How to Automate Reports (And Deliver Valuable Insight)

How to Automate Reports (And Deliver Valuable Insight)

If you’re one of the 247 million advanced and expert Excel users1 in the world, you’ve probably used Excel to automate parts of a very time-consuming and manual reporting process. Plenty of functions, formulas, scripts, and other features are at your disposal.

SQL, APIs, and other services help, too.

But do the reports you create deliver the information and insights people need to make data-driven decisions? Or are they just ad-hoc requests designed to answer a simple question?

Furthermore, can you automate them in an efficient and dynamic manner, then replicate and distribute that process to other people, who can then quickly replicate results and turn them into automatic insights?

A Checklist for Automated Reports

For your reports to help with the decision-making process, they need to help everyone understand:

1. What happened?
2. Why did it happen?
3. What could we do next?
4. What should we do next?

However, creating reports that answer all those questions is challenging when processes are manual, not easily adjustable, and further analysis is needed to get the required information.

It’s for these reasons that most reports only provide information for the first step of the decision-making process, and many analysts are asked to create reports and dashboards that provide descriptive analytics only.

But the status quo is only sometimes the best route. There’s a golden opportunity awaiting you if you can create reports that help people answer questions at all stages of the decision-making process.

Let’s talk about why the reports (and dashboards) you’re creating now don’t deliver that information and what you can do to fix it.

Why Most Reports (and Dashboards) Don’t Cut It

Reports and dashboards are the two main types of reports companies use to track KPIs and other metrics. Most answer questions about performance, such as how much a specific metric changed year-over-year or from one quarter compared to another.

That kind of business intelligence is great for understanding the overall growth or decline in the performance of a KPI—and the first step of the decision-making process, which is understanding the results.

However, it only helps a little with all the steps of the data-driven decision-making process.

Let’s take a look at why.

The Data-Driven Decision-Making Process

1. Understand the results
2. Interpret the results
3. Use results to determine potential next steps
4. Execute the best option

1. Understand the results

These are usually questions answered by yes, no, a number, or a word or phrase.

• How much did revenue go up or down?
• What was the result of our recent campaign?
• Are we meeting our goals for the year?

Knowing this information requires a high-level overview of the data. Reports and dashboards are great at this.

2. Interpret the results

At this step, people begin asking “why” questions?

• Why did revenue go up or down?
• Why did our recent campaign perform the way it did?
• Why did or didn’t we meet our goals for the year?

To understand this, you’ll need to explore the data behind the results. You can find this information with reports and dashboards built in Excel. Still, it requires time, knowledge of Excel, and further analysis—from you and the people using Excel.

3. Use results to determine potential next steps

During step 3, people will ponder all the actions they could take to drive change using “what could” questions.

• What could happen to grow revenue?
• What could happen to increase campaign performance?
• What could we do to meet our goals this year?

Understanding the answers to these questions requires predictive analytics that provide confidence scores and coefficients. This is possible in Excel with small datasets, but the spreadsheet software isn’t built to handle large or complex datasets.

This process also requires preparing the data, which is faster and more efficient when automated—especially in a workflow. Automated workflows allow you and others to trace the steps of a process. You can use them to easily figure out how the data was modified, which is painful task when manually examining cells and formulas in a spreadsheet).

4. Execute the best option

At this step, decision-makers will have a few possible options to choose from. What they’ll want to determine is the best action to take. To do this, they’ll ask “what should” questions.

• What should we do to grow revenue?
• What campaigns should we continue or run next?
• What should we do to hit our goals for the year?

Knowing the best option requires prescriptive analytics. Again, this is possible in Excel if you use small datasets but not large ones.

Improving the Reporting Process

Now that you know the kind of information everyone needs, there are four things you can do to improve the reporting process.

Every report you produce should be:

1. Easy to understand
2. Shareable
3. Self-service

Here’s how each of the three criteria helps with the decision-making process to deliver actionable insights.

Easy To Understand

All team members should be able to understand:

• Performance without asking, “What am I looking at?”
• What factors contributed to the results
• What factors could have the most significant impact on future performance
• The best decision to make in any situation to deliver results

Shareable

All team members should be able to:

• Share reports with anyone who may need the data to make decisions
• Maintain compliance and protect sensitive/personal data while viewing
• Customized reports for regions, partners, and third parties
• Share reports with auditors or anyone who may have permission to request data

Self-service

All team members should be able to use reports to:

• Generate custom insights without needing to have technical expertise
• Query data and discover what factors contributed to performance
• Answer questions about best possible options without requiring ad-hoc requests
• Explain why a specific action is the best solution to a problem

Making the Shift from Excel to Automated Reporting Tools

As great as Excel has been for data analysts over the past few decades, it can’t keep up with the modern analytics and business data many companies need.

As a dashboard tool, it needs more processing power to handle large datasets. It has apparent vulnerabilities in maintaining data compliance. And it was never built to easily share reports.

So, what should you use instead?

Well, there are many options available, but here’s a checklist you can use to select an option that covers everything we talked about.

Checklist for Automated Reporting

• Imports and aggregates data from all the sources you use now and may use later, such as ERPs, data warehouses, and more
• Reads and handles all the data types you use now and may use later, including real-time information
• Integrates with other analytical tools and data sources
• Automates analytical processes, such as prep, blend, clean, analysis, advanced analytics, machine learning, data science, forecasting, and more
• Exports data to visualization services you may use, such as Tableau, etc.
• Provides functionality to deliver scheduled reports
• Centralizes data assets, including datasets, workflows, and reports
• Enables access controls to ensure only people with the proper permission can access specific data
• Delivers clear data lineage for auditing and more
• Provides interactive visualizations, reporting dashboards, and more that people of all levels of experience and technical knowledge can use
• Automatically surfaces insights for decision-makers
• Offers both cloud and on-prem options
• Enables learning with intuitive interfaces, short learning curves, and resources
• Empowers and encourages everyone to use as part of the decision-making process

The New Reporting Process in Action

Now that we’ve covered all the information you should include in a report, how it helps with the decision-making process, and what you need from your analytics software to support this endeavor, let’s walk through a short, albeit simplistic, example of this process in action.

For this example, we’ll provide a hypothetical business performance example that reports on the sales of a new kitchen appliance that speeds up dinner prep.

1. Understand the Results

You start by exploring different business reports for the recent sales of product XYZ. This can include sales reports, marketing reports, paid social media reports, and more.

Instead of starting with processing the data to produce a single report, you anticipate the next questions stakeholders might ask, such as:

• What factors contributed to the rise or fall in sales of XYZ?
• What actions could the company take to improve or increase sales of XYZ?
• What action should your company take to ensure the desired outcome?

Because you can use report automation tools to automate data preparation and analysis of the data, you spend more time searching for additional information that will enrich your analysis.

You search a centralized hub or repository of all datasets that might help answer all these questions, such as past performance, marketing and sales campaigns, and more. You also search for external data sources, such as weather, economic factors, surveys, and more that you could enhance your reporting with.

Once you have everything you need, you automate the process in an analytics automation tool that prepares the datasets for you or even upload the data to an automated dashboard. Then, you upload the data into software that automatically generates insights for anyone using it.

The software ideally uses AI and can automatically surface anomalies, outliers, and trends for anyone who uses it. It automatically generates reports and delivers the results to you and anyone else who needs them. This is especially helpful if you integrate your automated analytics with regularly updated data sources.

You can also enrich those results with data from third parties and leverage machine learning to look for correlations between supply, pricing, campaign efforts, and more.

2. Interpret the Results

At this point, people will begin asking questions. Sales of XYZ are up in Region A but down in Region B.

Because the software you use automatically generates insights, some people can explore this further, identifying factors that led to increased sales in Region A. But maybe they need more information to determine what led to the decrease in Region B.

Instead of having to go back and edit your Excel database, reports, and dashboards, you can search for new data and easily incorporate it into your automated workflow—which then feeds it into the automated reporting software you’re using.

You can adjust your CRM system to collect data, adjust tables, and push data into your cloud data warehouse directly. To simplify data analysis, you can also sample datasets instead of pulling all your data from a cloud data warehouse, saving time and money.

Typically, you need a data engineers to pipeline all of this, but the automation makes it all self-service.

Because of this, you’ll be able to find more correlation between your data and perhaps your company discovers that a specific ad campaign was correlated to increased sales in Region A but not Region B.

3. Determine Potential Next Steps

Now that your company knows that an ad campaign produced more sales in Region B, there’s a mad scramble to figure out what ad campaign to run.

Typically, people would begin pitching ideas, but you can now perform predictive analytics to narrow down what factors of an ad campaign could increase sales the most.

At this point, you could run a survey in Region B and use that information to inform what kind of ad campaign to run. Or you could analyze your data to find the highest-performing ad campaign you previously ran in that region.

No matter your approach, you inform the decision with data by grabbing all the datasets you can by automating the preparation of that data for machine learning.

You learn that what contributes the most to high-performing ads in Region B are ads that:

1. Include discounts and savings
2. Are under 15 seconds
3. Display pages and videos explicitly related to meals anyone can make in under 20 minutes

4. Execute the Best Option

At this point, all that’s left to do is create a few ad campaigns, score them as best as possible for machine learning, and run a prescriptive analysis.

Your company creates three ad concepts, and you discover that ad concept C has the highest predicted chance of increasing sales.

Teams develop an ad campaign, run it, and await the results.

Conclusion

Obviously, the reporting process is never as simple or clean as the hypothetical example above. Hopefully, the example gives you an idea of what the reporting and decision-making process could be.

While Excel is a wonderful tool for creating simple data visualizations, dashboards, and reports, it’s limited in the amount of data it can process and how quickly new information can be added and analyzed.

To improve the reporting process and create a data-driven decision-making process, you’ll need to look for ways to automate as many data analytics steps as possible. This will give you more time to ask the questions that lead to discoveries and find the data you need to make decisions.

See how Alteryx Auto Insights automates the reporting process and uncovers the information your organization needs to make business decisions.