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Opinion

What Does A Data Analyst Do?

Robert Yi

What does a data analyst do? Data analysts analyze data to drive business value. This purpose is often pigeonholed into Supporting The Decision-Making Process, but this isn't quite the entire story. Analysts are experts at not only analyzing but, more importantly, studying, interpreting, and navigating the massive streams of data your company is likely ingesting. The best analysts are not transactional data APIs, taking in requests and returning data, but act as advisors and explorers in the overwhelming, high-opportunity, yet often deceptive world that is data.

Analyst work can be bucketed into one of three categories:

  1. Reporting & self-service: creating data products (dashboards or apps) that enable others to directly look at data.
  2. Ad hoc requests: reactively supporting business decisions and problems with data.
  3. Strategic initiatives: proactively finding opportunities in the data.

In this post, we'll discuss each of these responsibilities, what they entail, and what qualifies as excellence therein. Let's get started!

Reporting & self-service enablement

The first responsibility of the data analyst is reporting: building dashboards and reports that enable non-technical colleagues to keep track of key metrics. This is a table-stakes requirement, as it gives any business minimal visibility into how things are going. Towards this end, analysts are responsible for:

  • Defining metrics in concert with business stakeholders. Analysts ensure what's being measured actually represents what it is stakeholders want to measure.
  • Creating dashboards and reports that are up-to-date and useful. This involves writing SQL queries or doing some basic data modeling to procure data, then creating these dashboards/reports using tools like Hyperquery, Tableau, PowerBI, or Excel.
Sample dashboard made in hyperquery.
A sample dashboard built in hyperquery.

Beyond reporting basic metrics, this workflow inevitably evolves into the broader category of self-service enablement: building dashboards and data apps that enable non-technical users to explore and understand their data without analyst intervention.

What excellence looks like:

  • strong technical ability in SQL and a visualization tool
  • ability to define clear and precise metrics with respect to the business objective at hand.

At this point, analysts will have enabled anyone in the organization to explore data independently. That said, this is only the bare minimum of what a good analyst can and should do. Dashboards and reports expose data without interpretation, and expert analyst interpretation is often critical in making more refined decisions.

This brings us to the ad hoc request.

Ad hoc requests

Ad hoc requests are one-off questions that aren't answered by existing dashboards or data apps. This is where the "decision-making" vernacular often comes in -- it's here that data analysts can directly support business functions in the service of making decisions.

Responding to these requests does not mean reflexively, unthinkingly fielding stakeholder questions. Rather, it's about acting as the resident expert with respect to data and helping stakeholders get to the root of what they really want. This means learning to uncover the truly impactful business question that needs to get answered, then finding and shaping the data that best answers that question. Here's how a typical ad hoc request might play out:

  • The analyst gets an ad hoc request from a product designer: how many times was this button clicked?
  • The analyst wants more context around why this information is needed and what the designer will do with this data.
  • Designer responds: he needs this data because he wants to know if the button is too small or if it's positioned in a way that makes it hard for users to click.
  • The analyst explains that getting raw button click data without comparison would not actually answer the question the designer wants answered. The analyst offers alternative ways of answering this question (an A/B test).
Sample ad hoc analysis, done well, with validated (and documented) alignment and result summarization.
Sample analysis done in hyperquery. When ad hoc work is done well (validated and documented alignment, with clear result summarization), the work can visually look a lot like a strategic, proactive analysis.

Stakeholders will almost always pre-determine what data they believe they need, but it's often not quite right. It's the job of the analyst to circumvent that train of thought, uncover the truly impactful business question that needs to get answered, and only then find the data best fits as a solution.

What excellence looks like:

  • ability to reason clearly - to navigate discussions around how data will be used and uncover primary objectives
  • strong SQL skills - once the plan is agreed upon, be able to pull what's necessary with speed and precision
  • strong command of basic statistical reasoning to be able to communicate the risks/potential sources of error in their work
  • strong communication skills

Strategic analyses

The final responsibility of an analyst is to generate strategic recommendations based on proactive -- as opposed to reactive -- data analyses. Strong analysts should always be on the hunt for opportunities in the data that their business counterparts might otherwise miss. What this might look like:

  • Analyzing customer traffic to uncover patterns that can inform marketing strategy.
  • Analyzing purchasing habits of customers to determine which products are most likely to be successful based on user data.
  • Identifying new opportunities for cross-selling or upselling products based on the habits of customers who have bought similar items.
  • Analyzing sales data to uncover trends and patterns that can inform pricing strategy.

While ad hoc requests and dashboards are a necessary part of analytics work, strategic analytics work is where analysts shine. This is where analysts are best able to provide unique impact for a business.

What excellence looks like:

  • direct impact on bottom-line metrics
  • direct involvement in framing how data can help strategic discussions

Other responsibilities

Thus far, we've only listed out the responsibilities of analysts insofar as they relate to the analysis of data. There are certainly other responsibilities of data analysts, including but not limited to:

  • Data modeling: hardening ad hoc SQL queries into code within tools like dbt
  • Knowledge management: storing work in a centralized, shareable place so that it's discoverable by the rest of the team (and the rest of the organization beyond!) in tools like Hyperquery
  • Data collection: aiding engineers in defining events and their schemas, how they'll be logged, and where they'll live in your database.
  • Metrics hardening: defining key metrics and dimensions for use across an organization.

Depending on your particular company's setup, these tasks may be largely relegated to data engineers, analytics engineers, or other data professionals. But analysts should still be involved in these processes.

Conclusion

Analysts are the key to unlocking data as a competitive, strategic value-add. And analysts are responsible for three types of analysis work:

  1. Reporting & self-service work
  2. Ad hoc requests
  3. Strategic analyses

As we’ve discussed, truly excellent analysts are able to not only get the technical aspects of their work done, but more importantly, they’re able to adeptly translate technical findings into concrete business problems and solutions and vice versa. They’re able to not just translate requests into SQL, but study, interpret, and explore data in service of the business, wherever such needs arise.

Tweet @imrobertyi / @hyperquery to say hi.👋
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To learn more about hyperquery, visit hyperquery.ai.

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