Why Choose Analytics Consulting Over Pure Data Science?

Welcome back. In the last lesson, you built a short introduction that connects your data-science background to client decisions. Today we’ll develop the answer that introduction invites: why do you want analytics consulting instead of a primarily technical data-science role?

A convincing answer need not argue that consulting is “better” than data science. It should identify the work you want more of, show that you understand the trade-off, and connect that preference to something you have actually done. Allow about 40 minutes for the lesson, including the video, reading, and a spoken run-through.

The difference is where you want to concentrate your effort

Consider a company concerned about customer churn. In a technically focused data-science role, an important assignment might be to assemble reliable customer data, build a churn model, improve its performance, and monitor it over time. That work requires substantial judgment; it is not “just coding.”

An analytics consultant may do some of the same analysis, but must also help answer the client’s decision question: Which customers, if any, should we try to retain, through what intervention, and at what cost? A highly accurate churn score does not answer that question by itself. A customer might be likely to leave but unlikely to respond to an offer. Another might respond, but the offer could cost more than the additional margin it protects. The recommendation depends on the economics and on what the client can realistically do—not only on model performance.

That is the distinction to use in an interview: you want to remain technically credible while taking greater responsibility for framing the business question and communicating what the evidence means for a decision. It is a difference in emphasis, not an absolute division of labour. Industry data scientists can shape strategy, and analytics consultants often spend considerable time cleaning data or writing Python.

For a practitioner’s perspective on the overlap, watch these two excerpts from Everything You Need to Know about Data Science Consulting, an interview with Gleb Drobkov on Ken’s Nearest Neighbors Podcast. His experience concerns consulting data science, not OC&C specifically, so use it to understand the work rather than to infer OC&C’s exact day-to-day routine.

The attraction is not simply “more variety.” Variety can mean learning unfamiliar sectors quickly and working through ambiguous questions before the data is tidy. A stronger reason is that you want that combination of analytical work, commercial judgment, and explanation to decision-makers.

An insight has to survive contact with the business

The McKinsey article How to get the most from big data describes people who bridge analytical and business functions as “translators.” Its point is more demanding than converting technical terms into plain English: the functions need to work together to define valuable questions, understand the available data, and make results usable.

Apply that idea to your Predictify-churn project without claiming an outcome you have not measured. You can credibly say that building a predictor prompted a further question you want to work on: how should a business choose an intervention, assess its cost, and test whether it actually retains customers? You cannot yet say the project reduced churn or changed a company’s retention policy unless you have evidence that it did.

The diagram below offers a related reminder. It depicts a data-and-analytics strategy alongside an operating model, with capabilities and deficits connecting them. Gartner explicitly presents these as components, not steps: an attractive analytical idea must be considered alongside the organisation’s ability to use it.

There is an important boundary for your OC&C answer. The vacancy describes a strategy and M&A-focused firm, rather than one centred on multi-month IT transformation. You can say you want to make analytically grounded recommendations that account for a client’s capabilities. Do not imply that you expect this role to own every subsequent system build or operational rollout. Equally, do not suggest that a consultant’s job ends at producing a slide: a useful recommendation specifies what the client could do and what evidence would justify the decision.

Turn your preference into an interview answer

Your motivation answer can follow a compact line of reasoning:

1. Respect the technical work. Say what you enjoy about rigorous analysis; do not dismiss data-science roles.
2. Name the additional responsibility you want. Be specific about framing a commercial decision, assessing trade-offs, or explaining a recommendation.
3. Offer evidence and a direction. Use an experience that supports your analytical foundation, then distinguish what you have done from what you hope to do next.

Here is a possible spoken answer, separate from—and more focused than—last lesson’s personal introduction:

“I enjoy the technical side of data science. I’ve worked on data-driven intrusion-detection research and built projects including a churn predictor. But I’m especially interested in the question after the analytical result: what should someone do with it? A churn score, for example, is only useful to a business if it helps decide which customers to contact, what intervention is worth its cost, and how to tell whether it worked. Analytics consulting appeals to me because I want to help frame those questions, do rigorous analysis, and explain the trade-offs to the people making the decision. Negotiating sponsorships has also given me experience adapting a proposal for external stakeholders. I want to build on that combination of technical work and communication while developing stronger commercial judgment.”

Notice the limits of the claims. The answer does not say your churn project produced a client recommendation, that your research had measured commercial impact, or that sponsorship negotiation was analytics work. Each experience supports a different part of your motivation.

If the financial side feels more natural to discuss, your options-pricing and P&L dashboard offers another bridge. You might say that building it made you interested not just in calculating a result, but in which decision the result could inform and what risks or assumptions a decision-maker would need to see. Keep this as an interest unless you can substantiate an actual decision or measured impact.

Be ready for the distinction to be challenged

An interviewer may reasonably ask, “Couldn’t you do that in an industry data-science job?” The honest answer is yes. Some industry roles offer substantial ownership of business decisions. Your reason for choosing analytics consulting is therefore not that other roles lack impact; it is that this role is expressly built around tackling client problems, bringing analysis into strategic recommendations, and communicating those recommendations. You are choosing that particular mix of responsibilities.

They may also ask whether you still want to code. Your answer should be yes, if that is true for you. The technical foundation is part of what you would contribute. What you do not want is to judge the success of your work only by whether a model runs or a metric improves, without asking whether it helps resolve the underlying business problem.

Finally, distinguish this answer from the next interview question. “Why analytics consulting?” explains your choice of work. “Why OC&C?” must explain your choice of firm. Briefly mentioning the role’s strategy focus here is useful; reciting the entire vacancy would obscure your personal motivation.

For a spoken run-through, keep three signposts in mind: technical foundation; decision beyond the model; evidence for wanting that responsibility. Aim to sound like you are explaining a choice, not delivering a memorised comparison of job titles.

Key takeaways

Your strongest reason for pursuing analytics consulting is not that you want to leave technical work behind. It is that you want to use technical work as evidence for consequential client decisions, while developing the commercial judgment and communication those decisions require. Acknowledge the overlap with industry data science, make your preferred responsibilities specific, and keep claims about past impact verifiable.

Next, you’ll narrow this motivation from analytics consulting in general to why OC&C’s strategy focus, analytics practice, sectors, and working model fit your goals.