What kind of data person are you?
Data titles are notoriously confusing and inconsistent! Personally, I’ve taken to using the term “data person,” as opposed to data analyst, scientist, or engineer because by definition, the skill sets of data people are broad and we tend to put on different hats as needed. That said, data people come in different flavors, and you can usually map personas to a few key goals and the tools and workflows they prefer to spend time in.
The distinction between data engineers, and analysts, and scientists, and analytics engineers has been discussed endlessly, so we’re going to focus on a few personas you might see in the earliest days of building out your data capabilities.
At Preql, we played all of these roles and we have empathy for every persona. At the end of the day, everyone wants to get value out of their data and trust the reporting and analysis they produce. That said, building a reliable analytics stack is hard, and each of the personas faces key challenges.
We know sometimes data people position themselves as anti-spreadsheet, but there’s data work being done in spreadsheets today that is integral to business processes. Our slightly controversial belief is that data should always aim to empower the business, not create workflows that are misaligned with business users. That’s all to say, we have deep respect and affection for Spreadsheet People.
Typically organizations will hire business analysts within specific domains (Marketing, Finance, Ops, Revenue). This is often a response to manual reporting processes becoming so time consuming that they require a single owner. The role of this person is generally to present information to leadership in the form of weekly reports, presentations, and analyses.
A Spreadsheet Person is most confident in Google Sheets/Excel and has the business context and domain expertise to define metrics and present reporting. They often put in hours of manual work in Sheets because they are too strapped creating reporting to build out automated processes. Where they typically lack expertise is in modern data tooling – this person is often not a SQL expert and wants to manage data in systems where they have a higher level of comfort. In businesses that eventually build out central data functions, business analysts become key “customers” of a central org. They usually represent the demands of their executive and share those requirements with the data team.
Some Spreadsheet People become comfortable in BI tools like Looker, Tableau, or Sigma. Generally, the BI work they tackle is on the dashboard creation and editing side and they don’t interface directly with underlying data. When Analytics Engineers are brought in, they typically partner with Spreadsheet People on metric definitions and QA, because previously the source of truth was in static spreadsheets that the Spreadsheet Person maintained.
A highly motivated Spreadsheet Person will want to learn SQL, build automation, and research tooling that could make their lives easier.
Are you a Spreadsheet Person?
Then you’re precisely who we want to empower with Preql. You have all the context and business domain knowledge required, but are blocked from delivering value because you have to translate this knowledge into more specialized data functions.
With Preql, you can:
- Move business logic away from complex spreadsheet workflows into something scalable
- Provide transparency to your stakeholders about how metrics are defined
- Connect to multiple data sources in a single report
- Sync metrics directly to your spreadsheets so you can work in the environment you’re most comfortable in
Skills: Excel/Google Sheets, domain knowledge, dashboard building, metric definitions, handles demands of executive teams
Pain points: Unsustainable manual workflows for reporting, overwhelmed by requests from executives, can’t make their lives easier without learning brand new technical skills, uphill battle to build trust in reporting.
Possible titles: Business Analyst, Ops Lead, Marketing Analyst, Data Analyst
As the name suggests, Dashboarders love building dashboards. More specifically, they love that dashboards are a relatively quick way to deliver reporting and insights to business stakeholders. They are most concerned with the end product of BI work. Any data clean up they have to do in the process in a means to an end. They are okay with taking shortcuts, like creating a pdt in Looker (a SQL view that Looker executes for you so you can build LookML on top of it) if it helps them build something useful more quickly. A Dashboarder keeps a lot of context in their head, and doesn’t always have time to document their work, or track definitions in an accessible way. That’s because their role is often reactive and very sensitive to reporting needs from executives and business stakeholders. By the time a definition becomes outdated they’ve moved on to other urgent things.
Dashboarders tend to favor one BI tool or another, and their skill sets are aligned to the strengths / requirements of a specific tool. For example, a Tableau analyst would typically be much more interested in data visualization and tend to use their expertise to stretch Tableau’s functionality into highly custom dashboards.
Are you a Dashboarder?
Your business stakeholders value and depend on you, which means once you build them a report, they ask you for more. You are the keeper of institutional knowledge about what logic lives where, and any time the business changes a definition you have to update logic in many places.
With Preql, you can:
- Reduce the pain of maintaining complex business logic in your BI tool
- Integrate with your favorite BI tool so your dashboards are always using the most up-to-date definitions
- Help your stakeholders focus on business decisions vs whether or not the data is correct
Skills: BI tool of choice, SQL, going deep on a particular dataset to understand nuances
Pain points: keeping work up to date as business needs/upstream data changes, unrelenting demand for new dashboards, building trust in numbers, having trusted data they can work with, slow-loading dashboards
Possible titles: BI Analyst, BI Engineer, BI Developer, Data Lead, Data Analyst, BI Lead, Analytics Lead, Business Analyst
Data Team of One
Being the first data hire at an organization is an incredible learning experience and one of the more stressful jobs you can take on. You’re tasked with building trust in data systems, fighting for investments in infrastructure, all while you have to keep the lights on. Someone who has worked in a larger data org or been a data team of one before generally won’t choose to do it again, which means these brave folks are often doing many of these things for the first time.
The Data Team of One is motivated by showing results quickly, and is more proactive than a typical analyst in looking for tooling solutions for their problems. They might spin up a Fivetran/Snowflake trial without telling leadership then demo a use case to them to get buy-in.
This person knows enough to distinguish good data practices from bad but doesn’t always have the time or experience to build things in a more scalable way. They’re under a lot of pressure and fielding lots of requests, so the conflict between short term results and scalable work consumes them.
Are you a Data Team of One?
The inner struggle of a Data Team of One is that you can feel incredibly powerful and useful in this role. Everyone depends on you for answers, and being the lone person who can communicate with the database by writing code is impressive! Someone doing this job for the first time may be tempted to maintain the feeling of being irreplaceable. A more assured, forward-looking Data Team of One will do everything they can to automate the most painful process of this very hard job.
With Preql, you can:
- Get from zero to trustworthy, well-defined metrics in minutes, not months!
- Allow your stakeholders to access metrics you’ve vetted and approved without having to ping you
- Maintain business logic and definitions in a single location so you don’t have to constantly update your SQL
- Focus on analytics and strategy vs ad hoc queries
Skills: learning on the fly, can set up data tooling (Fivetran, Snowflake, Looker, Segment) for the first time, SQL
Pain points: everything is urgent, actively creating a mess of SQL even when you know you shouldn’t, fighting to get budget for tools
Possible titles: Data Engineer, Data Lead, BI Lead, Analytics Lead, Data Analyst
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