In my recent post, Data Analyst Primer, I elaborated on how demand for data analysts is growing and highlighted the steps you can take to get a foot in the door, and land that first entry-level job.
The purpose of today’s article is to offer a different perspective, a pessimistic view discussing why work as a data analyst can feel meaningless.
Often, expectations of a dream job deceive us. There are plenty of unpleasant responsibilities analysts have to carry on their backs, that separate what appears to be an ideal career, from the actual reality of the situation.
The Dunning-Kruger Effect for Data Analysts
Unskilled and unaware: Difficulties in recognizing one’s own incompetence lead to inflated self-assessments.

As with any shiny new skill or passion that excites you, it proves easy to feel confident about a topic by Googling and doing extensive research online. New analysts run into this problem where they have sky-high expectations of the value they can contribute within the organization.
This is but another instance of the Dunning-Kruger effect; Lack of work experience leads to overconfidence in one’s own ability to make an impact.
The Valley of Despair
After diving headfirst and gaining on-the-job experience, do they begin to understand it’s not all rainbows and sunshine. Countless obstacles arise; Endless data requests, lack of analytical rigor, non-technical managers, you name it.
New analysts fall into the valley of despair due to crushed expectations, unable to climb the slope of enlightenment. A feeling of dread starts to set in. And then you wonder whether this is the right career for you after all.

You’re not alone. Being an analyst can be a hard and unfulfilling job, often a junior role without much autonomy where you work for 1–2 years before moving on to something better.
No job is without its ups and downs, and being a data analyst is no different. There will be things you love, and things that make you wonder… Why am I here?
Organization Hierarchy
First things first, you have to understand your place in the world to get a handle of why these issues occur.
The root cause of unfulfilling work in technology is often, a case of misaligned career goals. Join the wrong team that doesn’t provide the growth you desire, and you will feel your career stagnating, with the loss of purpose coming soon after.
Your responsibilities as an analyst will vary depending on, (a) whether the data team is centralized/decentralized , and (b) what competencies your team/manager prioritizes .

The segments are not mutually exclusive, so you may find yourself, for example, holding responsibilities of both an analytics engineer and an XFN support analyst. Choosing a position that is a bad fit for your career aspirations and skill development, means extra pain in your time at the company.
Cross-Functional (XFN) Support Analysts – Centralized Support Staff
Centralized data teams hire analysts to act as centralized resources. Analysts work cross-functionally across functional departments or product teams as support staff.
This is by far the most common hire in companies looking to invest in data analytics. Not surprising given that it is the most cost-effective option, especially if the data team has yet to prove its value at discovering insights for the Business.
The idea is to hire one or more analysts to take requests from multiple teams simultaneously, illustrated below:

Centralized data teams have full control and governance over their analysts, but distribute them to other teams within the organization. Therefore, these analysts report directly to an analytics manager, and at the same time, have dotted reporting lines to members of other teams they support.
Some companies rotate their analysts between teams so work doesn’t get stale, while bigger companies with more analyst resources will offer less mobility between business functions.
Competencies prioritized comprise soft skills such as Influence, Storytelling, Communication, and Data Visualization.
Technical skills such as statistical knowledge and programming are important, in so far that they help to achieve specific business goals, for example, finding levers to pull to increase revenue by xx%.
Analysts who Excel as XFN Support Analysts:
Have a blend of business and technical skills but not sure where to take your career? This is the perfect role for you to find out. XFN support analysts get to satiate their curiosity analyzing data for various teams.
Crunching data for business leaders and soaking up their knowledge of a specific domain, is a good stepping stone for you to figure out which areas of the business you want to specialize in.
On the flipside, you may decide after exploring you prefer to develop your technical skills instead. Because you report directly to an analytics manager, there is opportunity to obtain exposure to develop technical skills and become a senior Data Analyst, mentoring other juniors.
In terms of flexibility, this role is the best bet for your career. It allows the freedom of exploration without pigeonholing yourself into a certain career track too soon.
Scenarios that Cause XFN Support Analysts to Lose Sense of Purpose:
- Weak domain knowledge. Analyst intuition about a problem helps one find the signal in a messy sea of data. However, chances are your intuition is likely less developed than functional leaders and any recommendations or insights will be questioned and rejected. The analyst ultimately ends up answering questions posed by the business user. And the analyst ends up becoming a reporting monkey (See point 2).
- Reporting Monkey. Reporting monkeys respond to data requests, write queries, and build dashboards for less technical business users. They hold zero ownership over KPIs and how parts of the business are run, and simply change SQL or programming logic to answer the questions that another department has thrown at them, on-demand.
- Lack of analytical rigor. Businesses prioritize moving fast and taking action. As a result, you will have to inevitably, simplify statistical concepts for business users to understand and accept your analysis. The more complex the analysis, even if perfectly sound and scientifically accurate, will likely get tossed aside as an archived recommendation, which can be painful to accept for some.
- Non-Technical Stakeholders. When business leaders demand dashboards to be refreshed, or a number is incorrect because of a change in business logic, analysts have to fight fires, manually force refreshes that were automated, and serve as IT support, completely at the whim of the business leader. The exception is product managers who are often more adept with technology and understand the limitations of batch and real-time data pipelines.
- Technical skill stagnation. XFN support analysts who want to grow in technical leadership care about doing things efficiently with technology. Examples include writing performant SQL, version control, and working with various tools and databases. As a young analyst, you may or may not get the chance to do so if you Work for an analytics manager that has no regard for your technical skill development.
Functional Analysts – Working Directly Under Business Leaders
Data analysts not hired on a centralized data team report directly to functional department heads. Functional department heads or product managers have free reign to hire their own analysts and assume full responsibility for their development, visualized below:

If you find yourself wanting to develop technical skills, and work with new tools and technologies often, LEAVE. IMMEDIATELY. An analyst in a role like this who has the desire to grow their technical skills will experience all the pain of XFN support analysts, and more.
Technical expertise is not emphasized in these roles, rather the motto is to move fast and find insights that create the biggest wins for the business.
Competencies prioritized in roles like these are soft skills similar to XFN support analysts, but technical knowledge can take a back seat.
As a result, the technical skill bar for hiring is lower than that of an XFN support analyst. Functional analysts are expected to be subject matter experts in the respective business function or product team that has hired them.
Analysts who Excel as Functional Analysts:
Functional analysts are experts in their domain. They hail from a variety of backgrounds, and have honed their intuition in their domain of choice, knowing where to look for insight in a messy sea of data.
If you are skilled in the art of persuasion and simplifying complex problems using common structured frameworks (80/20 Principle, Four Quadrants, Formulas, Venn Diagrams), you will feel right at home in this role.
Functional analysts are more akin to artists than scientists. They have a knack for predicting what their respective managers will find interesting in the data, and will take action on. They can be very persuasive, making complicated concepts digestible to the everyday business leader.
They also have a set of skills specific to their domain that makes them more effective at getting up to speed and driving business impact. For example, marketing analysts would have a niche understanding of SEM, the Google Analytics platform, and knowledge of the common KPIs tracked by marketing teams.
Scenarios that Cause Functional Analysts to Lose Sense of Purpose:
For the sake of not repeating myself, I’m going to assume that if you land this position, you are looking to hone your skills in thinking about business and have a sharp commercial mindset.
Again, if you have the ambition to grow your technical skills, leave immediately.
- Centralized Functional Analysts. To be cost-effective, organizations have the option to hire analysts for one business function (such as operations) and operate like a centralized data team described in the previous section. The functional analysts then take on the responsibility of supporting other business functions simultaneously. In these scenarios, functional analysts will experience points 1, 3, 4 that XFN support analysts experience.
- Analysis Paralysis. To functional analysts, data is not a way to fully explore a problem space, but simply a way to tell a story and check their intuition. This is not necessarily a bad thing. Due to time constraints, it is not practical to check every single column for insights and confounding effects. The problem arises when the analyst’s intuition veers so far away from their manager’s intuition such that the entire analysis needs to be revisited, leading to multiple rounds of rework. This can lead to analysis paralysis, especially if the analyst is doing research for long-term strategic planning.
Analytics Engineer – Centralized Technical Experts
Data analysts working on a centralized team focused on technical expertise come in when there is a need for a more controlled environment for doing data analytics.
Analytics engineers introduce tools, technologies, and code practices (Data Pipeline Testing, Table Naming Conventions, Code Reviews) that make other analysts more efficient, and their output trustworthy. These roles are few and far between, however, only recently emerging as a result of Fishtown Analytics’ revolutionary open source tool dbt.
Competencies prioritized include dbt, Gitlab, advanced SQL knowledge, Python, Airflow, Fivetran, Stitch, ETL.
If you find yourself bored in such a role, and want to work more closely with the business, you can move to become an analytics manager or going back to being an analyst supporting business functions.
Analysts who Excel as Analytics Engineers:
If you have a knack for seeing ways to make analyst workflows more efficient and care about concepts like automation, version control, and good documentation, you will excel in such a role.
Analytics engineers usually desire to grow into strong technical leaders and enjoy working with new innovative tools and technologies, without as much focus on the bottom line of the business.
Scenarios that Cause Analytics Engineers to Lose Sense of Purpose:
- Lack of business impact. The value of analytics engineers in an organization is that they make other analysts around them better and more efficient at their job. This can result in them getting so far removed from the business, that their work can lack purpose and meaning on business KPIs.
- No recognition from business stakeholders. Business leaders often don’t see the direct output of analytics engineers, as they are not the ones doing analysis deep dives into business problems. This can make it seem like analytics engineers don’t add any value to the company, where other analysts get all the credit, while analytics engineers work quietly in the background.
Data Analysts don’t have any autonomy within the organization.

There is one similarity that is common between all the roles described above, a lack of autonomy.
Data analysts rarely get to lead data-driven change, and are merely building analysis and dashboards that someone else decided was compulsory.
The most soul-sucking act a data analyst can commit, is building analysis and reporting completely at the whim of a higher authority.
Analysts are basically direct support staff to someone in a higher position of power. As a result, they have no decision-making authority or ownership over KPIs of the business functions they support, endlessly responding to user or managerial requests.
Furthermore, you have no say as to whether your analysis gets used or acted on. Researching answers to questions someone else has asked for simply to satisfy another person’s curiosity, is probably the most soulless act an analyst can commit.
This is the reason why analysts are usually hired in junior positions, requiring only 1–2 years of experience. It is easier to mold someone fresh out of school and groom them into your version of an ideal analyst.
Avoiding Scenarios That Cause Loss of Purpose

Despite being armed with this knowledge, we cannot avoid all the scenarios described above. What we can do is minimize nightmare scenarios by not taking on a role that is misaligned with our career growth expectations.
If you are not sure where to go, work cross-functionally under an experienced analytics leader that cares about your success.
Interested in a particular function of the business? Look for business leaders you respect and shadow them as a functional analyst.
Or if you simply love technology, then analytics engineering is the career path for you.
Before you join any organization, do some research, and ask key questions during the interview stage that give you insight into the managers and colleagues you will be working with/supporting, and the skills you can pick up along the way. Make sure they align with your career growth expectations.
- What skills are prioritized in this role?
- Which functional teams will I be working with / supporting?
- What does the data team structure look like?
- Who are my managers and stakeholders?
You will find that companies utilize a mix of the structures we have discussed when looking to implement a data strategy.
Know who you report to, the stakeholders you serve, and who you can learn from before saying yes to a company. If the company cannot provide the growth you desire, you will be miserable and the nightmares will never seem to end.
The Light at the End of the Tunnel
All hope is not lost, however. If you stick it out for a few years, you can move into a more senior position, where you have the responsibility to oversee other analysts, own entire parts of the data platform, or be in charge of specific business KPIs if you become a business manager/leader.
In these roles, there is room for you to champion your ideas and hold leverage in the organization to change things for the better, how you see fit.
After working for a few years, junior analysts come to terms with reality, learn to compromise, and just like Dunning-Kruger, can rise up from the valley of despair and stand on the beautiful plateau overlooking the horizon.
