How to Allocate Data Analysts in Small Teams
How do you map your analysts to the right work in a resource-constrained environment? This is a great question that I recently received from Patrick Mahaffey , a current reader and long-ago colleague:
As a medium-sized business [...], I would love to hear more about how to make the most of data analytics when you have, say, three or four analysts doing all the work for the organization. You still want analytics to support all the same functions (Marketing, Product, Sales, Finance, etc.) and are trying to garner sophisticated insights - you just can't support a large team. Any ideas for best practices there?
Why yes, I have opinions about this! In a simplified view, there are three main ways to manage this situation, assuming your analysts report to a single leader who oversees the data function for your business (for more on data team structures, see my earlier article here). You can allocate work to your small, centralized data team following any of these three approaches:
1. Shared Resources (aka Round-Robin)
In this model, the data team operates as a “service desk,” with analysts picking up tasks based on an ad-hoc request queue. Each sprint, an analyst completes a chunk of work, with limited preference regarding the request's origin. This approach is somewhat common in small teams.?
One of the main benefits is that it allows analysts to gain exposure to various business areas, building their skills through cross-training. Also, it provides flexibility and resilience against temporary or permanent shifts in staffing. However, there are significant challenges. Not every analyst can handle every request due to varying expertise levels. The lack of specialization among analysts can limit the depth of the data team’s integration with the business. In turn, this could shortcut their potential to contribute in a meaningful way.
2. Dedicated Resources (aka White Glove)
In this model, analysts are assigned to specific business stakeholder groups. For instance, one analyst in your centralized data team might be solely dedicated to marketing requests, while another analyst will only pick up work from the product org. I have seen this commonly occur when there is a resourcing model where certain business leaders will “pay for” a dedicated analyst on the centralized data team using their own headcount, with the expectation that the analyst will only do work associated with that business area.
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This setup has several advantages. Stakeholders tend to be highly satisfied if they get direct support, plus the analysts get to develop deep analytical expertise in their assigned areas. However, there are drawbacks. Some areas might not justify a full-time analyst, leading to resource gaps in areas that might otherwise be strategically important. I've seen limited success with attempts to allocate one analyst's time across multiple business areas. Also, dedicated analysts might become too narrowly focused, which can lead to job dissatisfaction and knowledge gaps if they leave.
3. Strategic Alignment
Typically used in more mature organizations, this model involves reallocating analysts from specific areas to focus on broader business priorities. For example, if customer retention is a strategic priority, a portion of analysts' hours would be dedicated to this goal.?
This approach requires intentional time-tracking to understand where analysts spend their time and make strategic allocations. It ensures that attention is distributed based on business needs rather than the loudest requests. This model allows for a balanced focus on strategic goals, foundational “run the business” work, and ad-hoc requests, but it requires meticulous planning and tracking.
Conclusion
In a resource-constrained environment, effectively managing analysts is key to maximizing their impact across all business areas. Each of the three operating models offers distinct pros and cons.?
By carefully considering these models, you can choose the best approach for your business to ensure that your data team provides the most value.
Simplifying and Automating Marketing Tag QA
7 个月The Shared Resources approach is great if there is someone ensuring tasks are strategically aligned and prioritized. This approach struggles if you don't have at least one seasoned person on the team. Then if you can structure it so the teams you support allow you to bill hours to their budgets you can justify growing your team.
Self Taught Data Analyst
7 个月Love the article! Have you had any success with the white glove approach with some sort of rotation element? So you get that deep knowledge across multiple domains
Managing Director at HSBC Innovation Banking
7 个月This is great. I remember picking your brain years ago on centralized v decentralized data teams and love how you've laid out the options here and in your other posts!
CEO @ Sunday Afternoons | Omni-Channel Marketing, Process Improvement
7 个月Thanks, June Dershewitz ! Insightful as always.
Founder, SaaS Pimp and Automation Expert, Intercontinental Speaker. Not a Data Analyst, not a Web Analyst, not a Web Developer, not a Front-end Developer, not a Back-end Developer.
7 个月What about the hub and spoke model, especially the rotating variant where a junior analyst rotates through departments, spends 50% of their time doing data analytics work and the rest work that aligns more with the team they sit in? The Hub and Spoke Model and self-serve analytics https://www.dhirubhai.net/pulse/hub-spoke-model-self-serve-analytics-alban-g%C3%A9r%C3%B4me?utm_source=share&utm_medium=member_android&utm_campaign=share_via