In Latin, ad hoc means “for this.” As the Merriam-Webster dictionary points out, the English usage has essentially evolved to mean “for this specific purpose.”

In the context of data analytics and business intelligence refer to insights produced to meet users’ specific needs rather than produced in a generalized or scheduled manner. Think of it as the difference between being able to ask a role-specific question on the fly and receive a relevant answer vs. having to comb through a canned, monthly report in an attempt to find potentially useful information.

At the end of the day, ad hoc analysis via self-service data analytics is meant to make the process of gleaning data insights more immediate, flexible and customizable for a wide variety of business users.

Let’s take a closer look at how some components of ad hoc data analytics as well as some use cased in the modern workplace.

Components of Ad Hoc Data Analysis

Self-service analytics include a host of features that can work together to help its users ask questions and get specific answers from company data on an as-needed basis rather than having to depend on pre-existing reports.

Some components of ad hoc data analysis platforms available today include:

Search-driven analytics: A search analytics interface allows users to input their queries in a straightforward, natural way — comparable to how you’d type a question or statement into an online tool like Google. Search tools then create new, highly personalized insights in response.

Interactive data visualization: Rather than producing an entire report, ad hoc analysis tools can produce a single chart displaying just the answer to the specific question at hand — automatically formatted into the design best suited to the insight. Another important feature often found in conjunction with ad hoc viz is the capability to drill down further into the insights displayed from broad to specific — something static reports notoriously lack.

Personalized business intelligence dashboards: Again, instead of data insights being couched in larger reports, ad hoc tools allow users to create both individual charts and personalized dashboards for related collections of insights.

The goal here is to furnish a variety of decision-makers across the with speedier and more hyper-relevant insights than they could possibly get from generic, structured reports.

Ad Hoc Analytics in Actions: Some Use Cases in the Workplace 

Given the specific nature of ad hoc data analysis, it’s impossible to list even a fraction of possible use cases for this technology in today’s enterprise. However, considering a few examples can help us better understand how this individual approach to data analytics can easily apply across teams, departments, sectors and industries.

Here’s a scenario from SelectHub illustrating when ad hoc analysis can make a difference: A sales manager is analyzing company sales data, trying to understand product sales by location. The team has already run a national report, but it does not provide a detailed breakdown of sales figures by local areas. With access to an ad-hoc, self-service analytics platform, this employee can query sales by region, state, city or neighborhood — giving them more granular insights than if they had to rely on the national sales report to make production and marketing decisions.

Another example, this time from the healthcare industry, would be a clinician analyzing a patient’s risk of a specific condition, like diabetes, rather than having to extrapolate this risk level using a more general report of the patient’s health.

When you hear “ad hoc” used in the context of data analytics, think “specific.” This type of analysis aims to give users answers to their own particular questions whenever the need arises.