Big Data Drives the Need for Frequent Ad-Hoc Reporting
By: Stefanie Duncan
Tags: Business, Finance, Analytics, Data Science, Big Data

Big Data Drives the Need for Frequent Ad-Hoc Reporting

Businesses no longer have to go on gut instinct- they can use data and analytics to make faster decisions and more accurate forecasts.

Data, data, and more data.

According to McKinsey, “organizations now have troves of raw data combined with powerful and sophisticated analytics tools to gain insights that can improve operational performance and create new market opportunities. Most profoundly, their decisions no longer have to be made in the dark or based on gut instinct; they can be based on evidence, experiments, and more accurate forecasts.”

Businesses no longer have to go on gut instinct; they can use data and analytics to make faster decisions and more accurate forecasts supported by a mountain of evidence.

Analytics dexterity translates to an advantage within industry competition. Leaders that continuously make data-driven decisions stake out large returns.

Where does ad-hoc reporting come in?

According to TechTarget, ad-hoc reporting refers to a process that is “designed to answer a single, specific business question. Users may create a report that does not already exist or drill deeper into a static report to get details about accounts, transactions or records.”

Ad-hoc reports are created for one-time use. Today’s volumes of data and modern tools make it possible for employees to analyze data on an as-needed basis to answer specific business questions. Instead of waiting for scheduled reports, this allows business queries to be answered on-the-spot.

Sometimes specific business questions need to be answered quickly. With ad-hoc analysis, decision makers obtain insights more rapidly, allowing them to make decisions flexibly and with as accurate information as possible.

David Landers, a partner at Deloitte Tax LLP, explains how using ad-hoc reporting in internal tax departments helps users reply more quickly to internal reporting requests without relying on IT.

Ad-hoc vs. structured reporting

Ad-hoc analysis in business intelligence sits in stark contrast with the managed reports seen in the early days of business analytics, which relied on templates distributed by IT departments. Ad-hoc analysis lets users decide which data sources to pull from, as well as how they want to represent it.

Ad-hoc reports also differ from structured reports when it comes to:

Configuration

As the name suggests, structured reports are produced using a formalized reporting template. On the other hand, ad-hoc reports vary in format as they are audience-specific.

Quantity of data leveraged

Structured reports leverage large volumes of aggregated data. On the other hand, ad hoc reports are generated based on smaller amounts of data as needed, given that they address specific questions.

Sudden dip in production? Unexpected bottlenecks? Unexpected costs? With ad-hoc analyses you can look into, examine, and appropriately respond to sudden and unexpected events.

Too good to be true?

Ad-hoc reporting is not a magical cure-all. Looker reported on three quandaries that ad-hoc reporting alone often fails to address:

Incomplete data

Having some — but not all — of your data in one place is a limitation of ad hoc reporting. If you’re using siloed or extracted data, your reports could become stale and prevent you from seeing the full picture.

Lack of data governance

Data governance involves the people, processes, and technologies required to create consistent and proper handling of an organization’s data across the business. Ad hoc reporting may depart from company developed metrics, logic, or available data which may create insights that conflict with other reports.

Data availability

Everyone needs to be using the same underlying data. If the underlying data varies throughout the same organization, it can produce data chaos resulting in conflicting answers and delayed decisions.

Your best bet awaits

These issues can be averted by working with tools that already address them. To the best of my knowledge, the following self-service reporting tools can help:

Sisense claims to be empowering developers, data engineers, and business analysts to simplify complex data and transform it into powerful analytic apps. Sisense lets you easily transform big data from disparate sources into visual BI reports in a matter of minutes.

According to their site, DataRails is tailored for finance professionals and allows them to access and leverage financial and operational data independently, regardless of technical competency. This means the finance team can easily support planning, analysis and reporting needs on-the-go to drive faster and more effective decisions.

Dundas BI states that they “provide organizations with a single application for connecting, interacting, analyzing and visualizing any data, from virtually any data source, on any device.

Data is the Next Frontier for Competition.

More frequent reporting can accelerate optimal decision making, and on the spot reporting will pave the way for true business agility. Executives who are able to frequently and consistently get data-backed answers for even the smallest of questions with ad-hoc reports will see superior organizational decision-making.

Just make sure employees know what they’re doing.