Make Data-Driven Decisions with Agile Ad Hoc Analysis
Sr. Content Marketing Manager, Sigma
Guesswork makes an unreliable foundation for decision-making. Today’s companies know that they must be data-driven to compete, which makes ad hoc analysis essential. But actually achieving this goal is proving to be elusive — a survey of Fortune 1000 companies found that just 24% of respondents said they thought their organization was data-driven in 2020. A primary reason that making data-driven decisions is so challenging is that ad hoc analysis is bottlenecked. Too many organizations are dependent on their data teams for next-level insights beyond the dashboard. This post examines the significance of agile ad hoc analysis and how to democratize ad hoc reporting and analysis across your organization.
What is Ad Hoc Analysis?
Ad hoc analysis is a process in business intelligence that provides answers to specific day-to-day business questions. Business teams must operate with speed to capture opportunities and mitigate problems. They can’t wait for the information they need to make good decisions. Ad hoc analysis is designed to deliver insights when decision-makers need them, rather than requiring them to wait on scheduled reports or additional information from the BI team. Ad hoc reporting is typically based on what-if or exploratory analysis and may result in a data table, report, dashboard, and/or visualization, to communicate insights. It allows business teams to find answers to the questions they have without depending on the data team for deeper insights.
Static vs. Ad Hoc Reporting: What’s the Difference?
Unlike periodic static reports created by BI teams, which were the standard before the Big Data revolution, ad hoc analysis gives power to the business user. Decision-makers can access relevant data, conduct what-if analyses, and present their resulting insights in various formats on an as-needed basis. While static reports are a broad-brush look at the big picture, ad hoc analytics offer deep dive into the data to find a specific answer to a specific question for day-to-day decision-making.
Static reporting vs. ad hoc reporting
Ad hoc analysis tools enable business users to explore data at a level not possible with static reports. Here’s how the two formats differ:
- Offers surface-level reporting on what is already known.
- Reports out on a limited, predefined set of business metrics.
- Leaves data consumers without the ability to ask follow-up questions without returning to the data and bI team.
ad hoc report
- Provide dynamic, in-context analyses that surface real-time, highly relevant insights.
- Allows anyone to dig directly into data — often at the most granular level — to make critical decisions on-demand.
- Empowers non-technical business experts to go beyond the dashboard to independently find answers to their most pressing questions.
Ad Hoc Data Analysis Examples
What does ad hoc data analysis look like in the real world? Here are a few examples.
Ad Hoc Sales Analysis — A sales team may analyze churn rate to uncover why churn increased in a particular quarter and create a report to guide a strategic response.
Ad Hoc Marketing Analysis — A marketing team may want to view conversion rate data alongside marketing content. An ad hoc analysis with an embedded dashboard can make this information more accessible to team members.
Ad Hoc Financial Analysis — A finance team may examine year-over-year same-store sales for each region to help determine where to invest resources.
3 Steps to Effective Ad Hoc Analysis
With the right self-service analytics tool, conducting agile ad hoc analysis is simple. These three basic steps will deliver the insights your team needs for better decision-making.
CONNECT A CENTRALIZED REPOSITORY OF DATA
To take advantage of the data relevant to a given decision, your team needs access to it. All your data sources should be available via your BI tool, ideally through a cloud data platform or warehouse that ensures data governance. Your tool should connect directly with your data platform or warehouse for security and compliance reasons rather than moving, storing, caching, or copying data.
PROVIDE A BI TOOL DESIGNED FOR NON-TECHNICAL USERS
The value of ad hoc analysis is tied to the ability to explore the data and find the answers to both high-level and follow-up questions. You’ll want to be sure that team members can access relevant, quality data and that they can manipulate queries and formulas in an easy-to-use interface (without requiring technical coding skills). Provide your team with a BI tool that allows them to explore data in a familiar interface like a spreadsheet.
CREATE REPORTS, DASHBOARDS, AND VISUALIZATIONS
Presenting your findings to team members is where data becomes actionable. As team members brainstorm over what they see in a report, dashboard, or visualization, ideas and solutions begin to flow. Ad hoc analysis may require anything from a simple pie chart or a multidimensional network diagram. Focus on keeping your communication simple and easy to digest.
Is Your BI Tool Up Capable of Agile Ad Hoc Analysis?
Many BI tools aren’t built for true self-service analytics. Barriers to use will result in fewer people gaining data-driven insights. Look for the following capabilities in a BI tool to ensure your team can conduct the ad hoc analyses they need.
Ability to Drill Down
The definition of ad hoc analysis includes the ability to dive deeper into high-level reports and ask follow-up questions. Decision-makers need to understand the reasons why trends or problems are occurring. For this reason, you’ll want a BI tool that allows access to the data driving dashboard visualizations and reports — either via a drill-down chart or through access to the underlying worksheet.
Ability to Manipulate Queries and Formulas
New questions often arise from a given query or formula. Decision-makers should be able to easily conduct what-if analyses to look at the impact of different variables.
Ease of Use for Non-Technical Team Members
Non-technical users shouldn’t be required to learn SQL or proprietary code to do their own analysis. A tool that isn’t easy to use will mean that decision-makers will either try to download data and work with it in a spreadsheet (creating governance problems) or go back to the BI team every time they have a question (creating bottlenecks that lead to delays). With a tool like Sigma that allows users to work in a spreadsheet-based interface, ad hoc data analysis is simple and efficient.
Ability to Create Attractive Visualizations Easily
For insights to be valuable, they must be understood quickly. Data visualizations are invaluable for this purpose. Team members should be able to quickly and easily create attractive, meaningful visualizations with their ad hoc analyses.
Ability to Share and Collaborate
Taking advantage of opportunities and solving problems requires collaboration. For this reason, your BI tool should make it easy to share, build on, and repurpose colleagues’ analyses without worrying about version control.
Empower Decision-Making with Democratized Ad-Hoc Analysis
A true self-service BI tool that empowers your decision-makers to conduct their own ad hoc analyses will dramatically improve your company’s journey toward being data-driven. Domain experts can find the insights they need promptly, and you’ll be able to maintain governance at the same time.