From Cohort to Root Cause: 4 Ways To Own Your Analytics Workflow


From Cohort to Root Cause: 4 Ways To Own Your Analytics Workflow

It’s the final team sync before the big board meeting. You’re reviewing a dashboard your BI team put together when you notice a sudden drop in conversion from freemium users to paid customers. Everyone starts to panic and ask questions, so you decide to take charge.

You start by drilling directly into the data underpinning the KPI dashboard. Next, you pull in some additional sales and marketing data before using your masterful spreadsheet skills to quickly build out a cohort of freemium users from the last week.

You start taking questions from the audience (your colleagues) and doing rapid iterative analysis until you find the root cause of the problem right there in the meeting. The crowd (conference room) goes wild!

And then you wake up. What really happened?

The Reality of the Traditional Analytics Workflow

Unfortunately, you were unable to access the data underneath the KPI dashboard because you don’t know how to code. So you had to end the meeting early and “take it offline” to ask your BI team for help.

When they told you it would be at least 72 hours before they could get to your request, you decided to take matters into your own hands. You asked them to extract the data from their cloud data platform to a tool you and nearly 1 billion other people know how to use: the spreadsheet.


Even people with technical skills prefer spreadsheets! More than 50% of data experts still use Excel to analyze data, while 34% use Google Sheets.

But you soon discovered that much of the data had been aggregated to fit within Excel’s scale limitations. It was also missing data from a crucial source. Not to mention, it was already out-of-date the second you downloaded it.

You sent an emergency ping to your BI team requesting another extract, this time with all of the data and the missing source. Frustrated and exhausted, you accidentally fell asleep with your head on your desk waiting for your massive spreadsheet to load.

Now it’s almost midnight, and tomorrow you’ll have to tell your boss she’ll have to wait at least 3 days for your BI team to help you complete your analysis. It’s going to be a rough board meeting!

This is a prime example of how the traditional analytics workflow is holding teams back from making data-driven decisions at the speed and scale required by today’s fast-paced and highly competitive market.

The Traditional Analytics Workflow Holding Modern Teams Back

Fortunately, new cloud-native technologies are emerging that empower data-savvy spreadsheet gurus like you to take control and reimagine their analytics workflow. These solutions allow teams to harness their existing Excel skills to independently investigate billions of rows of live data, join additional data sources and drill into granular-level detail, and collaborate to find critical answers on demand — without having to depend on BI.

This guide explores 4 of the most common yet complex and iterative analyses done by cross-functional departments like marketing, finance, and sales. We’ll examine how leading teams are reimagining their analytics workflows and breaking free from traditional spreadsheets to do them more quickly, easily, and accurately than ever before.

#1: Root Cause Analysis

A root cause analysis is a method of problem solving used to identify the origin of business issues and find appropriate solutions to prevent them from occurring in the future. There are multiple methodologies and techniques to perform a root cause analysis to find the source of a problem or trend, but overall, it helps companies:

  • Improve organizational agility and resilience
  • Mitigate blockers to company growth
  • Minimize unnecessary costs
  • Increase operational efficiencies
  • Maximize profitability

How traditional spreadsheets are holding you back

  1. Excel crashes or slows down with data sets of 1 million rows or less, but modern root cause analysis requires teams to analyze up to 100s of billions of rows. As a result, data must be summarized or aggregated before extraction, which prevents teams from drilling in and seeing the level of granularity they need to explore and identify core issues.
  2. As teams ask consecutive questions and make new discoveries in the data, it’s common to identify additional data sources that need to be joined into the analysis. However, doing this in a spreadsheet is a highly manual and messy process — especially if this data is in a non-standard format, like JSON, which requires additional parsing and preparation by a data expert before it can be used in a traditional spreadsheet.
  3. Spreadsheets are static, siloed snapshots of data, which means they’re instantly outdated as soon as your BI team extracts them from their cloud data platform. Sharing them with colleagues creates governance, security and version control problems, and any future updates must be built from scratch using new (and instantly stale) data.

How root cause analysis should be done

A leading Fortune 500 foodservice distributor in the US has 300,000 restaurants and foodservice operators dependent on their deliveries to run their businesses and succeed, resulting in a multi-billion row dataset of service-level data. Employees use a Service Level Impact dashboard in Tableau to identify issues that must be addressed to ensure fulfillments are achieved and SLAs are met.

But they lacked the ability to drill down directly into the dashboard to get to the record level without BI team assistance, so employees had to request an extract of all the data related to each specific issue. Then they would rely on their spreadsheet skills to attempt to determine exactly what the problem was, why it had happened, and which action should be taken to resolve it.

This lack of timely access to data negatively impacted employees’ ability to fully understand and resolve issues, which led to missed SLAs, penalties, and ultimately, customer retention and acquisition challenges. Not to mention, the BI team was spending 20% of its time answering ad hoc questions and extracting data – data that needed to be continuously refreshed as issues evolved or requirements changed.

To solve this issue, the BI team recreated the Service Level Impact analysis in a cloud-native analytics solution and embedded a link to the analysis in the Tableau dashboard. Now 2000+ employees can click into the live, governed, multi-billion row dataset underpinning the dashboard to investigate the data and create pivot tables just as they had in Excel. Teams are able to quickly spot and address potential issues before they become serious problems.

Want to keep reading?

See more ways to own your analytics workflow, including campaign spend attribution, scenario modeling, and more!