When driving data projects, you will encounter business stakeholder challenges that often go unspoken. This is not always because people hold back but because they don't fully know how to vocalize their constraints.

If they can't directly address their requirement, chances are we can't either. To hear others' speech, we start by asking questions from different perspectives.

4 PERSPECTIVES DRIVE EFFECTIVE DATA TRANSLATION

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1️⃣ DEFINING

Learning your stakeholders' business strategy provides some of the necessary context behind the challenge they are attempting to solve. From there, you can help scope, understand constraints, drive alignment with the organization's vision, and find potential overlap with other business unit's needs. Example Qs:

🔹What is your business strategy? How is this particular challenge aligned?

🔹What question/problem are you really trying to address?

2️⃣ UNDERSTANDING

Understanding how data will be consumed and by whom drives thinking for reporting, visualization, training, and change management needs. Example Qs:

🔹What are your current pain points?

🔹Will this [solution] enable more exploration or immediate decisions?

3️⃣ DESIGNING

Review the original ask; clarify the original goals. Verify the relevance between the business need and the granularity of the data to be used. Example Qs:

🔹What should this [solution] be and not be?

🔹What level of drill-down is required to address this business concern?

4️⃣ DATA

Anticipate data preparation time; understand the behavior of data being used. Contextualize data sources for end users within solutions, training, and change management resources. Ensure engineers and end users are familiar with the behavior of business data to increase development times. Determine whether the refresh needs are aligned with business needs and data team cost planning. Example Qs:

🔹How many different data sources are we dealing with?

🔹Will all the data be available on time? If not, identify risks, determine plan

While these questions are not exhaustive, they give a sense of the multiple perspectives data team leads must adopt to help translate business requirements into data requirements.

👉What sorts of data translation issues does your data team run into?