Users managing their own reports: the rise of ad hoc reporting

What is ad hoc reporting anyway?

Ad hoc reporting is any business report or data analysis curated and created by users, as and when they need it.

Ad hoc reporting in business intelligence is in complete contrast with the managed reports seen in the early days of business analytics, which relied on templates distributed by IT departments.

The BI world is evolving

Self-service analytics and actionable intelligence infused into internal workflows are empowering more workers every day, presenting them with the insights from analyzed data that they need where, when, and how they need it. 

That may be the greatest strength and the unexpected drawback of modern analytics — the metrics a business already understands tell them about what has been important in the past, not what may be important now or in the future. When using a business intelligence (BI) tool, it’s natural to gravitate toward what was previously important because of factors like upfront modeling requirements and the ease of looking at existing analytics compared to the effort required to create new metrics. 

How can organizations deal with rapidly evolving situations in a timely manner and use analytics to ask the most relevant questions and tackle bigger challenges that could drive their next evolution? The answer is ad hoc reporting. The right BI platform empowers users with ad hoc reporting tools and removes the overhead of incorporating data into truly new insights, allowing far more freedom in asking the critical questions, without the effort trap of relying on existing analysis.

Whichever version of ad hoc reporting your company needs (one-off questions or big, game-changing analyses), the right BI platform will help you do it better and get the results into the hands of the people who need them. 

Enhance infused analytics with ad hoc reporting

Flexibility is an essential part of driving evolution at any company. When choosing a BI provider, make sure it offers genuine ad hoc reporting, and not just parameterized insights that you can’t alter or customize.

For example, a marketing team might rely on a recurring bit of infused intelligence, produced on a regular basis, telling you how many leads were created in the past week.

This is useful but may open up more questions for a savvy marketing manager than it answers. They might want to know, for instance, where these leads actually came from, how many were converted into sales opportunities, or what the demographic makeup was. To determine that, the user needs to be able to run or adapt a new query, not rely on something decided in advance.

Additionally, the marketing leadership org may task a data expert to pull in data from a wide array of locations and apply machine learning and other advanced analytics techniques to it in order to uncover strategic insights. In this case, ad hoc analysis (meaning the ability to ask deeper questions and present the answers where, when, and how they make sense for whomever needs them) has the ability to answer big questions and evolve the business. The right BI and analytics platform will empower users to dig deeper and take action based on the results.

GitLab uses Python and R to go deep into data

Cloud data storage has become increasingly inexpensive. Every organization, new and old, has some kind of cloud data warehouse or other solution, storing massive amounts of information every day. Turning that information into intelligence and opportunity isn’t easy. 

The data team at DevOps life cycle tool GitLab was charged with unlocking deeper insights from its data stores to answer critical business questions. 

“What was frustrating with our previous solution was that it was a process to get reporting done,” said Taylor Murphy, Staff Data Engineer. “With the architecture we had set up, we knew what the tables looked like, we knew the SQL query we needed to write, so we wanted a tool that would allow us to write the SQL and visualize it.”

And for many organizations, SQL and some self-service tools might be enough to get what they want out of their data. But as data stores become bigger, companies rely on more data sources to draw insights from, and the questions they ask become more complex, organizations are reaching for more robust tools: 

“With Sisense, we had the additional ability to move into Python or R to do something more complex,” Taylor said. “That opened up so many possibilities that everyone on the team was super excited about.”

Sisense’s impact was twofold, allowing the data team to ask bigger questions and to share those results widely, demonstrating value so its efforts could truly drive change. According to Taylor: “Sisense has multiplied the effect of the data org beyond the data team.”

The future of ad hoc reporting: Faster results, deeper discoveries

The right analytics and BI platform will infuse actionable intelligence for daily tasks into workflows, empower nontechnical teams to answer new questions as they come up, and allow data teams to go deep to bring back groundbreaking insights.

When choosing a BI platform, it’s not “either/or,” it’s both: Empowering both nontechnical users and data teams to ask questions on their own, in their own ways, is important. The most robust platforms give users tools to take data analysis to the next level, ask questions that can truly evolve the business, and put that actionable intelligence into the right hands, with ease.

Author: Scott Castle

Source: Sisense