July 29, 2021

Agile Analytics Provides Answers for Daily Decision-Making

Julian Alvarado
Sr. Content Marketing Manager, Sigma
Agile Analytics Provides Answers for Daily Decision-Making

Actionable data that can guide business growth is being generated continually, day in and day out. It’s estimated that each person on the planet created 1.7 MB of data every second in 2020. But with so much data coming from so many different sources, it can be difficult for decision-makers to wrangle all the data their organization collects. In fact, just 24% of executives said they thought their organization was data-driven in 2020. Agile analytics can help.

In this article, we look at what agile analytics is and share five best practices to ensure your teams get the most out of your data. Finally, we wrap up with some examples of how agile analytics can help a variety of business teams use data to tackle challenging problems.

What is Agile Analytics?

Agile analytics is based on agile development methodology, which facilitates speed, adaptability, and collaboration. Agile analytics is designed to be flexible so that business teams can move quickly to explore data, find answers to pressing questions, and iterate on analyses to answer follow-up questions. With agile analytics frameworks, the emphasis is on the outcome, not a rigid process. It allows business teams to make use of extremely large data sets and streaming data to inform daily decision-making.

5 Agile Analytics Best Practices

Agile analytics takes advantage of the capabilities of the cloud and modern, cloud-native tools to empower business users to access and interpret data as needed — without having to wait on data experts or BI teams to generate reports for them. Here are five best practices for getting the most out of your agile data analytics initiative.

  • Connect to a cloud data platform Central to the purpose of agile data analytics is the ability to use data for everyday decisions, not just the big ones. However, to take advantage of relevant data, your team needs to be able to access it in a secure manner and have confidence in the data’s integrity. For this reason, your analytics tool should connect directly with your cloud data platform or warehouse to ensure security and compliance. It shouldn’t be necessary to move, store, cache, or copy data.
  • Provide an analytics tool designed for non-technical users Because your team members need to explore data on an ad hoc basis and find the answers to both high-level and follow-up questions, your analytics tool should accommodate non-technical users. Team members without SQL skills must have the ability to manipulate queries and formulas in an easy-to-use interface. Look for an analytics tool that allows business users to explore data in a familiar interface like a spreadsheet.
  • Be sure your tool allows users to explore dataAgile analytics frameworks are designed to make it easy for business teams to find answers to questions on the fly. Your teams need the ability to dive deeper into high-level reports, ask follow-up questions, and look at “what-if?” scenarios. Your analytics tool should allow access to the data driving dashboard visualizations and reports — either via a drill-down chart or through access to the underlying worksheet.
  • Facilitate collaboration and sharingInherent to the value of agile data analytics is collaboration. Domain experts each have a unique perspective and skill set that they bring to the table, and teams can take more effective action when they have input from everyone who has insight into a situation. For this reason, your analytics tool should make it easy to share, build upon, and repurpose colleagues’ analyses — all in a secure environment, without dealing with version control issues.
  • Make it easy to create visualizations Data visualizations effectively communicate the story that data is telling. In order to help audiences to understand insights, your team should be able to quickly and easily create attractive, meaningful visualizations. The process of presenting analysis findings to others is often when data becomes actionable. Colleagues begin to brainstorm, and ideas and solutions then start to flow.

Agile Analytics Use Cases

The agile analytics approach can help teams in any business to better understand prospects’ and customers’ needs, spot opportunities, and solve business problems more efficiently. Let’s consider how four different teams could benefit from using actionable agile analytics.


A marketing team might use agile analytics to evaluate the performance of various media channels mid-campaign and dial in messaging or adjust spend based on learnings.

Product Development

A product development team might evaluate how customers are engaging with an existing product to determine what features to prioritize in a new rollout.


A sales team might look at year-over-year sales by geography to help decide where to invest resources in the upcoming quarter.


A finance team might measure the financial impact of a new business strategy and make recommendations on iterating it in light of their findings.

Of course, all of these teams can bring their expertise to cross-functional decisions that need to be made. By working together, they can arrive at a strong plan of action for any given scenario. As a result of taking action based on data, rather than guesswork, teams become more effective, and the organization becomes more successful.

Agile Big Data Reporting and Analytics

Agile analytic frameworks empower teams with the ability to move quickly to find the information they need for effective decision-making. With agile analytics, companies can ensure they’re making the most of big data and enable their domain experts to conduct analyses on an as-needed basis.

With an agile data analytics tool like Sigma, domain experts are able to find the insights they need when they need them, and they can maintain compliance while doing so. As a result, your organization becomes more data-driven, increasing business growth.

We are Sigma.

Sigma is a cloud analytics platform that uses a familiar spreadsheet interface to give business users instant access to explore and get insights from their cloud data warehouse. It requires no code or special training to explore billions or rows, augment with new data, or perform “what if” analysis on all data in realtime.