Exploratory Data Analysis (EDA) is a visualization concept developed by John Tukey in 1977. It involves gathering information and presenting it—usually through visual models—to make it easier to understand and analyze.
People use EDA every day since many decisions are based on data analysis. Before making a large purchase, like a car, we read reviews, research safety ratings, test-drive different models, and check our finances. All this data helps us make a final decision. Similarly, when applying for a car loan, the bank uses EDA to analyze our finances, credit history, and job security to decide whether to grant us a loan and under what terms. Just like banks, businesses should use EDA to maximize their profitability.
The EDA process typically involves simple techniques like data plotting, applying statistics, and creating visual representations of data to reveal patterns and trends.
EDA is sophisticated because it determines both how to define a data set and how to scrutinize it. If the wrong data is analyzed, the process is ineffective. Similarly, using the wrong analysis method renders even the best data useless. EDA experts develop an intuition for what to look for and how to interpret their findings.
EDA is crucial for informing decisions by revealing patterns, not by confirming or rejecting assumptions. It is the initial examination of data and should occur before any assumptions or conclusions are made to avoid faulty analysis.
When Tukey wrote his book to define EDA in 1977, it was an analog process with limited information available. The invention of the internet and digitization has drastically increased the amount of available data, making EDA applications more efficient and complex.
With the rise of online searches, businesses had to adapt by building websites, optimizing search engine results, and quickly responding to customers. This led to businesses performing their own EDA: how to build a website, who to hire, and what online presence would best serve and attract customers. Forward-thinking businesses also asked: What data should we track online, and how can we leverage it? This is the foundation of business intelligence (BI).
In BI, EDA helps businesses investigate data by making sense of vast amounts of information generated daily. This enables companies to uncover patterns that human analysis might miss and predict sales trends, informing decisions about marketing, sales, and customer retention.
Since the internet’s rapid expansion in the 1990s, businesses have continuously adapted to the changing digital landscape. The internet has made markets more competitive, and businesses cannot afford to ignore the powerful applications of EDA if they want to stay relevant and viable.
EDA vs. Reporting
Some companies may believe they have an EDA process in place when they are simply generating reports. Reports often lead companies to chase their tails because they are not forward-looking. Reporting shows what was relevant in the past, so actions taken based on reports are retroactive and may not reflect the current or future market reality.
For example, most businesses run regular (monthly, quarterly, annual) financial reports. This standard procedure shows a management team or board of directors what was spent and on what during a given time period, in addition to how much liquidity the business has. While these static reports have practical applications, they are not very useful for informing business strategy.
By applying EDA to financial information, a company can analyze its finances to reveal trends in past or present performance, which can then inform future strategies. The most common elements of analyzing finances using EDA are horizontal analysis, vertical analysis, and ratio analysis.
- Horizontal analysis examines specific line items across a period of time (the longer—the better).
- Vertical analysis takes all line items and breaks them down as percentages of the whole.
- Ratio analysis uncovers statistical relationships, reveals strengths and weaknesses, and shows how well a business performed over a given period.
Financial analysis is crucial for any business, as is sales analysis. Again, most businesses run sales reports but don’t necessarily perform sales analysis. As with finances, sales reporting plays an important role, but it does not replace EDA. Sometimes reports reveal differences or anomalies, but without strategic analysis, what can be done with that information? How can it be used to translate into more sales, higher customer satisfaction, or better retention rates? Looking at trends to predict the future is a key element of successful EDA and gives companies the best chance of translating data into future success.
Why Use EDA?
EDA is not a new practice. Larger businesses have been using it for years, and now more small and mid-size businesses recognize the critical role a streamlined EDA system can play in attracting and retaining customers.
By examining the data your business collects, initial EDA efforts allow you to manage that data efficiently. This process can confirm existing strategies or reveal why they might not work. Even better, once EDA is integrated into business operations, it can reveal patterns before new decisions or strategies are implemented. Remember, EDA is meant to be an initial exploration of data.
Learning to use data analysis effectively may require a cultural shift in your business approach and education and buy-in from all stakeholders. This should be an easy sell because, when done right, EDA leads to improved customer retention and satisfaction and often translates into new customers and increased sales.
Take, for example, a produce supplier that does not track customers’ reasons for returning or rejecting orders. When a customer calls to complain, a sales representative does everything they can to make the order right—sends a replacement order or processes a refund—but does not enter data about what was wrong with the order in the first place. As a result, the produce company cannot meaningfully review and address its customers’ biggest issues. Was the produce spoiled? Was it misclassified or mislabeled? Was the order simply filled incorrectly? Without recording and tracking this information, the business cannot use it to improve its services or bottom line.
Now imagine that same produce company a year after implementing an EDA plan following training for one of its IT specialists. The data revealed that most customer complaints were related to fruit being overripe. The company worked with its farm suppliers to implement new policies on how the fruit is picked, packaged, and stored, and its warehouse team established new protocols for inspecting orders upon receipt and before shipping. The sales team was trained to educate customers on proper fruit storage—who knew that bananas emit a gas that ripens other fruits! The marketing team created infographics explaining appropriate storage temperatures and conditions. Complaints about overripe fruit dropped by 64%, and many customers started placing larger orders, trusting the product and the business more. This simplified example powerfully illustrates how EDA can reveal patterns that can significantly impact sales.
EDA can also use outside data, like third-party data sets, to expose trends and patterns across the marketplace that your company can leverage to develop new marketing and outreach strategies and attract new clients. For example, a seasoned politician wanting to run for a higher office might purchase voter data from the state to find potential voters and donors. The candidate’s team can blend that data with existing data from previous campaigns to create sophisticated models of the most passionate or generous voters to target.
How Does EDA Work?
EDA is a theory of analysis, and each organization must create its own EDA practices. This is a multi-step process:
- Get Buy-In
All stakeholders must first agree that EDA is necessary. With the help of an EDA expert, the company decides what data should be collected. Successful EDA involves analyzing as many elements and dimensions of data as possible, so typically, a business chooses to track as much data as it can to define EDA effectively. - Collect and Organize Data
Data is collected from various sources, including customer relationship management (CRM) software and third-party data sets. It is then organized into a data management system, usually using enterprise resource planning (ERP) software. Common ERP software solutions include Jet Analytics and TimeXtender. Many companies use SQL Server Integration Studio (SSIS), but while SSIS will get the job done, Jet Analytics or another dynamic ERP solution will get you where you want to go much faster and with more flexibility and ease. - Extract Data
An expert (or team) trained in EDA identifies the specific parameters of the data to extract and the lens through which to view that data for productive analysis. The data set is then extracted into a software solution like Power BI or Jet Reports. From there, it takes just a few clicks to turn that data set into one or more dimensional models. A dimensional model is a user-friendly visual representation of a specific data set. While a dimensional model should be simple to read and interpret, creating one is quite complex and requires EDA expertise. - Analyze Data
The dimensional models reveal patterns and trends that can be used to develop new strategies; they may also reinforce existing strategies or highlight what isn’t working. These models can be recreated as often as necessary and tweaked to use more, fewer, or different data points. The sky’s the limit once a business commits to EDA.
What if a Company Has Limited Data to Analyze?
A business can’t explore or analyze a data set if they don’t track it initially. There’s an adage among farmers that the best time to plant an apple tree is 20 years ago or today. The same is true with data collection and analysis. Some businesses may regret not starting sooner, but it’s never too late, and there’s no time like the present to define EDA.
With each customer or client, many elements can be tracked, and the detail of the data tracked significantly impacts EDA. Think of grading systems in college. When professors hand out Pass/Fail designations, it does not provide a full picture of how any given student did in class. Was a “Pass” closer to an A or a D? The same is true with customer data. It’s not enough to have their basic details; you also want to formally track their ordering or purchasing habits, including the month, day of the week, and even time of day. Which sales representative did they talk to? When they reached out for support, which representative worked with them to resolve their issue? Did their purchasing habits change after that support ticket was resolved? These are just a few of the many data points that can and should be collected by any business wanting to implement a robust EDA program.
Implementing new data collection standards and processes can be done in a short amount of time, and a useful data set will accumulate quickly. EDA experts can work with your staff to determine what data to track, how to track it, and which software solutions make the most sense for your business. If you start now, a year from now, you will have a rich data set to work with and define EDA within your business. If you don’t, a year from now, you may be falling behind your competitors.
What if a Company Has Multiple Data Sets Across Various Platforms?
Commonly, a business will have multiple data sets across numerous platforms, none of which talk to each other. A business may use QuickBooks and SalesForce, both of which can provide rich data for analysis, but if those systems aren’t exporting into a blended data set, they cannot be used in conjunction with one another to allow for the most effective analysis.
Imagine a retail or eCommerce store that does not analyze customer trends. All of the data is there because door readers show how many people come in and at what time, and all sales are rung up in their Point of Sale (POS) system, but neither the door reader data nor the POS data is exported to a blended database, so no one is looking at that data meaningfully. This has tremendous implications for staffing levels, sales and promotions, and ordering practices. If the data is there, but no one is using it, what good does it do?
The solution is to build a blended data set that automatically exports data from all of your platforms into your ERP software of choice. This is simpler and more cost-effective than migrating data and makes data analysis much easier.
How to Use EDA to Become a Data-Driven Company
If your business wants to leverage its data but doesn’t know where to start, scheduling a call with an expert at Tigunia is an excellent first step. Whether you’re already doing some level of reporting or looking to advance with dimensional modeling and integrated data points, Tigunia is ready to help you elevate your data analysis and define EDA for your specific needs.
Smaller businesses might feel overwhelmed or think they lack the resources for EDA, but at Tigunia, we provide right-sized solutions tailored to your needs. We’re flexible to meet your requirements and specialize in customizable solutions that fit your budget. With Tigunia, you get reliable, ego-free assistance from experienced staff who will guide you through the process until you’re proficient in EDA.
Tigunia can help you choose the right ERP for your business or work with your existing software. Our full ERP team is ready to dive into any setup we encounter. Once you’re ready, we’ll export data from SQL servers into your chosen ERP software, blend it into a data warehouse, and start building dimensional models.
Data protection is critical when exporting from one platform to another, and Tigunia is committed to securing your data. We ensure that your data is merged securely, giving you easy and reliable access to all the data points you want to analyze.
Some clients have us manage all their data reporting and analysis, but more commonly, we teach our clients how to perform EDA independently. Our experts can train someone with database skills to run Jet Reports or Power BI in about a day. This includes one-on-one instruction, job shadowing, and guidance on what to look for and how to spot trends in data. Once your designated analyst is trained, they become a valuable resource for your company, capable of running all your data analysis and training other staff. Even after you’re up and running, we’re always available for questions, strategy sessions, or problem-solving any issues that arise.
Leveraging sophisticated tools to analyze your data provides a competitive edge. This is easier than you might think and essential for staying ahead in today’s market. Every forward-thinking business should take advantage of this or risk being eclipsed by competitors.