No matter your industry, your business is probably struggling with an all-too common data challenge: You have too much data to make sense of and too little time to analyze all of the critical information at your fingertips, let alone uncover meaningful insights and take data-driven action based on these learnings.
Companies that prioritize data exploration, however, can gain a competitive advantage. Particularly if they leverage automated data exploration, which enables businesses to process data quickly, understand insights, and take action faster.
But you’re probably wondering: What is data exploration, anyway?
As the name suggests, data exploration refers to the act of examining data to translate these various sources of intelligence into actionable insights. This process is the first step of data analysis and involves digging into and analyzing the massive datasets businesses collect today.
By conducting this type of analysis, data analysts are not only able to describe the data, pinpoint patterns, and uncover concrete learnings, they can use their work to inform decision making across every aspect of the organization. They can also make recommendations about meaningful changes that can be implemented, the kind that has the power to positively impact both customer and business outcomes.
In this guide, we’ll go over everything you need to know about data exploration, including what data exploration is, examples of common data exploration techniques and business use cases, and how effective data exploration can help you deliver growth across all of your company’s most important KPIs.
From survey responses and customer service interactions to website traffic trends and transactions, companies of all sizes are constantly acquiring data for analyses. Small and medium-sized businesses are managing terabytes of data and industry titans like Twitter are juggling hundreds of petabytes of data. And often, these data sources are growing much faster than any of these companies knows what to do with them. In fact, as much as 60% to 73% of this big data doesn’t get put to use with data analytics.
Wasting the data they’re collecting is to these companies’ distinct disadvantage. Especially when you consider that brands that use big data effectively are more likely to see profits increase (by as much as 8%) and costs decrease (by as much as 10%), according to Entrepreneur.
Data exploration helps companies dig into the huge volumes of raw data they’re collecting by the minute and use insights gleaned from these data points to power data-driven decision making, whether using visualization tools, manual methods, or machine-learning powered techniques.
Data exploration is important because it can help your company gain critical insights about changes in your data, so you can understand what’s having the biggest impact on your most important metrics and KPIs and implement data-driven decisions that help you influence the health and growth of your business for the better. With effective and timely data exploration, businesses can uncover new ways to drive revenue growth, average order value (AOV), conversions, customer retention, customer lifetime value (CLV), and more.
By getting the right insights to the right people at the right time, your company has the potential to inspire, surprise, delight, and innovate. You’ll be able to make improvements to your products, policies, and internal operations if you are able to know which questions to ask, get the right answers, and act—all in a timely manner.
Data that sits unused and unexplored—or can only be examined with a great amount of time and manual effort—does no one any good. Not your business and certainly not your customers, who, at the end of the day, want their needs to be met. Automated data exploration can help solve these challenges and help your organization save time, money, and resources while delivering insights that help your teams improve your marketing campaigns, sales, and other KPIs.
Companies in ecommerce and retail, tech, media and entertainment, gaming, financial services, and more all rely on data exploration to better understand which factors are driving business outcomes. Some of the top business use cases for data exploration include gaining:
Sisu client Samsung has used our automated data exploration tools to answer key questions about customer behavior and preferences. They specifically used our technology to figure out which types of customers are most likely to upgrade devices, which are more interested in older models, and, ultimately, which factors influence these behaviors.
Traditional business intelligence (BI) tools failed to handle and process the massive amounts of data Samsung has generated from selling 80 million new handsets per quarter—that is, about 2 billion Galaxy phones sold over the last decade. Sisu’s automated data exploration capabilities, however, which seamlessly pull from data sources like Snowflake Data Cloud,
Amazon Redshift and Amazon Athena, Google BigQuery, Microsoft SQL Server, CSV tools, and more, have helped the company analyze hundreds of variables across its datasets.
As a result, Samsung has been able to look at customer demographics, device preferences, and customer interactions with the brand, and analyze this information to create actionable insights that have helped the company drive customer upsells, retail sales, and overall campaign performance.
In addition, what used to take the company weeks to answer can now be answered in real time on a daily basis using Sisu’s dashboards, meaning the team can act upon learnings as they uncover them. Not only that, the brand is now able to address and resolve even more issues than ever before, using our technology to answer 10 times the questions they used to, all while saving hundreds of hours per month.
There are two main types of data exploration tools and techniques: manual data exploration and automated data exploration.
When it comes to manual data exploration techniques, companies have a few different choices. They can write scripts to examine raw data using open-source tools built using Python or use manual data exploration tools like Microsoft Excel or Google Sheets spreadsheets to examine data in its raw format and create simple charts and data visualizations to detect patterns and correlations between categorical variables.
Some common exploratory data analysis (EDA) techniques and visualizations data analysts frequently use to look into specific questions about a given dataset include:
For speedier results beyond what’s possible with these manual methods, companies are increasingly turning to automated data exploration and visualization tools like Sisu for a less time-consuming analysis process.
For instance, our Sisu data analytics technology is designed to accelerate data exploration for analysts, enabling your valuable team members to quickly explore their metrics and detect meaningful changes in their data ASAP, using machine learning and statistical analysis.
Algorithms can be used to create intelligent machine learning models that can learn from data and be applied to understand big data at scale, unlocking connections that the people working on your team don’t necessarily have the time or processing capabilities to uncover. These kinds of machine learning algorithms are the basis of data automation tools like Sisu, which uses machine learning, artificial intelligence, and augmented analytics to help analysts discover insights faster.
Let’s take a look at some of the most common questions about data exploration.
Data discovery is the first step data analysts must take before conducting any type of data analysis or data visualization activities. This process is about understanding which datasets need to be examined.
If that sounds a lot like data exploration, that’s because data discovery has a lot in common with the process of data exploration. However, data exploration typically emphasizes addressing focused questions or known issues, exploring different variables. While data exploration, on the other hand, is a broader, more general approach. Data exploration involves getting the bigger picture of your data before you may even realize which questions you want to ask and eventually find out the answers to.
Think about it this way: Once you glean insights from your data using data exploration, then you can dig deeper using data discovery to consider the why behind what you’re seeing for further analysis.
There are a lot of overlaps between data examination and data exploration. To understand the difference, think of the purpose of data examination as a way to perform data quality checks to make sure datasets can be used for analysis during the process of data exploration.
Traditionally, data exploration has been a manual method or approach used to gain insights for analyses. On the other hand, data mining uses machine learning to detect patterns within data using algorithms to automate the process.
With the advent of automated data exploration tools like Sisu, the lines between manual data exploration and automated data mining are blurring. The terms can be used interchangeably.
If rote, repetitive data exploration is slowing your data scientists and data analysts down, automated data exploration could help cut your data analysis time from hours to seconds. Learn how you can understand what factors matter the most to your business faster by teaming up with Sisu, named a 2021 Gartner Cool Vendor in Analytics and Data Science.
Connect with one of our Sisu experts and see for yourself why leading companies like Samsung, Mastercard, Wayfair, Upwork, and Gusto are using Sisu’s machine-learning powered decision intelligence engine to increase analyst efficiency and decrease data prep by automating data exploration using AI.