Unicorns not required: Building analytics teams for success
Learn the roles and skills needed to build a robust analytics team
By Laurie Rugemer and Charlotte Taft
When it comes to building an analytics team, the idea of the sparkly purple unicorn — the sparkly perfect person who can solve all your data problems — is an idea that comes up time and time again. This comes through if you take a journey through LinkedIn job postings for analytics roles. Wanted: accomplished professional capable of provisioning cloud data warehouses, fluent SQL, adept with machine learning, advanced Tableau skills, able to easily work with senior stakeholders — not asking for much!
If we think about all the things we are asking analytics teams to do, unicorn hunting begins to make sense. We see teams responsible for everything from design to performance to skills that used to be considered more purely IT-related.
Let the methodology drive the jobs you’re hiring for
Let the jobs that need to be done determine the makeup of your analytics team. Creating, maintaining, and scaling a robust analytics methodology can clarify what the jobs are and what roles have the skills to perform them.
By structuring analytics work around Align, Design, and Develop phases, this becomes clear. Let’s dive into each phase to see what jobs and roles are needed.
Phase 1: Align
The align phase of this three-step process aims to ensure that you don’t build solutions — for the purpose of this article, let’s assume dashboards — that are not what your end users need or want.
What happens during this phase
What do we need to know from our stakeholders in order to deliver what they desire in a way that delights and enables action?
- Identify stakeholders and users: Who are the intended users? What do they do and what does that tell us about what they need in a tool?
- Conduct user interviews: Understand users’ jobs and experiences — as well as their goals. Going beyond just their ask of a dashboard can help make sure the dashboard aligns with the larger goals of a team or group.
- Understand current tools or data sources: How are they interacting with data currently — what are their expectations and pain points? What limitations might exist or require consideration in creating training resources?
- Gather requirements and refine key business questions: Pin down the important who-what-where-why-how and translate this into business questions you can use to frame the Design and Develop phases.
- Document required data and use cases: Create artifacts — and planning documents — that can enable job sharing across the analytics process.
Being efficient and effective at the Align stage of this process is essential to ensuring that you don’t build dashboards that go unused — or that do more than is necessary and waste your bandwidth and resources.
What skills are needed?
In terms of the key skills needed to nail the Align phase, the most valuable incorporate a balance of interpersonal and technical know-how. Being able to roll things up and to document and relay findings in a usable way is critical. The most important skills include:
- Stakeholder relationship management
- User interview skills
- Persona mapping
- Facility with data
- Design thinking
- Problem-solving
- Business rules analysis
- Gap analysis
The outcome of this phase can include artifacts as in the below summary:
The roles or titles you’ll most likely see working on activities during the Align phase are business or data analyst, as well as product and project management-type toles, like product manager or product strategist.
Phase 2: Design
The second step in this process looks to pin down — and confirm — what the tool will look like and how it will deliver the required functionality. To do this, wireframing is driven by the findings documented in the Align phase — the priority business questions, metrics, and user considerations.
What happens during this phase
In the Design phase, responsibilities center around creating low-fidelity wireframes or prototypes to refine the user journey or experience. This step in the process also offers an opportunity to get feedback from users before committing more technical resources, and to get ahead of the data development steps by identifying how different views might need different types of data. In this phase you:
- Draw data and explore visualization options: Identify the links between the business questions and the right visualization types, as well as desired interactivity, from filters to tool tips.
- Create wireframes: Visual representations of the desired outcome make the conversations and lists from the Align phase tangible and can highlight questions that still need to be answered or inconsistencies.
- Gather wireframe feedback and sign-off: Seeing a wireframe spurs additional thought in stakeholders, and while scope creep needs to be managed, this is a key time to ensure their vision and needs align with what is set to be developed.
- Document data model requirements (level of granularity, fields and sources, joins and calculations required: An often-overlooked step prior to development, mapping the links between the visual components and the required — and available — data is crucial to efficient development and can help avoid issues down the line.
What skills are needed?
The skills required will change based on your users and needs. Depending on how much feedback and input stakeholders require, and how long teams have worked together and already know and understand expectations, the complexity or length of this stage will vary. The core skills include:
- Wireframing
- Data modeling
- Design thinking
- Journey mapping
- Workshop facilitation
- Brand identity
- Accessibility design
From a tool perspective, common platforms include wireframing solutions like Miro, Figma, or Lucidchart, while wireframing options like Balsamiq or Sketch are also popular. That said, anything from pen and paper to PowerPoint or Adobe work!
The types of roles or titles that can cover these tasks include Experience Designer, Visual Designer, and Data Visualization Practitioner.
Phase 3: Develop
Once we have approved wireframes and data mapping is completed, dashboard development can begin in earnest.
What happens during this phase
The jobs to be done include preparing data, connecting data sources to Tableau, developing dashboards with appropriate calculations and sound visualization choices, optimizing dashboard performance, and eventually demoing and explaining the dashboard to business users and stakeholders.
- Prepare data: Understand and prepare data: Where is it coming from? What is the structure? What is the quality of the data?
- Connect data sources to Tableau: Create a plan to bring in external data sources and link them together through relationships, in an ETL tool like Tableau Prep, or however makes the most sense given your dashboard design and data mapping
- Dashboard development: Build the dashboard in Tableau, both with appropriate metrics and calculations based on the dashboard data map, and with an eye on data storytelling and visualization best practices.
- Dashboard demos for business users: Showcase the dashboard for business users to get feedback and, ultimately, to help with user adoption.
What skills are needed?
The key skills and tool knowledge needed to be successful in this phase can include:
- Data visualization
- Data modeling
- Information design
- Design thinking
- Data blending
- Agile
- Data integration
- SQL programming
- Trend analysis
- Forecasting
In terms of technology, the tools stack you need will depend on your business, but typically include facility with data visualization tools like Tableau, Power BI, or Looker. Data preparation or engineering skills can be valuable and may range from using helper tools like Alteryx or Tableau Prep to programming with SQL. Some data visualization practitioners have specializations in mapping, such as Mapbox, ArcGIS, or Esri. In workplaces where statistics are important to the analytics deliverables, tools like SQL Server Integration Services (SSIS) may be beneficial, or programming languages like R or Python.
As this list lengthens, it is wise to consider how much of a unicorn you need. Is data engineering and data visualization required of the same resource? Is that something that can be shared between existing roles? As such, there are many roles that can cover the above tasks, but it is recommended that careful thought be put into your needs before attempting to put it all on one person.
The types of roles that can cover these tasks include data engineers, data architects, data visualization practitioners, and data analysts.
Get introspective: Review your own process
The three-step process we’ve outlined above aims to deliver an analytics solution. The steps reveal the skills and tasks that are key to each part. If we turn this around on the process of building an analytics team, we are left with a solid and iterative approach to your enhanced analytics lifecycle.
Align: Take inventory
Look inward at your current business state. Which tasks are most important to the analytics delivery process? How does the skill set of your current team — and current users — align with where you’d like it to be? Given these, compare what you’d like the next level in analytics maturation to look like with this current state. Which roles and responsibilities should be prioritized?
For example, if your report stakeholders are experienced and work easily, often requesting reports, and know how to communicate requirements, dedicated skill sets focused on the Align phase may not be a top priority. Or, if you find that there’s often a mismatch between what stakeholders request and what is delivered, there might be more of a need for Align and Design skills — or a need for more time to be spent on those steps — than at present.
Design: Establish and review your process
Evaluate how you are currently delivering analytics solutions, from query results to dashboards or embedded products. Are requests ad hoc, or is there a reviewable, developing analytics strategy and pipeline? How can the current — and desired — state be broken out roles-wise, and what changes could be made to team structure based on these and your Align findings.
Develop: Deliver iteratively
Are resources being deployed in a way that supports business strategy? What support does the development team need, in resources, training, or other areas? Are you able to report on the ROI of analytics deliverables in a way that can help ensure continued funding — and continuous improvement?
One size doesn’t fit all
Below is a diagram outlining how different roles fit into each phase — with intentional overlap to show that you don’t need a unicorn, but you do need to know what you need to build a successful analytics team based on a repeatable methodology.
We all know that with resourcing challenges, it often isn’t possible to hire every role we’ve outlined today. However, the key to starting this process and identifying the skills needed in every phase of the analytics process is to know what you DO need to create a sound, repeatable methodology that creates meaningful results for stakeholders.
The key is to use analytics methodology to understand the skills needed to be successful as an analytics team and evaluate where you have strengths and weaknesses, instead of relying on the elusive unicorn to do it all.
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