The DataHero Blog

The Difference Between Reporting and Analysis and Why it Matters

August 14th, 2017

Many people were certain that Hillary Clinton was going to win the presidential election, but their assumptions were based on polling data reports, not data analysis.

Understanding the distinction between data reporting and analysis will help you avoid making similar data mistakes with your business. Data reporting, like the poll data used in elections, can tell you what is happening with your business, but without context or knowing why something is happening, your interpretation won’t be based on all the facts.

Simply put, reporting uses data to track the performance of your business, while an analysis uses data to answer strategic questions about your business. Though they are distinct, reporting and analysis rely on each other. Reporting sheds light on what questions to ask, and an analysis attempts to answer those questions.

data-reporting-flow-chart

[Source]

Luckily, you don’t need to be a data scientist to build reports or make analyses for your business. We’ve laid out how to use both approaches to get the most out of your data.

How Data Reporting Reveals The Right Questions

In the same way your car dashboard monitors important metrics like gas consumption, mileage and speed, your business needs a dashboard of data reports to ensure everything is running smoothly.

Data reporting is the process of organizing data into charts and tables in order to track performance of your business. This raw data keeps you aware of what is happening with your business. When your business is not reaching one of its goals, your reporting charts should alert you of the issue, prompting you to respond.

While reports are the first line of defense for your business health, it is often impossible to extract insights that can help you fix an issue or seize an opportunity. For example, this Sales Win/Loss report gives you an indication of individual sales performance, but the data doesn’t explain why each rep has different results.

data-reporting-using-datahero

If you want to understand why some reps perform better than others, this report can’t help you. That said, the report did its job to make you aware of individual performance and prompted you to ask questions. To find the answers, you will need to do a deeper analysis of your CRM data.

How Data Analysis Helps You Find Answers

Data analysis is the process of examining data with the goal of answering a business question that supports decision-making. An analysis can reveal powerful insights if you are able to uncover why something is happening and what you can do about it.

Here are three key steps to building an analysis that helps uncover insights:

1. Start With Specific Questions. Before you dig into your data, write down what questions need to be answered to achieve your goal. The more pointed the question, the more valuable and actionable the answer will be.

For example, instead of asking, “How can my sales reps improve performance?”, you need to ask something like this: “Where in the sales pipeline are my higher performers spending their time vs. lower performers?”

2. Identify Data Sources. When you start with a detailed question, you are able to pinpoint the data needed to formulate an answer from that question. Using the example above, you can determine that you’ll need sales pipeline data, specifically time allocation by each rep within each stage of the pipeline.

Once you’ve chosen your data sets, integrating the data and visualizing the analysis can be done in a cloud-based data tool like DataHero.

3. Interpret Results. Data analysis still requires you to make a conclusion about your findings. As you find interesting facts or patterns and put them in context to your business question, you’ll want to test your conclusion by asking yourself, “Does the data answer my question and defend against any objections? How?”

An Example of How Reporting and Analytics Work Together

You don’t need to be a data scientist to build reports and analyses when you use DataHero. Continuing the sales performance example from earlier, here is how you can build analyses to help answer your questions.

Let’s pretend you are the head of sales and your goal is to improve your team’s performance. The first step is to review performance metrics like the wins and losses report. This is a report you can easily build in DataHero by integrating your CRM data.

Win/Loss Ratio Report

Create DataHero Chart: Drag the following attributes onto the chart canvas:

  • Opportunity Owner
  • Win/Loss

data-reporting-win-loss-report

This report highlights how many deals have been won or lost for each sales rep. As the head of sales, you need this performance data to evaluate your team, but you also want to learn how you can help the lower performers improve.

Perhaps your first question based off this report is, “How are the high-performing reps allocating their time within the pipeline vs. lower performers?” Here’s how you can quickly build this analysis in DataHero to help you answer this question:

Analysis: Stage Duration by Sales Rep

Create DataHero Analysis: Drag the following attributes onto the chart canvas:

  • Stage Duration
  • Stage
  • Opportunity Owner

Data-reporting-analysis-chart

When you see where each sales rep is spending their time in context to their performance, you can start asking deeper questions. For example, one of the lowest performers spent a lot more time in the negotiation stage (see green arrow).

A natural follow up question is, “Does spending too much time in the negotiation stage affect overall performance?” You can find this out by analyzing the average duration of each stage with win/loss data.

Analysis: Average Stage Duration by Win/Loss Ratio

Create DataHero Analysis: Drag the following attributes onto the chart canvas:

  • Stage Duration
  • Stage
  • Win/Loss

data-reporting-time-spent

The analysis shows you that exceeding 25 days for the Negotiation Stage will probably lead to a loss. By digging deeper into the data, you are able to help your lower performing reps who were spending too much time in the Negotiation Stage. You can now coach them to spend less time on accounts that are being stalled in negotiations to focus on new opportunities.

Your Win/Loss report monitored sales performance and raised the questions, while the analysis led you to an actionable result. Data reporting and data analysis must work together to deliver value.

When You Control Your Data, You Can Find The Answers

In the case of the Presidential election, most polling reports did not show the Electoral College votes or dive deeper into votes by state, which were critical data points for winning. The polls were directionally accurate in that Clinton won the popular vote, but the reports were not answering the right questions to help achieve the objective — winning the election.

For your business, you no longer need to be a data-trained professional to build reports and analyses that help you answer the questions unique to your business objectives. Self-service data tools like DataHero put you in control of your disparate data.

When something goes wrong, you no longer need to guess or assume the best solution. By asking the right questions of your data, you can create your own analyses that generate the insights you need to keep your business moving forward.

By Walter Chen

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