizon and Lenovo use Tabluea, and the integrations are pretty accurate. Nissan used the tool to track sales worldwide as well as improve their operations and vehicle delivery, and build an enterprise data culture.
Selecting the Right Tool
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Apart from features, there are several factors to consider when choosing the best data exploration tool. It’s not about how many data sources the tool can handle or the number of visual representations it offers.
What’s the point of getting data visualizations that are too technical to understand?
The key is to choose a tool that’s the right fit for your business. Here are some tips to help you select the right data exploration tool:
Consider the Tool’s Data Volume
Before getting a data exploration tool, you have to make sure it can handle the volume of data your business processes. Even small businesses may process thousands of data entries daily, and not all tools have the capacity.
Sometimes, data volume isn’t the issue. Even Microsoft Excel can handle millions of data entries. The problem is that it can’t integrate entries from multiple databases.
Always check that your data exploration tool can handle both the right volume and number of diverse database integrations.
Understand What Level of User Expertise is Required
If you have a separate department for data analysis with expert data scientists, having a complex tool may work.
But if you’re analyzing the data yourself, look for something that can do complex data mapping, exploration, and analysis while presenting the data in easily understandable visuals.
Look for a Budget Best Fit
Budget may be your first consideration, but try to have some flexibility when deciding how much to spend on a data exploration tool. It helps if you keep the benefits of automating the process in mind.
What price would you put on a tool that can lead to a potential 50% increase in productivity? The answer to this question is your budget.
Challenges and Pitfalls
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Having an automated tool for data exploration helps, but no tool is perfect. Even if you find a tool that fits your data processing requirements and can be integrated with multiple databases, you’ll still run into challenges.
Let’s explore common data exploration challenges and how to overcome them:
Defining Data Exploration Parameters
The biggest challenge in data exploration is defining the goals and parameters of data exploration. You’ll have to choose which databases to include, which data sets to explore and define what you want to achieve from the process.
To overcome this challenge, you’ll need to explore data discovery tools and have a clearly defined data policy.
Dealing With Poor Data Quality
Not all data sets will be complete, and poor data quality can cause problems in the exploration process. Let’s assume you have to run 100 forms through your data exploration tool. If 20% of the forms are incomplete, it may result in bias.
To overcome this bias, you’ll need to clean the data before running it through an analysis tool. Fortunately, there are tools for this, too.
Dealing With Human Error
Data exploration tools are there to help you organize data for more complex analysis. However, there’s always the risk of human error when interpreting the data. While there’s no way to eliminate human error completely, you can reduce the risk by structuring the data or automating analysis with AI tools.
Future Trends in Data Exploration
Considering the pace at which data exploration has advanced, the future of data analysis looks bright. It’s hardly been a couple of decades since data exploration was completely manual. Nowadays, you can fully automate the process and even get real-time data filtration.
The rise in AI and machine learning has paved the way for augmented analysis which allows you to automate the interpretation process as well. Imagine feeding data into the analysis systems and getting optimized business strategies!
The existing tools are also getting more powerful. You can now analyze more than 50 different databases, integrate cloud-based sources, and store more data than before.
However, you’ll still need human input to set up the systems for accuracy. You’ll also need to consider compliance solutions to ensure your data processing systems are in line with the GDPR, CPRA, and other data privacy regulations.
Final Thoughts
Data exploration is a prerequisite for data mapping and data discovery. Having the right tool can help you save time and ensure better data analysis accuracy. However, you’ll need to set up the tool, define the analysis parameters, and ensure the process is in line with data compliance regulations.
At Captain Compliance, our team of data compliance specialists will help you choose the right data exploration tool and ensure that the process is according to compliance regulations.
Check out our customized data compliance solutions.
FAQs
What are Data Exploration Tools?
Data exploration tools are automated tools that help filter out and organize data for further analysis. They help categorize raw data and present the insights in easy-to-understand visuals. This can then be used for data mapping.
Explore the importance of data mapping in compliance services.
What are the Basic Tools Used in Exploratory Data Analysis?
The basic tools used for exploratory data analysis include Matplotlib, excel, Seaborn, Pandas Profiling, and more. These tools are designed to filter out raw data for more in-depth analysis. This can be used in a DPIA and other data audits.
Learn why a DPIA is important for your business.
What are Data Exploration Examples?
Data exploration is used to categorize data according to set variables. An example would be categorizing data according to name, height, demographics, buying preferences, etc. This data can then be used for data discovery.
Learn more about how the data discovery process works.
What is the Best Tool to Explore Data?
There’s no “best” tool for data exploration. However, the most popular tools are Tableau, Power BI, and Looker. When choosing a data exploration tool, always keep your business’s data policies and requirements in mind.