Auto Insights: What Makes a Good Use Case?

[MeganDibble]

Have you heard? One of Alteryx’s new cloud offerings, Auto Insights, allows you to democratize analytics, automate ad-hoc reporting, and free up capacity on your analytics teams. [Cue: excited murmuring.]

With all these benefits, the logical follow-up questions are “how?” and “when?” How do I leverage this new technology to add value to my organization? And when do I have a good use case? This post will cover the three main components of a good use case and some business examples to give you an idea of how to use Alteryx Auto Insights (AAI).

Current Analytics

First, I will address the analysts: if your team wants to implement more self-serve analytics for the business, then Auto Insights may be a great fit. AAI allows analysts to save time by eliminating single-use dashboards and ad-hoc analysis. Instead of writing new queries for every request, your business users can answer their own questions.

Now, for the business users: if you feel as though you do not have enough analytics support to assess the root cause of business issues, then you will like the Alteryx Auto Insights product. Once AAI is loaded with data, you can derive personalized insights from that data, all with a few clicks. When you are on the hook for performance outcomes, you can get the answers you need when you need them.

Business Process

Auto Insights provides the most value when the data is a part of a re-occurring business process. If you have a huge Excel file that must be updated manually for each reporting cycle, stop right there! Auto insights can ease your pain. Analysis for re-occurring business processes is automated—even if the questions you want to answer change slightly.

For example, let’s say your company builds a “mission” with HR data on employee turnover over time, and this data can be refreshed every month. In the beginning, the HR users might be most interested in “why are so many people leaving our company?” Then, as the monthly refresh & report out continues, HR might get better at retaining people, and now they want to dive into the details by the fields manager, department, and region. Or perhaps they want to start analyzing a different measure in the dataset, like vacation time taken. In both cases, users can adjust the filters and the measure to answer the most important questions facing their department.

Data Availability

The first requirement for Auto Insights data is that it is time-series data. Put simply, there is a date column in your data set with a range of values. The AAI interface is set up for time series data—you can compare periods easily, see trends over time, and drill down to understand the “why” behind the change.

Another requirement is that your data must be clean before importing it into Auto Insights. For a full definition of clean (which includes specific formatting), please reference this help document. And for training on how to configure your data, you can reference this interactive lesson.

Finally, your organization’s data must have multiple dimensions to perform root cause analysis and create data stories. A dimension is any column you could use to segment your dataset, like region, sales organization, sales campaign, etc.

Examples

If it is hard to imagine a good use case at your company, below are some concrete use case examples.

#1: Support Center

Use Case: Call center team leaders can analyze support data and gain insights into where calls and queries are coming from, which products are impacted, and where up-skilling opportunities are for their teams.

Opportunity for Auto Insights: The data is time-series, with multiple dimensions to explore (department, team, consultant, call type, complaint type, etc.) Also, there is a need to perform analysis over time to drive better performance. There will be a significant “line of business” audience with multiple call centers—maximizing the value gained from the insights.

#2: Supply Chain

Use Case: Business leaders can gain insights into the various components that make up both product mix and sales mix, identify changes in vendor quality, and understand historical trends.

Opportunity for Auto Insights: Users can gain insight into the impact of changes in material cost and root causes of changes in sales margin. Users can also perform a vendor analysis against industry standards using ratio calculations and understand any changes in vendor quality (e.g., order fill rates or material quality.) Data analysis is done over time, and there are multiple dimensions to segment the data.

#3: Healthcare

Use Case: Users in healthcare services or health insurance can utilize patient data to gain insights into their medical and claims history.

Opportunity for Auto Insights: There is generally rich data segmentation (like demographics and medical history). The data available allows for time comparison and actual vs. target analysis. Additionally, automated anomaly reporting could help identify patient risks. One thing to note: AAI is only available as a cloud solution, but some businesses may prefer to keep sensitive information on-premise.

Summary

The use cases do not end with the three examples given above. Sales, marketing, and user engagement data are well suited for Auto Insights too. If you want to give AAI a try, click here. And if you think you have a use case but are not sure, comment below, and we can figure it out together!