Why Process-Driven Reporting is Essential for Accurate Insights

Everyone wants to report on everything.?

All.

At.?

Once.?

This enthusiasm for data-driven decision-making is awesome, after all, it keeps me in a job. It should be championed in every business - But here's the catch:

High-quality reporting depends on high-quality processes.

You can have a data engineer to ensure downstream stakeholders can access data on your business processes. An analytics engineer can model this data, preparing it for analysis, and a data analyst can generate reports, showing you a myriad of numbers. However, without clear, standardised processes, the insights derived from these reports can be misleading, and it’s the responsibility of data professionals to communicate this to stakeholders.

Let’s consider a typical scenario:

The sales team requests a report on their leads, focusing on lead conversions. They likely envision this as a straightforward process:

1. Ingest the data.
2. Report on the data.
3. Problem solved! Next!

Here’s what happens:

When the sales lead receives the report, they exclaim, “Oh wow! Lead conversions are up significantly this month, and the quality of our leads is improving. That tweak to our marketing strategy is clearly paying off!”

Can you spot the issue?

The sales lead is making an assumption about what it means for a lead to be “converted”. They presume that a lead conversion indicates an interested customer who has been accurately targeted by the marketing strategy.

Here’s what’s actually happening:

• Salesman 1 believes a lead is converted only when a customer agrees to a product demo.
• Salesman 2 thinks a lead is converted when a customer doesn’t hang up immediately after a cold call.
• Salesman 3 considers a lead converted as soon as they obtain a customer’s email address for follow-up.

Without a standardised definition of “lead conversion,” proper documentation, and a well-established business process, the insights drawn from reporting can quickly become unreliable.

So what actually happened?

In reality, the “improvement” in marketing effectiveness was a misinterpretation. Salesman 2 had a higher lead allocation that month, leading to an increase in what they considered “lead conversions.” The marketing strategy wasn’t performing better; the data was simply being misinterpreted.

This isn’t the fault of the sales team.?

If I were reading the report as a non-data person, I’d likely make the same conclusion. The insights derived from reporting shouldn’t require a deep understanding of data nuances. They should allow the reader to:

1. Scan the report.
2. Analyse the information.
3. Draw a straightforward conclusion.

The role of the data team is to enable non-technical stakeholders

So, how can the data team prevent these kinds of misunderstandings?

Let’s revise the initial three-step process to include key actions that ensure accuracy and clarity:

1. Understand the Task: Before diving into technical work, take the time to understand the purpose of the task. Why is this report needed? What insights do stakeholders hope to derive? How will this create ROI for the business? If you can’t explain why you’re doing it, you shouldn’t be doing it.
2. Ingest the Data: Bring the data into your data lake or data warehouse.
3. Understand the Business Process: Before jumping into modelling and reporting, ensure you fully understand the underlying business process. This may involve discussions with key stakeholders and reviewing any existing documentation. Doing this after ingesting the data can help quickly put a face to the name by looking at the data whilst coming to terms with what the process involves. In this case, talking with the sales lead would be a great start.
4. Investigate Data Deficiencies: Often, the data provided may be missing key fields, mutable, or have other deficiencies. Understanding the task and the business process makes identifying and addressing these issues easier.
5. Report on the Data: After making necessary adjustments to address data deficiencies, create the report. Ensure that the data is accurately represented and that any limitations are clearly communicated.

In the sales example above, a diligent data professional would have:

• Requested documentation on what constitutes a “lead conversion”, and;
• Highlighted the potential impact of varying definitions on the report’s insights.

Reporting is a powerful tool, but it’s only as strong as the underlying processes. By prioritising standardised definitions, clear communication, and a thorough understanding of business processes, data professionals can ensure that the insights they provide are accurate, reliable, and actionable.