Ad Hoc Data Analysis: Definition, Examples and Top Tips

Data Visualization
Jun 12, 2024
Ad Hoc Data Analysis: Definition, Examples and Top Tips

Imagine you’re the head of sales at a SaaS company. An important product launch is coming up and you need to pick the best channel for getting the most sales qualified leads. You want the data, but the IT department says that it will take eight days to complete. To save the campaign, you need the power of ad hoc analysis.

Ad hoc reporting and ad hoc analysis bring the power of business intelligence to the hands of the everyday business user. Instead of waiting for someone else to serve you answers to your questions, you can fetch them yourself with self-service dashboards and data visualization.

Here is what ad hoc data analysis is and how it works.

What is ad hoc analysis?

Ad hoc is Latin for “as needed” and an ad hoc analysis is just that - an analysis of data as needed. It happens after ad hoc reporting.

Instead of receiving Excel spreadsheets on a weekly, monthly or quarterly basis, stakeholders can analyze data ad hoc, or as needed, to get access to real-time metrics and KPIs.

This allows for faster decision-making as key stakeholders can answer specific questions in the moment they need answers, instead of waiting for days or weeks to get actionable insights.

An example of an ad-hoc embedded dashboard in Kenjo

The benefits of ad hoc analysis in a business context

Ad hoc analysis allows anyone to create reports and drill down into business data to make better decisions. You don’t need data analysts or any knowledge of data science to be able to create a report, whip up some visualizations, interpret them and make data-driven decisions.

Here are some practical benefits of doing ad hoc analysis in your business.

Improved decision making

If everyone can understand company data, they can make important business decisions more quickly. They can take the data from the company-wide data sources, turn it into graphs and once they understand it, act quickly.

An example of an embedded ad-hoc dashboard in Spaceflow

Typically, it can take hours (in the best-case scenario) to days and weeks for the IT team to create dashboards for end-users. With a good BI tool, the end-users can create their own dashboards in a matter of minutes.

More flexibility and adaptability

Typically, the IT team will create static reports, which means that you can’t do any deep data exploration. With ad hoc data analysis, you start from the same datasets, but you can use a variety of analysis tools to run custom queries and get the insights you need.

For example, if you get a marketing analytics report, you don’t have to look at a static dashboard. You can use a BI solution to drill down into the data, and find not only the best-performing marketing channels, but also go deeper and find campaigns and keywords that drive revenue in a specific target market.

Operational efficiency

With ad hoc data analysis, very few people in your company need to know SQL to get valuable insights from your data, on-demand. In fact, you need just one person (or several) with knowledge of data governance and data management to prepare the data for reporting and analysis.

This means two things. First, any department in your company can use business intelligence tools to solve a business question. Second, the IT department that’s in charge of data preparation, reporting and analysis will have more time to do the things that matter for the overarching business goals.

Personalized reporting

Everyone has access to the same datasets. Only now, they also have the know-how to create one-off reports that suit their own business needs.

With self-service BI tools, business users can adjust each report and analysis to their individual needs. For example, a COO and a CTO might take the same dataset on business expenses for a fiscal year. One of them will look into the profitability of maintaining an outsourced tech team, the other will look into the most used programming languages across clients.

Cost efficiency

Regular reporting can be costly and time-consuming. If you need to build a large report once per month, it may be pricey to create something that suits all users and their individual needs. And when you do need personalized reports, it is going to cost extra on top of the scheduled ones.

Ad hoc reports and analyses help streamline your BI operations. Thanks to features such as templates, pre-built access to data warehouses and many others, many BI operations such as forecasting can be done quickly and for dollars at a time.

Top ad hoc reporting tools to try in 2024

Want to empower your own team with ad hoc reporting and analysis? Here are some top choices to consider.

Luzmo: for SaaS businesses that want to provide embedded analytics features to their end users. Allows anyone to create and analyze data from a dashboard.

Looker: for enterprise companies looking to master big data analytics. Comes with a steep learning curve but rewards you with beautiful visualization options.

Microsoft’s Power BI: for companies of all sizes looking to create simple dashboards quickly. Has a huge online community you can tap into if you need to troubleshoot, and there are plenty of PBI developers available.

Tableau: for any business looking for a user-friendly BI experience with a good choice of data sources and visualizations. Easy to scale but may end up being very expensive.

Wrapping up

Ad hoc data analysis is the future. With the advance of AI-based business intelligence and easy-to-use self-service analytics tools, anyone will be able to create reports and dashboards and interpret them, on their own terms.

This includes SaaS app users too. Imagine giving your end-user full access to their app data so they can slice and dice it and see how much value it brings to their business. With Luzmo, you can do just that.

Launch your first embedded SaaS dashboard in hours - not days or weeks. Book a free demo with our team so we can show you how.

Mile Zivkovic

Mile Zivkovic

Senior Content Writer

Mile Zivkovic is a content marketer specializing in SaaS. Since 2016, he’s worked on content strategy, creation and promotion for software vendors in verticals such as BI, project management, time tracking, HR and many others.

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