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.

 

Challenges of data analysts

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:

Ill-defined role

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.

Value misperception

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.

Most time spent on fire-fighting and low-value-added tasks

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.

 

Status quo

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:

State I — The data wrangler

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.

State II — The dashboard builder

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.

State III — Analyzer

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

 

The future data analyst — business advisor

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:

• Partnership — best-in-class teams have their data analysts embedded into business functions and partner very closely with business stakeholders.
• Proactivity — data analysts share proactive business recommendations by spending significant time on diagnostic analytics instead of just showing/describing what happened. They come for daily/weekly/monthly business reviews with causes as to why key metrics are changing and potential recommendations.
• Domain/business expertise — understanding of the business and domain (e.g., product) to connect the dots and drive actionable insights

 

What it takes

This transformation needs to be enabled by augmentation, coupled with a culture and people shift. These are the main requirements:

• Augmentation — this is the main enabler behind this shift. Delivering actionable insights at the speed of business requires augmentation of traditional BI workflows to eliminate the speed — comprehensiveness trade-off (see more here). Moreover, to become trusted advisors, analysts should not be spending hours slicing and dicing on dashboards. Instead, they should focus on finding real insights, by leveraging ML to run statistical tests and pointing out business teams where to look, and closely partner with them. This is the only way to conquer the last mile of analytics. derive insights, communicate them properly and drive action.
• Business and domain expertise — data analysts need a good understanding of the business and domain expertise they’re working on. Thus it’s getting more common for analysts to specialize in one business function (e.g., marketing analyst) and be embedded into the corresponding function (e.g., marketing team) (Read more about the importance of domain expertise in this article by Randy Au)
• Soft Skills — to become trusted advisors, data analysts need to go beyond technical skills like SQL language. Business acumen, communication, listening skills, curiosity, empathy, and storytelling are critical to becoming strategic partners, developing domain expertise, and driving recommendations and action
• Data culture — the right data culture where business stakeholders see data as a key asset and are open to close collaboration with data analysts is a key requirement. Driving this culture requires data teams to gain the trust of their counterparts, by focusing on business value.

 

How to close the gap

This evolution requires a mindset, skills, and cultural shift enabled by the right processes and tools. Here are a couple of specific best practices:

Tools — augmented analytics as an enabler

• Augment existing workflows so that analysts get granular insights at the speed of business and have enough time to work on the last mile of the analytics marathon.

People — develop expertise to make analysts trusted advisors

• Embed your analysts into business functions
• Promote learnings exchange among different analysts (also cross-functional)
• Develop best practices for technical and non-technical matters (e.g., how to conduct root-cause analysis, how to present insights)

Culture — evolve your data culture to maximize value

• Promote a culture focused on business impact — develop a framework around measuring the value, promote success stories, etc
• Ensure that analysts develop domain & business expertise by providing context, creating alignment

Processes-support the whole transformation

• Develop a set of processes to facilitate knowledge transfer between business and data teams (e.g., data jams sessions, business deep dives)
• Set up weekly or monthly business reviews performance and involve your analysts

 

Elevate your data analysts to maximize business impact

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).