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The Beginner's Guide to Data Analysis

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Data analysis seems abstract and complicated, but it delivers answers to real world problems, especially for businesses. By taking qualitative factors, data analysis can help businesses develop action plans, make marketing and sales decisions, and excel in a crowded marketplace. If you are running a business or just interested in the concepts, you can use this guide to learn the basics of data analysis.

What is Data Analysis?

Data analysis is what happens under the larger umbrella of data analytics. Analysis of qualitative data allows businesses to run data analytics on a larger scope and address company-wide issues such as productivity and performance. What data analysis involves is evaluating qualitative data in the hopes of establishing a reason for something that has happened. It can also identify patterns that then inform business decisions.

What is Qualitative Data?

In essence, qualitative data is non-numerical data. It might include customer behavior, such as how and when a consumer clicks on a link. It can also include customer feedback, transcripts, or recordings of sales calls.

The Different Categories of Data Analytics

There are four main categories of analytics: descriptive, diagnostic, predictive and prescriptive. Descriptive analytics documents what has happened over a certain period of time. Diagnostic analytics assess the why in an event: Did an election affect sales in November? Predictive analytics looks to the future and determines what will happen based on previous, similar events and models. Finally, prescriptive analytics assesses data then makes recommendations based on that data analysis.

Developing a Hypothesis

As a first step, you want to develop a hypothesis for whatever event or behavior you want to explain via your data analysis. Is the heat during the summer affecting beer sales? Are the bright colors on a landing page driving more customers through your sales funnel or fewer? Take a moment to develop a workable theory for the why, then use your data analysis to prove or disprove your hypothesis.

Analyzing Your Data

The first step in any data analysis is segmenting the data optimally. You want to look at the metrics that most directly affect your hypothesis. Segment your data by category, looking at factors such as time or location. You can also code data to make it easier to analyze. Next, look over the data to spot trends and spikes. Finally, assess the impact of any trends that you spotted in your assessment of the data.

Developing an Action Plan

Ideally, data analysis leads to actionable decisions that improve business performance. If possible, compare your results with comparable research in your space or niche. Funnel your results into a report that you can share with your team for discussion on an ongoing basis.