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Business Analyst?

Business Acumen is The Key to Your Science

By Narghiza ErgashovaPublished about a month ago Updated about 12 hours ago 8 min read
Narghiza Ergashova on Vocal Media / Data Science to Business Decisions

Imagine you've spent weeks preparing a detailed analysis. You've combed through mountains of data, meticulously verifying each step of your work. You present it, and... crickets. Your audience, likely non-technical stakeholders, don't seem to connect with it. What went wrong?

In my field of expertise, I often presented financial information to the Board and Senior Leadership Team. This required not only a deep understanding of the numbers but also effective communication skills to convey the data clearly. To me, the "unpacking" approach—breaking down complex information into more digestible parts—combined with working with the psyche of Board members to "build" their appetite for the data, worked best in engaging my audience. This method helped ensure that they fully understood and appreciated the financial information provided, facilitating better decision-making and strategic planning.

This means the answer lies in understanding your audience. If you work in a research-focused organization or are mostly presenting to technical stakeholders like engineers, an academic “white paper”-style analysis might be the way to go. But, if your audience is comprised of non-technical business teams or executives, focusing on key insights rather than technical details is crucial. Communication in the workplace isn't about what you find interesting—it's about what the audience needs to hear.

Narghiza Ergashova on Vocal Media

One well-known approach for this type of insights-led, top-down communication is the Pyramid Principle developed by McKinsey consultant Barbara Minto. This methodology helps you structure your communication so that you get straight to the point and then expand upon it as needed.

Although one has to stay open minded to the fact that there is no bullet proof solution that renders audiences at the disposal of your researched data. I have been to plenty of companies where stakeholders were "hell bent" on "trading out" of their insolvent positions and would take no advice towards more rational solution involving voluntary administration. In situations like this it is of course best to "pick your battles".

One notable example from my career involves a company called WPG Resources, which transitioned from exploration to mining. Shortly after my involvement, it became evident that while the long-time explorers had struck gold, they had never operated a full-scale mining production business. The company hit all three insolvency triggers, yet the Board, seemingly blindfolded, continued to issue misleading, aspirational statements to the Australian Stock Exchange (ASX). The most alarming aspect was that stakeholders believed in their vision, and no amount of data could convince them otherwise. Unsurprisingly, the business went bust a few months later.

But enough of outliers, lets re-zoom on mainstream organizations.

Strong Business Acumen Defines Great Data Scientists

Technical skills are a given for any Senior Data Scientist at a top-tier company. However, what sets great data scientists apart from good ones is strong business acumen. You won't stand out by incrementally improving your technical skillset; instead, you'll make your mark by ensuring your work drives maximum impact for your stakeholders—be they in Product, Engineering, or Business teams.

Why Business Acumen Matters

Understanding Priorities:

To maximize your impact, it’s essential to comprehensively understand the business priorities and the challenges your stakeholders face. This entails thorough research and ongoing engagement to stay attuned to their evolving needs. Access to decision-making stakeholders is crucial; cultivating strong relationships and maintaining open communication with these key individuals is vital for effective collaboration and achieving meaningful results.

Narghiza Ergashova on Vocal Media

Understanding Leadership Dynamic:

The effectiveness of your information hinges greatly on your grasp of the dynamics and personalities within the leadership team. Recognizing who among them is open to new ideas and enthusiastic about change is vital for gaining traction. Equally important is identifying those who favor a conservative approach and may resist change, allowing you to tailor your communication strategies more effectively. Building strong relationships and clearly demonstrating the value of your insights can further amplify your influence within the team.

Scoping Solutions:

You should be able to scope analytics solutions that directly address these priorities or problems, ensuring they align with your business goals. It's crucial to evaluate each solution carefully, considering factors such as scalability, ease of integration, and potential impact on your operations. Doing so will help you make informed decisions that drive meaningful improvements.

Narghiza Ergashova on Vocal Media

Effective Communication:

Communicate your insights and recommendations in a way that your audience understands and finds actionable. Tailor your message to their level of knowledge and use clear, concise language. Provide concrete examples or scenarios to illustrate your points, ensuring your audience can easily grasp and apply your advice to real-world situations.

With strong business acumen, you'll also have the context and judgment needed to sanity-check your work. You'll know whether the results of your analysis or your proposals make sense within the broader business context.

Developing Business Acumen

Business acumen isn't typically taught in schools or bootcamps, so how do you develop it? Here are a few concrete steps:

Engage in Cross-Team Meetings:

Pay attention during company All Hands and other cross-team meetings when strategic priorities are discussed. These sessions often provide crucial insights into the company's overall direction, upcoming projects, and how different teams are aligning their efforts to achieve shared goals.

Connect Your Work to Business Goals:

During planning cycles or when new projects arise, ask yourself, "How does this relate to the high-level business priorities?" Take the time to thoroughly evaluate the project's potential impact and alignment with the company's strategic goals. If you can't make the connection, discuss it with your manager to gain clarity and ensure that your efforts are contributing effectively to the organization's success.

Ask “So What?”:

A data point or insight only becomes relevant once you can articulate why anyone should care about it. What should they be doing differently based on this data?

The ultimate aim is to transition from merely taking requests and working on inbound JIRA tickets to being a thought partner for your stakeholders that shapes the analytics roadmap in partnership with them.

Be an Objective Truth Seeker

In many organizations, there's a tendency to cherry-pick data to fit a desired narrative. This practice can lead to biased conclusions and misinformed decisions. However, as a data scientist, you have the unique responsibility to counteract this tendency. By advocating for rigorous and unbiased analysis, you ensure that decisions rest on accurate and comprehensive data.

Data Science teams typically don't directly own business metrics and are, therefore, under less pressure to meet short-term goals. This relative independence allows data scientists to prioritize the integrity of their analyses over immediate business targets. Consequently, they play a crucial role in maintaining the credibility and reliability of data-driven insights within the organization.

Stakeholders might sometimes pressure you to find data that supports a narrative they've already crafted. This pressure can stem from a desire to ensure their initiatives or projects appear successful and well-founded. While conforming to these demands might earn short-term favor, what will truly benefit you in the long run is being a truth-seeker and upholding the narrative that the data genuinely supports. By maintaining integrity and fostering a culture of honesty, you build trust and credibility—priceless assets in any professional setting.

Combining Data with Primary Research

Data is a powerful tool, offering critical insights and guiding informed decisions. However, it often overlooks emerging signals that haven’t reached significant scale or are missed by structured data. These subtle trends and nascent patterns are key to understanding future movements and shifts. This is where primary research becomes invaluable, providing a nuanced and detailed perspective that enhances traditional data analysis. By engaging directly with the source, primary research uncovers hidden insights, enriching our overall understanding and enabling us to anticipate changes more effectively.

For instance, if you operate within a B2B SaaS company, data might reveal a decline in win rates for your enterprise deals. This trend can be alarming, particularly if these deals constitute a significant portion of your revenue. To fully grasp the situation, it’s crucial to consult with sales representatives for their insights, delve into their detailed deal notes, and possibly even reach out to prospects to understand their decision-making processes. Initially, this research may seem like a collection of random anecdotes and isolated incidents. However, over time, patterns will emerge that likely didn't surface in the standardized metrics you’re monitoring. These patterns can provide valuable qualitative data, enabling you to make more informed strategic decisions.

Skepticism is Key

If the data looks too good to be true, it usually is. When people see a steep uptick in a metric, they tend to get excited and attribute this to something they did, like a recent feature launch or a successful marketing campaign. This initial excitement can lead to overconfidence and possibly misguided decisions based on misleading data. Unfortunately, such changes are often due to data issues, such as errors in data collection or processing, or one-off effects like seasonal variations or random anomalies. It's crucial to thoroughly investigate and validate any unusual data trends before drawing conclusions or making strategic decisions.

For example:

  • Data might be incomplete for recent periods, and the metric will level out once all data points are in.
  • A one-time tailwind might be boosting the metric temporarily.

Having a healthy dose of skepticism, curiosity, and experience is crucial to avoid pitfalls and generate robust insights. Skepticism helps one question assumptions and avoid being easily misled, while curiosity drives continuous learning and exploration of new ideas. Experience, on the other hand, provides the practical knowledge that helps to navigate complex situations and make informed decisions. Together, these qualities form a solid foundation for critical thinking and effective problem-solving.

Be Open to Changing Your Mind

If you work with data, it’s natural to change your opinion regularly. For example:

You might recommend a course of action to an executive based on the current data and insights available, but then change your mind as new data comes in, requiring a reassessment of the situation and potentially a new strategy to achieve the desired objectives.

You might initially interpret a metric movement one way, thinking it indicates a positive trend, but further analysis and deeper insights often reveal a different story. This can uncover underlying issues or alternative explanations that weren't immediately apparent.

Many analysts hesitate to walk back on previous statements out of fear of seeming incompetent or angering stakeholders. This reluctance can stem from the pressure to maintain a consistent image or the anxiety of admitting a mistake. However, your job isn’t to stick to a prior recommendation out of fear of losing face. Leaders like Jeff Bezos understand the importance of changing your mind when confronted with new information, or simply when you’ve looked at an issue from a different angle. They recognize that the business landscape is constantly evolving, and that flexibility is crucial. Being able to clearly articulate why your recommendation changed is a sign of strength and intellectual rigor, not weakness. It demonstrates a commitment to accuracy and a willingness to adapt in the face of new evidence, ultimately leading to better decision-making and long-term success.

Wrapping Up

Great data scientists are defined not just by their technical prowess but by their ability to understand and solve business problems. By developing strong business acumen, being an objective truth seeker, combining data with primary research, maintaining skepticism, and being open to changing your mind, you can elevate your role from a good data scientist to a great one.

Truly Yours,

Narghiza Ergashova

Read more from Narghiza Ergashova here:

  • Narghiza Ergashova on The Ritz Herald / Redefining the hunt for the talent: smart contracting’s revolutionary role;
  • Narghiza Ergashova on Markets Insider / Redefining the hunt for the talent: smart contracting’s revolutionary role;
  • Narghiza Ergashova on www.thegolderitual.com.au
  • Narghiza Ergashova on www.Medium.com

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About the Creator

Narghiza Ergashova

Finance Executive.Strong negotiator focused on achieving win-win outcomes. Expert in building effective relationships with stakeholders. Personable, articulate, highly motivated individual with a focus on achieving business objectives.

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    Narghiza ErgashovaWritten by Narghiza Ergashova

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