Making decisions before you have suitable information can be a costly mistake. The temptation is often great to rationalize one’s actions based upon the hoped for outcome in decision making situations. However, applying the latest analytics tools to the data you have at hand is far more likely to return a desirable result. In fact, data analysis holds the potential to improve customer relations, predict upcoming expenditures and anticipate upticks in demand.
Table of Contents
What is Data Analysis?
Simply put, reviewing available data to gather information upon which to base decisions is referred to as data analysis. More broadly, data analysis is the process of filtering, studying, deciphering and visualizing information. The goal of the process is to reveal insights capable of informing keener and more effectual judgments, which can lead to more successful endeavors.
There can sometimes be a bit of confusion regarding the terms data analysis and data analytics. While similarities between the two processes exist, analytics encompasses data management as a whole. This includes collecting, storing, organizing, and analyzing information. Further, the tools and methods employed to examine data and communicate the results of the processes are included under the data analytics heading.
Data analysis, on the other hand, is more about converting collected intel into metrics, information and validations.
Conducting Data Analysis
Approaching the process in an organized fashion greatly improves the potential for accuracy. Taking the following steps, in the order presented here, tends to render the best results.
Define the Goal
What is the desired outcome? Whether it’s a problem you’re trying to solve, an area of operations you’re seeking to understand better or if you’re trying to decide how to proceed, you must define that goal at the onset. This will help you formulate specific questions, and give you an idea of what to look for. Approaching the process without clearly defining your need is unlikely to return actionable data.
Gather the Information
You’ll need facts on hand before you can begin to analyze them. You’ll also need to know where to look to acquire the data you’ll need. This could be sales records, customer demographics, lead tracking, or other sources of pertinent information. You’ll need to be certain you’re getting appropriate information — and enough of it — to help you make an informed decision. However, you must also be careful to avoid gathering so much information that you fall victim to paralysis by analysis.
Ensure its Hygiene
Inputting garbage will typically result in garbage output. Examine the accumulated data for accuracy, account for outliers and null values. Eliminate duplicate entries, standardize the data’s structure and correct syntax errors. The goal here is to avoid “GIGO”. Ensuring the hygiene of your data before beginning analysis will greatly improve the quality of your analysis.
Mine For Insights
The nature of your goal, or the information you seek to uncover, will play a significant role in determining the type of analysis you apply. Among the methodologies to consider are:
- Diagnostic analysis, which can be employed to find the cause of a problem and potential solutions.
- Descriptive analysiscan help you describe data through summaries of key sections of it.
- Predictive analysisuses historical data and statistical modeling to forecast future outcomes or trends.
Interpret and Apply Your Results
At the conclusion of these steps, facts related to your query should emerge. What recommendations do your findings indicate would be effective? Data driven decision-making can improve your organization’s ability to acquire new customers, keep them in the fold and expand your profitability.