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An important aspect of the business analyst’s job is to proactively gather requirements by asking the right questions and aligning stakeholders prior to implementation.  For world class business analysts, effective communication and project management skills are as important as the ability to query a database or create a dashboard.  

Picture this common scenario.  When business stakeholders want a report or dashboard, they often want it ASAP.  They begin by submitting a request, which may then get prioritized by a central analytics team, followed by a series of meetings with business analysts to define what needs to be built.  Business requesters may attempt to explain what they think they want, oftentimes sketching ideas in spreadsheets and pointing to dashboard examples that most closely resemble their envisioned output.  In this iterative process, business analysts are expected to understand the domain they serve (e.g. marketing), along with the underlying data and analytical capabilities that are available at the company.  In short, business analysts have to gather requirements effectively from both business stakeholders and from the data team.

One could boil the ocean when it comes to requirements.  Time and resource constraints are the only constant requirements to expect, so business analysts should work pragmatically by focusing on the highest impact considerations.  Here are the top 8 questions to pose when gathering requirements.  

  1. What is the business objective driving the request and the desired outcomes?  Clear understanding of business objectives will help determine what analytical output needs to be built.  Most objectives fall into these common business buckets: increase revenue, lower costs, mitigate risks.  
  2. Which KPIs and metrics matter for that objective?  Understanding the specific KPIs and metrics that the business requestor needs to track will enable business analysts to more efficiently identify the data sources, transformation, and analytical methods to use.
  3. Who is the intended audience?  An executive summary for a presentation to the board of directors is very different from an operational dashboard for a business function.  Knowing the intended audience will help business analysts tailor their analytical output to the audience’s needs and preferences.
  4. How do we expect the business logic to work?  Business analysts need to work with business stakeholders to define the calculations / “math” for metrics used in dashboards and reports, but also validate them with the data team.  This is where effective collaboration with data engineering is critically important.
  5. Where are the constraints, dependencies, assumptions, and risks?  A key part of expectation setting is to call out the intended use and what could go wrong – much like a prescription drug commercial.  Ask, align, and document considerations that will impact scope, schedule, resourcing, and the analytical outcome.  
  6. What is the data we need?  Data may be available, unavailable, and/or can be derived —  all for a single dashboard or report.  Business analysts need to understand how much time will be spent on enabling the necessary datasets, i.e. data wrangling.  Much of that work will be in the hands of data engineers, requiring business analysts to provide them with necessary and sufficient context and inputs.
  7. Where is the data coming from?  Business stakeholders often want to know where the data came from, as they often have strong intuition based on their direct experience with source systems.  It’s helpful to document the data lineage of metrics by mapping the transformations that the data is expected to go through from source to destination.
  8. Which data quality requirements do we need to prioritize?  “Garbage-in, garbage-out” is a truism, but in practice there are degrees of garbage.  It’s more common to have data quality issues than not.  For instance, problems with data quality might entail any combination of accuracy, completeness, validity, consistency, relevance, and timeliness.  Business analysts should set acceptance criteria upfront on data quality, so that clear expectations are in place for the types of analyses that can be supported.

Business analysts have an incredibly important role in data-driven organizations, where they act as translators between functional stakeholders and data teams.  In a way, their work is both an art and a science!  Enterprises that seek to level up their analytical capabilities should enable business analysts with the right tooling, ongoing education, proper analytics management, data governance, and recognition for their contributions.  

Spectio is an AI-enhanced workflow and collaboration product that enables more efficient BI workflows to drive better outcomes for platforms like Tableau, Power BI, Looker Studio, and others. It is purpose-built for business analysts and addresses daily bottlenecks they encounter in their jobs.

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