Somak Sarkar
About Somak Sarkar
Somak Sarkar is an experienced analytics leader and data strategist with more than a decade of experience helping organizations turn complex information into practical insights and informed decisions. His background spans professional sports, elite athletics, health and wellness, and independent consulting, with a career centered on predictive modeling, performance optimization, and data-driven strategy. A significant part of Somak Sarkar’s career has been spent in the NBA, where he has worked with the New Orleans Pelicans, New York Knicks, and Minnesota Timberwolves. His experience includes developing analytics infrastructure, predictive models, automated data pipelines, reporting systems, and customized dashboards for coaches, front-office executives, scouting teams, and sports science professionals. His work has supported areas ranging from game preparation and player development to roster evaluation and strategic planning.
Beyond professional basketball, Somak Sarkar has applied his expertise to international athletic performance. He has worked with the Canadian Women’s National Soccer Team and Own the Podium, contributing data-driven approaches to training, competition preparation, resource allocation, and performance strategy. These experiences have allowed him to apply advanced analytics in environments where timely, accurate information can play an important role in decision-making.
Somak Sarkar has also served as an independent consultant, bringing his analytical perspective to organizations outside of professional sports. His consulting experience includes digital strategy, SEO, audience growth, website optimization, and data-informed planning within the health and wellness sector.
A graduate of Rice University, Somak Sarkar earned a degree in Mathematical Economic Analysis with a focus on Financial Computation and Modeling. His technical expertise includes Python, R, and SQL, as well as statistical modeling, automation, data visualization, and the development of scalable analytical systems. Throughout his career, Somak Sarkar has distinguished himself through his ability to connect technical analysis with real-world strategy. Rather than treating data as an end result, he focuses on making it understandable, useful, and actionable. His combination of analytical expertise, strategic thinking, and collaborative communication has enabled him to support organizations and leaders navigating complex, high-performance environments.
Why Communication Is an Essential Skill for Data Professionals
Data has become an important part of decision-making across nearly every industry. Organizations use analytics to understand customers, evaluate performance, identify opportunities, manage resources, anticipate challenges, and make more informed strategic choices. As access to information has expanded, so has the demand for professionals capable of transforming large amounts of data into useful insights. Technical expertise is naturally an important part of working in analytics. Data professionals may need to understand programming languages, statistical methods, databases, predictive modeling, visualization tools, and other specialized technologies.
Somak Sarkar understands that being able to conduct sophisticated analysis is only one part of the job. The value of an insight ultimately depends on whether other people can understand it and determine what to do with it. For that reason, communication has become one of the most important skills a data professional can develop. The strongest analysts do more than produce accurate numbers. They translate those numbers into information that people can understand, trust, and use.
Technical Expertise Is Only the Beginning
A highly sophisticated analysis may uncover an important trend, but that discovery has limited practical value if the people responsible for making decisions cannot understand its significance.
Consider an analyst who develops a predictive model identifying a potential change in customer behavior. The methodology behind the model might be statistically sound, but senior leadership may not need an extensive explanation of every calculation involved. Instead, decision-makers are likely to want answers to practical questions: What is changing? How confident are we in the finding? Why does it matter? What could happen next? What actions should we consider?
Answering those questions requires communication skills alongside analytical ability. Somak Sarkar explains that this does not mean eliminating technical detail altogether. Rather, data professionals need to determine which details are relevant to a particular audience. An engineering team may want to understand the technical structure of a model, while an executive may be more interested in its business implications. The same analysis can therefore require very different presentations depending on who is receiving it.
Understanding the Audience
Effective communication begins with understanding the audience. Data professionals frequently work with people who have varying levels of technical knowledge. Some colleagues may understand statistical terminology and programming concepts, while others may have little experience interpreting analytical models. A successful analyst adjusts accordingly. When communicating with technical colleagues, detailed discussions about methodology, assumptions, limitations, and modeling decisions may be appropriate. When presenting to non-technical stakeholders, those same concepts may need to be translated into straightforward language and practical examples.
Understanding an audience also means recognizing what matters to them. A finance leader may approach information differently from someone working in marketing, operations, product development, or human resources. Each department has its own priorities, responsibilities, and measures of success. Before presenting an analysis, it can therefore be useful to ask: What decision is this person trying to make? What information do they actually need? What questions are they likely to have? Starting with those considerations helps ensure that analytics supports the decision rather than simply adding more information.
Turning Data Into a Story
Storytelling is sometimes associated primarily with creative fields, but it also plays an important role in analytics. Data storytelling does not mean embellishing results or forcing numbers into a predetermined narrative. Instead, it means organizing information so that the audience can understand what happened, why it matters, and what the findings suggest. Raw numbers rarely provide that context on their own. Imagine that a company’s website traffic increased substantially over several months. Reporting the percentage increase communicates what happened, but deeper analysis might reveal much more. Perhaps a particular source accounted for most of the growth. Maybe visitors arriving through that source were also more likely to engage with certain pages or complete desired actions.
Somak Sarkar emphasizes that a useful presentation would connect these pieces rather than simply listing individual metrics. It could explain the original situation, identify the change, highlight the factors associated with that change, and discuss what those findings could mean for future strategy. That progression creates a coherent story from the data. Importantly, effective storytelling should also acknowledge uncertainty. If the data does not establish causation or if there are limitations to the analysis, those qualifications should be clearly communicated. A compelling presentation should never come at the expense of accuracy.
Making Data Visualizations More Useful
Charts, dashboards, and other visualizations can make complicated information much easier to understand. However, more visual information does not necessarily mean better communication.
A dashboard filled with dozens of metrics can overwhelm users, while an overly complicated chart may require so much explanation that it defeats the purpose of visualization. Effective data visualization is largely an exercise in prioritization.
The person creating the visualization should consider which information deserves the most attention. Labels should be clear, unnecessary elements should be minimized, and visualizations should make important patterns easier, not harder, to identify. Context is equally important. A number showing current performance may mean little without a comparison to previous periods, established targets, or other relevant benchmarks. Somak Sarkar explains that the goal should be to reduce the amount of effort required to understand the information. When users can quickly determine what a visualization is showing and why it matters, they are better positioned to use that information effectively.
Building Trust Through Clear Communication
Communication also influences whether stakeholders trust analytics. People may be reluctant to base important decisions on a model or recommendation they do not understand. This can become particularly challenging when analytics involves machine learning, predictive modeling, or other techniques that may feel inaccessible to non-specialists. Data professionals can help build confidence by explaining their work transparently. Somak Sarkar shares that this includes discussing where data came from, what assumptions were made, what a model can reasonably predict, and where limitations exist.
Communicating uncertainty is especially important. Analytics rarely provides absolute certainty about the future. A forecast is not a guarantee, and a statistical relationship does not necessarily demonstrate cause and effect. Presenting estimates as definitive conclusions can create unrealistic expectations and ultimately damage trust. Strong communicators are comfortable explaining what they know as well as what they do not know. In many cases, acknowledging uncertainty makes analytical recommendations more credible rather than less valuable.
Helping Stakeholders Become Confident Data Users
One of the most valuable things a data professional can do is help other people become more comfortable using data themselves. An effective analyst does not simply deliver a report and move on. They help stakeholders understand how to interpret the information, which metrics deserve attention, and how insights can be incorporated into everyday decision-making. Somak Sarkar understands that this can gradually improve data literacy across an organization.
When employees understand the information available to them, conversations can become more productive. Teams can ask better questions, evaluate ideas more objectively, and identify when additional analysis may be necessary. This also changes the analyst’s role. Instead of functioning primarily as a person who produces reports upon request, the data professional can become a strategic partner who helps teams define problems, evaluate possibilities, and make more informed choices.
Communication Is a Two-Way Skill
Communication in analytics is not limited to presenting findings. Listening is equally important.
Before beginning an analysis, data professionals need to understand the problem they are being asked to solve. Stakeholders may request a particular report or metric when the underlying question is actually something different.
Asking thoughtful questions can reveal the true objective. What decision will this analysis support? What problem are we trying to understand? What would make the information useful? Are there contextual factors that may not appear in the available data? These conversations can prevent analysts from spending significant time answering the wrong question. Collaboration also gives analysts access to valuable domain knowledge. Somak Sarkar explains that a technically unusual pattern may have a simple operational explanation that someone working directly in the area immediately recognizes. Combining analytical expertise with the practical experience of other stakeholders often produces stronger conclusions.
Combining Analytical Ability With Communication
The growing importance of data does not diminish the value of technical expertise. If anything, increasingly sophisticated analytical tools make strong technical foundations even more important. But technical ability and communication should not be viewed as competing skills. They complement each other. Technical expertise allows a professional to conduct reliable analysis. Communication allows that analysis to influence real-world decisions. For data professionals looking to advance their careers, developing communication skills can therefore be just as valuable as learning another programming language or analytical technique. Practicing presentations, writing concise summaries, improving visualizations, asking better questions, and learning to explain technical concepts in everyday language can all strengthen an analyst’s effectiveness.
Ultimately, successful analytics is not measured only by the complexity of a model or the size of a dataset. Its value is demonstrated by what people are able to do with the resulting information.
The best data professionals understand that their responsibility does not end when the analysis is complete. Somak Sarkar emphasizes that by presenting findings clearly, tailoring information to different audiences, telling accurate stories with data, listening to stakeholders, and communicating uncertainty responsibly, they help transform numbers into understanding, and understanding into action.