Data collection tools and techniques have improved significantly, leaving companies with vast amounts of data—billions or even trillions of data points. There are many ways you can use this data: to improve customer satisfaction, to deliver a better product, or to raise morale among your employees. You’ve collected all this data through surveys, questionnaires, tracking software, and other means, and compiled it into spreadsheets.
But there’s a problem: the data as collected isn’t organized or structured. All you see is numbers. You thought that data collection was the panacea that would revolutionize your company, but raw data is only the beginning; alone, the numbers don’t do much.
In its raw form, data is difficult to analyze and interpret, even for skilled analysts who do it for a living. The key is to find patterns and relationships among the data. That’s the problem with data mining, which is defined as “the process of collecting, searching through, and analyzing a large amount of information in a database, as to discover patterns or relationships.” The word “large” may feel like a bit of an understatement here. Where do you even begin to try to make sense of these huge collections of individual numbers?
When you’re dealing with colossal datasets, it takes a trained eye and a mind with a talent for numbers—along with help from data mining software—to find the patterns hidden in the columns and rows. For the rest of us, a data spreadsheet can trigger a sort of “highway hypnosis.” Our eyes glaze over, our thoughts drift, and semantic satiation begins. Suddenly, you notice how weird the number graphemes look. The shape of the number “3” looks like something you’ve never seen before.
Here’s the good news, though: you have the data. This is the first step. But now, you need someone to analyze the data and draw meaningful conclusions. As a business owner, you have a few options here.
Option 1: Hire a Professional Data Analyst
Professional data analysts have undergone specialized education and training. They can examine your data and pick out patterns where others might only see numbers. These professionals can help you uncover the secrets hidden in your databases.
While many data analysts are employed by large companies, there are people who provide independent consulting services. It’s not out of the question that you could probably find someone who specializes in your industry. While working with a data analyst, you can communicate with them and gain better insight and understanding into the process. The downside to this option is that data analysts are highly paid, in-demand professionals. Although this kind of service and consulting isn’t cheap, the cost may well be worth it for you if the potential insights from the data are valuable.
Option 2: Use Data Mining Software
There are software programs available that can automate all or most of the data mining process. But while this option sounds ideal on paper, it’s not always as simple as uploading your database and letting the program work its magic.
Data mining software programs can vary substantially in their level of user-friendliness. Most software will require that you at least have a solid technical understanding of data analysis; after all, it was designed to aid professional data analysts, not necessarily to replace them. It’s not just a matter of dragging and dropping a spreadsheet and then printing out results.
The companies that produce the software can sometimes provide guidance on how to use it effectively. You may have to pay the company for training. This can make the software manageable, but in many cases, business owners still find themselves in over their head. You might end up bringing in a professional data analyst anyway.
But if you’re good with numbers, you’re tech-savvy, and you’re a fast learner, you can give this option a shot. Even if you need to dedicate one of your staff members to handling it, the cost will still be less than what you’d pay a data analyst (which will be in the thousands of dollars). It depends on how involved you want to be in the process. Some business owners can benefit from learning how to use data analysis software, and the learning process could bring future benefits.
Option 3: Do It Yourself—Learn Data Analysis
There’s a certain sense of satisfaction in mastering a new skill yourself. Learning data analysis isn’t for everyone. If you’ve never really been a “math person” and you don’t consider yourself very technical, this option might not be a good choice for you. But if you’ve got a head for numbers and learning to analyze data sounds intellectually intriguing and exciting to you, it’s possible to train yourself.
There are plenty of self-teaching resources available to help you learn the basics of data mining and data analysis. Whether your business simply doesn’t have the budget for a professional or it’s simply a matter of intellectual curiosity on your part, you can hone your data analysis skills with patience, practice, and an autodidactic approach. The following is a brief overview that covers the basics of data analysis.
Step 1: Anomaly Detection
Anomaly detection is the first step, intended to rule out any errors or inconsistencies in the data that could throw off your results. Anomalies can often become readily apparent, and there’s also a chance that the inconsistencies themselves will provide some preliminary insight into your data.
Step 2: Association Rule Learning
“Association rule learning” means searching for consistent relationships between any two variables in your dataset. You want to determine early in the data analysis process whether or not two given variables have any correlation or causal relationship. This provides the necessary foundation for further analysis, so this is a critical step. To truly understand your data, you need to find every existing pattern in it.
Step 3: Clustering
When you’re looking at your data, you might notice a large number of data points of similar value for one particular variable. By “clustering” these together, you might be able to find deeper patterns that weren’t obvious at the outset. To provide significant and relevant information, these clusters must be separate from other known structures in the data.
Step 4: Classification
Now that you’ve identified sets of patterns and clusters within your data, you can begin applying these newly identified structures to new information. You can apply them for analyzing new data in the future or test them to determine whether they remain consistent.
Step 5: Regression
At the regression stage, you’ll need to start applying the structures to develop an overall function that matches your data. This function will become your working model as you move forward, so it’s important for it to be free of errors.
Step 6: Summarization
Your analysis is basically complete at this point, and you’ll have gleaned valuable knowledge and insight from data structures and functions. Now, you may want to summarize this data and visualize it for future reference. This can make it easier to share your insights with your team and to use what you’ve found to guide your future business strategies.
This has been an incredibly basic and bare-bones overview of data analysis. If your head is already swimming, it’s definitely advisable to bring in an experienced consultant or at least make use of software tools that streamline the process. If your analysis ends up flawed, incomplete, or flat-out incorrect, it could have serious consequences for your business.
It takes years of studying to become a data analyst, not to mention higher-level mathematical skills. This might not be the place to take a stubborn do-it-yourself attitude. If you’re unsure, or you don’t feel qualified, spare yourself the headache and just hire someone. It may be costly, but the investment can bring considerable returns. Using insights from the data to guide your business strategy can have huge potential to improve your product, increase your profits, and meet other organizational goals. Hiring an analyst might be one of those things that essentially pays for itself.
Regardless of which route you take—hiring an analyst, using data mining software, or just crunching the numbers yourself—your datasets hold secrets that could transform your entire approach to your business. Unlocking trends and correlations within the data can be a very profitable endeavor, but it’s important to do it the right way.