Co-Founder
As a company, we focus on helping brands make sense of their data. We build products to move and model data and offer consulting services to put this data to use. Perspectives shared here are from views formed over eight years of working with brands across countries, industries, and scale.
As someone who started his career as a business programmer analyst and now oversees a team of over 150 analysts, engineers, business intelligence developers, data scientists, and engineers, I have some views about how teams should be structured, the challenges faced, and the opportunities lost. I write this article from the perspective of an analyst working at a small company; these are my thoughts and may not apply to everyone. YMMV
A data resource titled “Analyst” is often the first hire at a brand growing fast enough that founders and executives no longer have time to put together their excel sheets and run macros to understand business performance. I would argue that a data resource should be on the team as soon as you achieve Product-Market fit, but that is a topic for another day.
However, If I place myself in the shoes of an analyst who is working as a team of one, I am often left wondering with the following thoughts on benefits vs. disadvantages.
You are a one-person team. If you are someone who thrives in unstructured environments, loves to bring structure, and is unfazed by the changing nature of work, then you will enjoy working here.
No one else in the company can likely do what you can, and therefore, a sense of accomplishment will be high.
You will probably look to automate reporting and BI if you have the programming skills to do the automation and if budgets are allocated to the automation initiative
I need to upskill myself by getting exposed to new technologies and getting an opportunity to work on new technologies. But unfortunately, the opportunities to do these at small companies are limited.
If I am compiling reports every week manually, I am falling behind.
I am tired of doing the grunt work and want to automate. I convinced my management that automation of data and reporting is the way to go, and they have approved and provided a budget for me to buy the tools I need to build the automation. The following thoughts cross my mind
I have completed one or two years of working as an analyst. But what is next? I am still a team of 1 or 2 or 3. If I am interested in running the entire data function at a large company, then am I getting the proper exposure by working here, or should I be looking elsewhere?
I have become a generalist but lack deep skills in any specific area. I want to become a specialist because that fits my personality. What opportunity do I have here if more investments are not being made in data technology? A good career path for generalists is to build and manage teams supported by resources with deep specialization in particular areas. I am a generalist, but I do not see a path to building and managing a team here. What should I do?
I have learned everything I could and want to expand my repertoire. I would like to either pursue a path specializing in a data function or become good at applying analytics to solve business problems. Either way, analysts become good when exposed to many situations and apply the learnings to solve a specific problem. So, what options do I have here?
I am getting offered data roles in companies that focus on data initiatives. Should I bounce?
Often these thoughts lead to a churn of the analyst at a brand. Of course, analysts cannot be faulted for deciding to leave because the business cannot justify a data team of more than one resource. However, it is natural for any ambitious analyst to pursue a path that gives them more exposure, money, and a better career path.
The problem for the brand and its founders is that they have no backup when their analyst leaves. Or if a backup exists, that is often someone from your executive team who now has to juggle the analyst’s role.
In a worst-case scenario, you bought the software needed to build your data stack and paid for the software upfront. Unfortunately, the project is nowhere close to completion, and now the entire investment seems like a sunk cost while not meeting the original objectives.
Therefore, if you are thinking about building a one-person data team, you need to think again about the best way to structure it.
At Saras, we offer a data team as a service for brands. We ask customers to define the problem statement and leave the execution details to us. The benefits for customers are the following –
If you have to hire just one resource, make sure that resource is someone who can help with decision support. Leave the development and management of the data warehouse or any automation of data initiatives to experts who can ensure your data is readily available when needed.
Get a free 14-day trial of Daton today, no credit card is required.
Company
Offerings
Policies
Follow Us
DISCLAIMER: Various trademarks are held by their respective owners
© 2023 sarasanalytics.com
Sitemap
Get a free 14-day trial of Daton today, no credit card is required.
Offerings
Policies
© 2024 sarasanalytics.com
