5 tips for guaranteeing ad-hoc analytics success

Businesses can use data analytics to spot trends and relationships in their data. From there, they can use that information to drive their decision-making and find new insights.

However, traditional data analytics has a problem. With basic data analytics in legacy business intelligence tools or less data-focused business software, employees have to rely on canned reports and pre-built visualizations. They can answer some business questions, but they can’t get much farther than that.

To make good use of their data, businesses need the flexibility to arrange, analyze, and combine it however they’d like. They shouldn’t have to rely on baked-in visualizations or static reports.

The same is true at the employee level. The average employee shouldn’t have to rely on dashboards and visualizations that are handed to them. They should be able to adapt and change the BI content they have access to, so that they can better answer new business questions.

This more flexible approach towards data is possible in modern, self-service BI tools. With modern tools, businesses and employees can adapt, change, and reconfigure their data analytics to their heart’s content, whenever they want. This approach is called ‘ad-hoc analytics.’

However, a business can’t just change its data approach overnight. It takes time and work to change a business’s data outlook, especially when the change is as big as this one. If a business wants to change from static, unchanging reports and visualizations to ad-hoc analytics, they’re going to have to put in the work.

If businesses want to really make full use of their ad-hoc analytics strategy, then they need to build a culture of data discovery, self-service insight, and operational curiosity.

With ad-hoc analytics, employees have the tools they need to drive data insight, make well-informed decisions, and improve business operations at every level. You just need to teach them how to use them.

Fostering data curiosity

Ad-hoc analytics is a massive paradigm shift for data operations. Other, less flexible approaches are top-down; they assume that there will be a central data team or data manager who will handle all your data analytics. Users get their dashboards from these data experts, but they don’t get any say in how they’re built or what they show.

In contrast, ad-hoc analytics is a bottom-up approach. There’s no centralized data team that handles all data requests; users can make their own requests and build their own analytics. However, this can only work if users are willing to examine their own data.

At many businesses, the average employee has absolutely no data curiosity. This is because they’ve never had the opportunity to act on their curiosity. With ad-hoc analytics, however, they can quickly answer questions in a self-guided way.

By asking novel questions of their data, by asking ‘What if?’ and ‘What about?’, employees can discover novel insights all by themselves. This is the most important element for driving a successful data culture.

To get there, though, businesses need to succeed in their ad-hoc analytics approach. That’s the first step in ensuring an effective, innovative data culture.

5 tips for ad-hoc success

Here are some strategies for ensuring successful analytics, both for employees looking to perform ad-hoc analytics and businesses looking to enable ad-hoc analytics success.

Start with a specific goal

One of the biggest challenges when doing ad-hoc analytics is the amount and scale of the data you can analyze. Once a business has taken the training wheels off, employees have access to huge amounts of data, and seeing it all at once can be paralyzing.

Instead of trying to sift through all this data trying to find insight, it’s much more useful for data beginners to go into ad-hoc analytics with a goal in mind.

For example, instead of trying to create completely new dashboards ad-hoc, it might be easier to adapt already existing dashboards by simply swapping out one or two data sets or changing the visualization.

Streamline data access

One key move that contributes to ad-hoc success is loosening data restrictions and allowing more data freedom. Instead of tightly controlling who can see what data, it’s more useful for ad-hoc analysis to free up data sources for novel analysis.

This way, employees who do ad-hoc analytics have more options for novel analytics. They can incorporate more data into their dashboards and visualizations, which helps them spot new trends and find undetected relationships.

Standardize your data governance

Even though data freedom is important to ensure ad-hoc analytics success, effective data governance is just as crucial.

Data governance is the name for the strategies and techniques businesses use to control access to their data. Ad-hoc analytics is much easier with clear, consistent rules for data access than it is otherwise.

Without clear data governance rules, employees will never know what sort of information they can access and what they can’t. You don’t want employees accessing sensitive information like payroll information whenever they want, but you also want to facilitate ad-hoc analytics through greater access to other data sources.

Data governance rules help employees to understand what sort of information they can access and what sort of information they can’t.

Self-service analytics

In older, more complicated BI tools, the average user might be able to perform their own ad-hoc data analysis, but they’d never know that they could. This is because they don’t have the skills necessary to actually use their analytical tools.

It’s very important to avoid this issue in your BI system; otherwise, you won’t be able to enjoy any ad-hoc analytics success. If your employees don’t understand their tool, they’ll have to rely on pre-built dashboards and reports.

To prevent this issue, invest in a self-service BI tool. Self-service tools are designed for use by data novices; they’re user-friendly, intuitive, and simple to figure out. This way, your employees can actually perform the analytics that they dream up.

Understand visualizations

To effectively utilize ad-hoc analytics to their fullest extent, employees and businesses need to know the how and why of data visualization. They need to know when to use different charts, graphs, and figures to best communicate their data implications.

Without this knowledge, users often end up using visualizations that are bad fits for their data analytics. Not every visualization can communicate the same idea in an efficient way. Often, using the wrong visualization can lead viewers to incorrect implications.

For the best ad-hoc results, users need to know what visualizations to use and why. This way, they can communicate data implications in the most effective way possible.

Ad-hoc analytics—the key to data culture

Ad-hoc analytics is what makes comprehensive, agile data cultures possible. Without it, there’s no way that businesses can extract data value from every level of their organization.

By following these strategies, businesses can ensure that their overall ad-hoc analytics strategy is successful. From there, any employee with access to data can use it to drive insight both at the micro and macro scales.

Ad-hoc analytics is the most important element for encouraging data curiosity among a workforce. With a data-curious, empowered workforce, a business can easily use their data to make decisions and drive insight.