/ How ad-hoc analytics answers critical business questions

How ad-hoc analytics answers critical business questions

Companies that are trying to compete in today’s markets can’t boost their revenue or win business unless they effectively use data. Data is essential, and businesses need to use it to make decisions and build strategies.

The best data-driven businesses use data to power their decision-making at every level. Every employee is empowered to use data, and everyone can provide valuable insight into company operations and strategic success.

This environment, where every employee can participate in data discussions and make data-driven decisions, is called a data culture. Businesses that have a data culture are much more successful than those without.

To be competitive, businesses have to make building a data culture a priority. But a data culture isn’t as easy as just buying a BI tool or building public dashboards. Change has to come from the ground up and involve every employee.

Businesses that are looking to build an effective data culture need to invest in ad-hoc analytics. Ad-hoc analytics gives businesses the tools they need to encourage data curiosity and data discovery among their employees.

Ad-hoc analytics help businesses answer critical, time-sensitive business questions in a way that’s agile, employee-driven, and effective. Businesses need to invest in ad-hoc analytics to succeed in today’s markets.

 
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What are ad-hoc analytics?

Ad-hoc analytics are reports, data visualizations, and analytics that are designed to answer a single business question at a specific moment in time. They’re not designed to be permanent—they’re a quick, temporary solution to a business question.

This is different from most BI-driven data analytics, which are intended to be persistent, generally accessible, and thoughtfully designed. In addition, ad-hoc analytics are usually employee-driven, instead of being built for a team or because of a department initiative.

Ad-hoc analytics can be built using regular dashboarding and analytical tools, but more often, they’re built using pre-existing BI content. With visualization editing, users can switch out metrics, compare different data sets, and visualize their data in new ways.

Businesses can use ad-hoc analytics to supplement their more regular, formal reporting and dashboarding channels. Dashboards and reports are very useful for monitoring operations and tracking their success, but they’re less effective for answering novel business questions.

This is where ad-hoc reporting comes in. An ad-hoc analysis or visualization can be built in a few minutes, and it bypasses approval channels or business pipelines. Instead of waiting for permission to build a new dashboard, employees can just do it.

This helps businesses get answers to critical business questions quickly. When a new business question pops up, it’s essential to get answers quickly without waiting for dashboard design or new reporting schemes.

Best of all, ad-hoc analytics allows every user to drill down into their data and perform this sort of work. If an employee comes up with a business question, they can answer it themselves without having to ask for a whole new dashboard or visualization.

Ad-hoc analytics are an essential part of an agile, proactive data strategy. Using them, businesses can answer their questions quickly without building out completely new reports or waiting for a new dashboard design.

 

How can ad-hoc analytics help my business?

Ad-hoc analytics seem unimportant to many businesses. Many organizations don’t make any sort of effort to prioritize them, and employees often lack the tools to make ad-hoc analytics really work for them.

This ends up becoming a self-fulfilling prophecy, where businesses don’t devote any resources towards ensuring ad-hoc analytics success, employees can’t use their ad-hoc analytical tools for insight, and business leaders point to the lack of insight as proof that they’re unimportant.

Ad-hoc analytics can be extremely helpful, but businesses need to invest time and resources into making sure their ad-hoc processes work correctly. Otherwise, they’re not going to get any benefit out of the strategy.

 
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Ad-hoc analytical strategies can lead to success in a few key ways.

They empower the average employee

To build an effective data culture, businesses need to imbue their operations with data from the ground up. Data insights can’t come from just a few people; they need to come from everywhere in an organization.

This is only possible if the average employee can perform data analytics, ask and answer their own business questions, and find their own unique insights.

Ad-hoc analytics makes bottom-up data operations possible. It gives employees the tools they need to perform their own analyses, so that they can participate in data conversations and collaborate on a level playing field.

They streamline and speed up reporting

When your business situation changes, you can’t waste time building out completely new reports and visualizations from scratch. To stay competitive, you need to be able to react and adapt to market changes as quickly as possible.

It’s even worse if you don’t have the ability to build out new reports and visualizations. Many businesses still rely on canned, static reports and baked-in visualizations to monitor and analyze their data.

The longer you have to wait for an answer to your business questions, the more money you stand to lose as the market turns against you. Businesses need the ability to react to changes and access data analytics in an agile way.

Ad-hoc analytics is the solution that businesses are looking for. With ad-hoc analytics, your employees can build out analytics, attempting to answer your business questions within minutes. This way, you can react to changes almost immediately.

They encourage data curiosity

Once an employee has been empowered to design their own analytics, reports, and visualizations, they’ll get more comfortable doing so more and more often.

Eventually, employees get tired of seeing basic answers to basic questions on their public dashboards and start asking their own questions and finding their own answers with ad-hoc analytics.

Public dashboards can act as a good starting point or as common ground, but the real benefits of data analytics come when every employee can build their own content to answer their own questions.

When employees think up their own data questions and look for answers in a self-guided way, that’s called ‘data curiosity.’ Data curiosity is an extremely hard and valuable thing to encourage among employees, and ad-hoc analytics makes it possible.

 
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They answer critical business questions

When problems happen, it’s very unlikely that businesses have pre-existing dashboards and visualizations that can help to solve that problem. This is especially true when it’s a completely new, unforeseen problem.

In systems without ad-hoc reporting, businesses have to solve these problems blindly. They can’t build out new content without dedicating a large amount of time and resources to it, which leaves them helpless until they can get their new content online.

This is even worse for businesses that rely exclusively on baked-in visualizations and canned reports. There, they can’t even build out new content. They have to solve their problems with the analytics they have; often, that means they can’t solve them at all.

Ad-hoc analytics is the answer to this issue. With ad-hoc analytics, users can edit their current analytics to add new information, see new perspectives, and make new comparisons.

 

Ad-hoc analytics—ensuring business success

Ad-hoc analytics are essential for empowering employees and building a data culture. By allowing employees to edit and reconfigure the content they can access, businesses can encourage data curiosity and find completely novel insights.

The real power of ad-hoc analytics, though, comes from their ability to quickly and easily answer new business questions. By remixing and re-analyzing their data, businesses can solve their most critical business problems in an agile, data-driven way.

Check out some related resources:

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