It’s about time we elevate the data analyst role
Data analysts should become trusted advisors powered by data
Published on Tue 27 Dec, 22
NOTE: Most data & analytics roles are ill-defined. This article focuses on the data analyst role, which is often described as a BI analyst. Depending on the organization, there’s also a bit of overlap between the business analyst and the data scientist role.
The role of data analyst has rapidly evolved over the last few years. With the explosion of data complexity and business expectations, they face many challenges.
In the earlier years and in companies with lower analytical maturity, data analyst has been a generalist role. It has been responsible for almost the whole data and analytics marathon. Starting from preparation and visualization, and all the way to analysis and insight communication.
The growth and innovation in the data analytics space changed the meaning of being data-driven and the requirements for the data analyst role. This created the need for specialist roles and a strong emphasis on the last mile. This way data analysts are more focused on analysis, insights, and decision-making.
In line with this idea, Cassie Kozyrkov defines the key role of the data analysts as “looking up facts and producing inspiration for you” and puts a focus on the demanding nature/pace of the work by stating “The analytics game is all about optimizing inspiration-per-minute.”
I usually define this concept as speed to actionable insights.
Speed to actionable insights plays a key role in increasing the value of analytics and helping data deliver on its promise. Hence it should be defined as a north star metric for data analysts (Read more on accelerating speed to insights here.)
However, most companies are far from there, due to various challenges. Let’s look into what’s holding data analysts back.
Data complexity and business expectations have both risen. At the same time, traditional BI tools haven’t evolved much in the last 20 years, creating a gap in many data teams. Here are the main challenges faced by today’s data analysts:
There’s a general problem with role delineation in data & analytics. It’s not only specific to data analysts. Its role has a significant overlap with data scientists and BI analysts (and sometimes also business analysts).
If you look at most job descriptions, they will highlight that the mission of data analysts is to deliver insights. This is very ambiguous in itself — is the role to be a self-service enabler (i.e., answer to business requests and build dashboards) or to deliver recommendations? Organizations have different visions for the role of a data analyst, varying from self-service enabler (i.e., answer to business requests and build out dashboards) to trusted business advisor powered by data.
Data analysts are often seen as “second-class citizens” and feel left behind in their “technical” expertise by their Data Science counterparts. The data science role is associated with higher compensation and status, contributing to this gap.
Data analysts spend most of their time on low-value-added tasks, often in a very reactive approach (e.g., fixing broken dashboards). Many operate mainly as “dashboards/report factories” and are very disconnected from the business.
These challenges are holding data & analytics teams back, as analysts produce a lot of output, but very few business outcomes. In practice, most data analysts spend most of their time on low-value-added tasks, mainly focused on descriptive analytics, only presenting what happened.
Here’s a breakdown of the 3 most common data analyst states I’ve seen:
You can think about “the Data Wrangler” as a data analyst stuck in the initial generalist definition of the data analyst role. S/he is responsible for doing anything and everything data related ranging from pulling data from different resources, and reports, cleaning the data and preparing dashboards. Therefore they barely have time to focus on surfacing insights, and the value they deliver is diluted.
Outcome: Teams are stuck in the “what”, only able to identify what changed in the business performance and unable to answer “why”. The value of analytics is unclear causing a weak data culture.
While the “Dashboard builder” state is a bit more evolved than the “data wrangler”, these analysts are not focused on actionable insights. They spend most of their days on visualization, building, and managing dashboards. They look for insights reactively to answer the questions of the business teams or to put out fires.
Outcome: The value of the analyst remains uncovered. There is a significant bias in the decision-making process preventing the discovery of real insights, and the analyst teams are overwhelmed with ad hoc requests.
This is a mature state where the role of the analyst is better defined and the data culture of the company is already strong. Analysts’ focus is on delivering insights but they are unable to uncover them at the pace of business. They often also have a lot of non-analyst work to deal with. There is a need to augment analytics workflows to free them up and enable them to focus mostly on the last mile of analytics.
Outcome: Too much time spent on repetitive tasks (e.g., slicing and dicing on dashboards) hinders speed to actionable insight and business impact leading to missed opportunities
Working together with some of the most data-forward data companies, I witnessed the business impact that data analysts can deliver. These advancements are fundamentally changing how a data analyst functions within the company. They attend weekly business meetings to present what happened, why did it happen, and the so-what, sharing proactive solutions for the business.
A Data Analyst’s primary goal is to surf vast datasets quickly, liaise with business stakeholders, and surface potential insights. Speed is their highest virtue.
The result: the company gets a finger on its pulse and uncovers real insights at the speed of business. This generates the inspiration for decision-makers to select the most valuable quests for Data Scientists. I’ve identified three main characteristics that make data analysts trusted business advisors:
This transformation needs to be enabled by augmentation, coupled with a culture and people shift. These are the main requirements:
This evolution requires a mindset, skills, and cultural shift enabled by the right processes and tools. Here are a couple of specific best practices:
It’s about time that data & analytics teams conquer the last mile of the analytics marathon. Companies have invested significant resources in data collection, preparation, and visualization. Now it’s time to focus on the finish line — data analysis, insight communication, and better decision-making. Elevate your data analysts to conquer this last mile and deliver on the expectations (and ROI).
João Sousa is the Director of Growth at Kausa. Stay tuned for more posts on how to nail diagnostic analytics and increase the value of data.
Using machine learning, Kausa can analyze all your data, diagnose why metrics are changing, and provide actionable insights to improve business performance. With Kausa teams can save countless hours on data analysis and get ahead of competition by finding opportunities to unlock hidden value.