Blog post by Shoubhik Sanyal, Head of Advanced Analytics and Business Enablement – Finance Analytics Open Hub, Mars Uk Plc 2023

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In a data world of infinite possibilities, change has the potential to make or break the future of an organisation, depending on how it is introduced. In this blog post, Shoubhik Sanyal, Head of Advanced Analytics and Business Enablement for Mars Inc shares his tried and tested framework for ushering in new D&A capabilities by building lasting trust among the business community.

It’s been some years since “the art of the possible” seeped as a concept into the business world. Originating in 19th-century politics, it is the idea that to successfully drive change, it’s better to focus on creating and implementing achievable goals – what we can, rather than pursuing an ideal – what we want.

As a leader in the field of data and analytics, I strongly identify with this idea. The enormous potential of data to revolutionise the way companies serve their customers and employees cannot be ignored. Nevertheless, realising this potential demands a practical strategy for gradually developing data-related skills and abilities.

But what does a pragmatic approach look like? How can we ensure that we’re pursuing the right opportunities with the resources we have today while simultaneously laying the foundations to unlock greater value in the future?

These are the questions I asked myself working between business stakeholders and my team of data engineers and data scientists. In addition, I wanted to find ways to optimise the ROI on data projects wherever possible.

The result is a framework that enables me to intentionally engage with and guide stakeholders through a series of steps to achieve the best value-driven outcome.

Framework Levers: Partnership, Process, and Tools

The framework pulls on three levers: partnership, process, and tools.

The foundational piece is a partnership: it establishes the level of collaboration and trust that will underpin making the right decision for the business at each step.

The key tools are familiar: Design Thinking, which enables a thorough investigation of a business and user problem while maintaining stakeholder engagement end-to-end.

The Agile process is an iterative approach to software development that emphasises flexibility, collaboration, and customer satisfaction. It involves breaking down a large project into smaller, more manageable tasks, and completing them in short development cycles known as sprints.


As the beneficiary of any digital tool developed by my team, it’s logical to make people the foundation of the framework, or more precisely, partnership.

“This true partnership model changes the dynamic from the traditional IT support relationship of “givers and solvers” to one that gives me a seat at the stakeholder table.”

When I talk about partnership, I’m referring to a relationship built on trust over time through collaboration, transparency, and user-centricity. I’ve discovered that this true partnership model opens doors that may otherwise remain closed.

Firstly, it changes the dynamic from the traditional IT support relationship of “givers and solvers” to one that gives me a seat at the stakeholder table. This is key to opening a two-way dialogue that leverages the expertise of both parties.

Secondly, it also enables me to provide honest feedback in the instance that a problem is not worth solving, or when solved, it will not deliver the insights they are looking for, without the decision jeopardising future collaborations. Being clear and transparent and ensuring that the justification is fully understood will not only build respect but often open new ideas on which to collaborate.

Process and Tools

The end-to-end process has two main phases and a varying number of steps according to dependencies. The two main phases are exploration and proof of concept.

Phase 1: Discovery 

Step 1. Stakeholder Identification and Problem Qualification

The first step in the process is to get under the skin of the problem to verify that it’s one worth solving. We start by identifying the data being requested and why stakeholders need it. Here it’s important to look at who else is asking for this data, not only for the sake of efficiency but also to maximise the eventual tool’s value by delivering it to as many users as possible. I do this by creating a stakeholder map of all those requesting the data and then holding an initial exploratory call with the group.

The aim of this call is to collaboratively identify the answers to the following questions:

What is the problem we are trying to solve?
What are the key outcomes?
Who is the target audience?
Why do we need a solve for this problem?
What is the business value (Short Term and Long Term)
How does this solution differ from something that might already be available?

If the call does not produce the required clarification, and there is a sufficient appetite among stakeholders to solve the problem, we will engage in a design thinking session. Otherwise, we proceed to the initial data validation step.

Should a design thinking session go ahead, we will continue to interrogate the problem as well as look at the value the solution will produce and the opportunities to scale. As design thinking enables you to probe deeper into the user experience, it’s also an opportunity to uncover possible barriers to deployment: for instance, will the user be able to apply the insights the tool will provide them? Is the user looking to export dashboards to excel? If so, they can be red flags that the solution isn’t worth pursuing.

Step 2. Initial Data Validation

The next step is to explore the available data. We spend no longer than a week looking at available data and share a small slice of the output based on the analysis.

In preparation for building a proof of concept, we will also review what existing data elements can be leveraged to avoid duplication of effort or reinventing the wheel.

I am a strong advocate for the re-use of data to save time and money, and champion the internal promotion of new data sets and elements created as part of the PoC (Proof of Concept) to ensure other data teams know they are available.

Phase 2: Building a Proof of Concept

Once the problem and data have been validated, the next phase is to build the proof of concept. Here it’s critical to start small as small as possible rather than going for a big-bang approach to avoid compromising your speed to market or an expensive upfront outlay.

Using the Agile framework, we iterate and share, usually focusing on three preliminary outcomes or insights until we reach a critical landmass that justifies a bigger iteration. The process means that we are in constant touch with our users, listening to them and incorporating their feedback so that the result is owned collaboratively.

As well as delivering the best outcome for the business, the combination of partnership, process, and tools, creates a compelling journey during which stakeholders learn, contribute and, ultimately, own the result by benefitting from the solution going forward. Not only does the framework achieve its aim of focusing resources on the right opportunities, but furthermore helps to build the critical “data culture” piece of the puzzle through high stakeholder involvement and moves us closer to fully unlocking the potential of our data right across the business.

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