While the term ad hoc almost sounds like it could be a character out of Star Wars, it’s a Latin term, which means “to this” or “for this.” In the modern context, ad hoc is basically a way of saying something is done for a specific, likely one-time situation.
Ad hoc analysis, then, is when data is analyzed for such a scenario. One might use it to decide if option A or B is better for situation C based on historical data. It’s what happens outside of standardized dashboards and your everyday key performance indicators.
There are several reasons why ad hoc analysis can be a useful tool for enterprises trying to get the most out of their data. This certain form of data democratization carries big implications for the future in terms of how organizations use and act on data analysis.
Here’s how enterprises are leveraging ad hoc analysis for improved outcomes.
Get Insights from More People
Possibly the most critical benefit is the way it can speed up the entire decision-making process within an organization, as well as democratize data. With the old way of doing business intelligence, all data analysis needed to be done by a certain team of people. This included all inquiries, regardless of scale, urgency, or importance.
This was simply the way things used to operate — out of necessity. Only certain people were qualified to be working with data in a way that could ensure accuracy. Thanks to major improvements in software and cloud technology, however, this is no longer the case.
With ad hoc analysis, enterprises can get insights on lower-level questions in a matter of minutes. Relevant stakeholders can now get answers in less time than it used to take them to pose their queries to the data team. Even better, domain experts and business people can find their own insights. That’s a remarkable amount of power when you consider how much better business leaders can make decisions when they have data at their fingertips. Furthermore, ad hoc analysis can help foster a data culture within a workforce. When more people can use data for decisions, it becomes the norm across the organization. When people have data at the top of their mind, they’re also likely to ask questions that otherwise might have been ignored—spurring entire new fields of insight.
Allow Data Experts to Work on More Important Tasks
As already mentioned, it used to be that specialized data workers were needed for all data inquiries. This wasn’t just a time drain for the people waiting on answers; it sucked up tons of time for the data analysts as well. These are individuals whose talents are much better used doing deeper dives into higher-level data questions. Having them fetch basic visualizations every time someone needs to put together a PowerPoint isn’t a productive use of their time and expertise.
Extracting more time for data experts to do more in-depth work is one of the biggest reasons to leverage ad hoc analysis. Having everyone working on tasks that match their expertise shows how it can put an organization’s operating efficiency into overdrive.
Consider how important this could be in times of crisis. If an unexpected setback hits an organization, there might only be a short period of time to respond to it. Ad hoc analysis gives enterprises the ability to make tough decisions in shorter time frames.