Embracing Business is the Essential Guide for Data Scientists
Embracing Business is the Essential Guide for Data Scientists
We can't avoid business to be successful as a data scientist
The Data Science world seems all about technical things: data exploration, statistics, programming, machine learning algorithms, and many other stuff.
But at their heart, they’re all about solving the business problem. We are using data science to help the business, so it’s essential to understand the business we are trying to solve.
I am personally a very technical person. Even after working full-time professionally as a data scientist for years, it’s still challenging to understand the business processes and communicate them effectively.
I have still learned some things through my experiences, which I want to share with all of you.
In this newsletter, we will explore how data scientists can bridge the gap between technical and business impact. So, let’s get into it.
Understanding the Business Context
The first thing we need to understand before proceeding with any data science project is the business context. Here, the context refers to the specific circumstances and objectives of the business we are working with, especially the problem we want to solve.
As data scientists, our first instinct might be to explore the data and develop the model as quickly as possible. However, the best model is the model that makes it into production—and the one that makes it into production is the one that could solve business problems.
There are many ways to understand the business context. Some tips to do, including:
Engage with Stakeholders: Regularly interact with business leaders, managers, and end-users to understand their pain points and objectives.
Business Goals Alignment: Ensure that your data projects align with the business's strategic goals. Understand the key performance indicators (KPIs) that matter to the business.
For example, you can spend time with the marketing team to understand their campaign goals and what success looks like for them before developing the machine learning model. This ensures that your model's predictions are relevant and actionable.
It might take time to understand the business, but it would be worth it in the end.
Translating Technical Jargon into Business Language
Data scientists might understand the technical terms we use to explore the data or develop the model, but most business people won’t. It’s not that they don’t want to understand it; it’s just not their expertise areas.
Given the discrepancies in the terms we use in business, it's essential to break down these technical concepts into simple, relatable terms.
By doing that, we can communicate better with the business users.
Here are some tips that you can do:
Simplify Complex Concepts: Avoid jargon when explaining models and results. Use analogies and simple language to convey complex ideas.
Focus on Impact: Highlight how your technical work will influence business outcomes. Use metrics that matter to the business, such as revenue increase, cost savings, or customer satisfaction improvements.
For example, instead of discussing "precision" and "recall" in a classification model, you could explain how well the model identifies potential high-value fraud customers versus how often it misses them.
By focusing on the impact, such as increased sales or reduced churn, you help stakeholders see the benefits of our work. Use visualization, analogies, and straightforward language to make the explanations accessible.
Visualizing Data Effectively
Related to translating technical jargon to business language, visualization could create a narration that helps businesses understand better.
Effective data visualization is not only about creating charts and graphs. It involves developing a story that guides the audience through the data. As the quote says, “one picture worth a thousand words”—If you can use visualization correctly, we could have a better data science project.
Here are some tips that you could do":
Use Business-Friendly Visuals: Choose visualization tools and techniques that are easy to understand. Use bar charts, line graphs, and dashboards that clearly convey the message.
Tell a Story: Create a narrative around your data. Explain the "what," "why," and "how" of your findings. This will help non-technical stakeholders grasp the importance of the conclusion of our work.
For example, instead of showing a raw scatter plot of sales data, you could create a line graph that highlights trends over time and correlates these with marketing campaigns or seasonal effects.
The goal is to make the data tell a story that is easy to follow and understand, leading to informed decision-making.
Collaborating with Cross-Functional Teams
Love it or hate it. Data scientists do not work alone. We work together as a team to solve business problems. It doesn’t matter if it’s a startup or a big company (even a solopreneur might need a freelancer sometimes); we are working together with each other.
Collaboration is the key to achieving success in solving problems with data science projects. Cross-functional collaboration ensures that data science initiatives are useful in the realities of different departments.
To effectively perform and collaborate with others, you could:
Work Closely with Other Departments: Collaborate with marketing, sales, finance, and other teams to ensure your data insights are practical and actionable.
Feedback Loop: Establish a feedback loop to understand how your insights are being used and how they can be improved.
Please don’t neglect this relationship, as it could be useful for your data science project.
Conclusion
We have talk how business is important for data scientists for many reasons. To embrace the business as data scientists, we can do the following:
Understanding the Business Context
Translating Technical Jargon into Business Language
Visualizing Data Effectively
Collaborating with Cross-Functional Teams
I hope it helps!