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Global Captive Centers – The true enablers of Collaboration (Part-1)

3AI November 24, 2023

Featured Article:

Author: Vinodh Ramachandran, Neiman Marcus Group

Through my experience working for large retail GCCs I. have observed that most retailers, in addition to having a central data & analytics team (typically within tech rolling up to the CIO), also have analytics teams within the businesses running independently and managing all their data, reporting and analytics requirements. The reasons for businesses to have their own analytics teams is typically agility and ability to make faster decisions. However, this structure unintentionally creates silos within the organization creating a few non-desirable outcomes: 

  1. Lack of single source of truth – How many times have you encountered different sales numbers from different data sources within the company? There are countless hours that analysts spend to reconcile numbers across different data sources 
  1. Lack of standardized tools – Typical within Business Intelligence. I have seen instances where there is an enterprise reporting solution (MicroStrategy/Cognos/PowerBI) while the business teams are very comfortable with an alternate tool (say Tableau) and start building dashboards for their own consumption. Lots of hours are spent later trying to rationalize metrics and reports across the organization 
  1. Career Pathing – Analytics professionals within the business teams are unsure of their career options within business and are looking to move to central data and analytics teams for growth. While talent migration does happen across teams, it is less common since most of the teams are unaware of talent pool available within the enterprise 
  1. Prioritization, Role Clarity & Duplication of efforts – Many a times I have observed that teams are working on the same problem, may be solving a different hypothesis. Wouldn’t it be powerful if all these teams come together and build a strong unified story? Lack of visibility to project pipeline and preconceived notions of who can do what is a big derailer for analytics success and maturity 
  1. FOMO (Fear Of Missing Out) – This hardly gets spoken about but is the biggest roadblock for collaboration. I have often seen teams compete with each other rather than collaborating to solve critical business problems 

There are many more challenges, but I can think of these top 5 outcomes of a siloed working model within large retailers.  

This is where a GCC (Global Capability Center) can truly make a difference. GCCs can be envisioned as a bridge that connects all these silos within the organization. Most of the GCCs that I have been part of have always started with a CoE (Center of Excellence) model and have evolved over time to ensure: 

  1. Business Alignment 
  1. Competency development 
  1. Driving collaboration across teams 

In my next blog, I will elaborate on how to structure your Data & Analytics CoE to drive all the above 3 outcomes. 

I would love to hear more on some challenges you have faced specifically in Data & Analytics teams. Comment/DM your thoughts. 

Follow me to hear more about my experiences and suggestions for scaling up GCCs, especially in retail 

#gcc #coe #retail #analytics #data&analytics #scaleup 

Disclaimer – All views expressed here are strictly personal and do not represent any company or team or individual 

According to NASSCOM, India is home to almost 1600 GCCs and this number is set to grow to 2000+ in the next 3 years. A lot of companies are looking at GCCs as a way to tap into the vast talent pool, scale faster and cheaper and transform they way they do business, either to stay relevant or leapfrog competition.  

As I close in on 2 decades of GCC experience, specifically in retail, I would like to share some of my observations in setting up GCCs. Watch out for a series of articles on a variety of GCC related topics. I wanted to start out with my point of view on scaling up a Data & Analytics CoE in the GCC. Give it a read and let me know what you think. 

Title picture: freepik.com

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