Today, data is no longer used for just operations and compliance; it is the driving force behind a company’s strategy and performance. To leverage data for improved business performance, employees must think like a “data person” – a data scientist or a decision scientist. But what exactly is a data person, and how can a person become one? A data person is someone who thinks critically, interprets data, and applies insights to drive decisions and results – whether in business, research, or everyday problem-solving. They don’t just collect or analyze data; they ask the right questions, challenge assumptions, transform data into actionable insights, and collaborate with relevant stakeholders to implement and improve business performance. In this regard, here is my 10-step process on how to become a data person.

1. Start with the Business Problem

A data person frames the business problem objectively, related to increasing revenue, improving customer satisfaction, reducing costs, mitigating risk, and more. This data scientist always makes sure that he or she clearly understands the problem or decision at stake in the value chain as per the organizational and business priorities.

2. Ask the Right Questions

It is often said that asking the right questions is winning half the battle. When it comes to analytics, this couldn’t be more true. Questions aligned to the goals and the organization’s culture are crucial for success. In this regard, in today’s LLM (large language model) world, the question is often associated with a prompt. While a question seeks a specific piece of information, a prompt is designed to guide a more structured or creative response.

One of the most powerful approaches to asking the right questions is based on the KPIs (key performance indicators). A KPI reflects the organization’s culture in its business operations. In general, KPIs not only measure performance – they reflect the organization’s values, priorities, and operational mindset. By carefully mapping business goals to questions and KPIs, companies can create a comprehensive hypothesis of the business environment and stakeholder needs. Overall, the following five questions will help in implementing a solid data analytics solution based on KPIs:

1.  Why do you want to know?
2.  How much do you want to know?
3.  What is the value of knowing and not knowing?
4.  Who owns the entity that is measured to realize the change?
5.  Do you have the quality data to derive the KPIs/insights?

3. Data-Driven Thinking

Good decisions are built on quality data. While intuition and Human-in-the-Loop (HITL) approaches can play a role, the true power of decision-making comes from leveraging high-quality insights powered by data. The data person always starts with a broad view – first, they scan the data landscape to uncover patterns, averages, correlations, outliers, data distribution, and other relevant trends. Then, they dive deeper and seek the story behind the numbers. What do the averages reveal? What do anomalies tell, especially concerning the mean and standard deviation? What does the data distribution mean? What variables drive the biggest impact? However, data isn’t always what it seems. The data person will challenge assumptions and identify biases, errors, missing pieces, and more.

4. Focus on the Decision, Not Just the Data

A good data scientist doesn’t just analyze data for the sake of analysis. The purpose of such analysis is to make better decisions in order to measure and improve business performance. A data person will always determine the critical factors or data variables (dependent, independent, control, and confounding) that are most influential in driving that decision. Are there trade-offs that need to be considered? The data person will focus on crafting actionable insights, which involves:

• Decisions related to measuring and improving performance.
• Consumption of business resources like money, time, and skills.
• Impact, i.e., performance improvement/workflows. Identify who will act on the insights and what action is expected.

5. Integrate Context and Assumptions

A data person will always strive to understand the context. Data is often biased. It isn’t neutral; it reflects the world in which it was created. Therefore, the data person will consider the context in which data was collected, the business environment, and any factors that play a role in making the data biased. Basically, every dataset is influenced by the people, IT systems, and assumptions that generate it, meaning bias can creep in at multiple stages. For example, if historical data has shown a trend, the data scientist will analyze the insight more and validate its relevance in the current situation.

6. Apply Analytical Models and Tools

A data person will choose the right analytical models and tools (e.g., R, Python, and Excel) based on the questions and the data type – nominal, ordinal, or numeric (NON) – to derive insights from data. They formulate the model before applying a variety of analytical techniques and tools, such as:

• Descriptive models, like correlation, to provide insights on historical performance.
• Predictive models, like regression, to forecast outcomes.
• Prescriptive models, like optimization and sensitivity analysis, to find the best solution and understand different scenarios.
• A/B testing or experiments to test hypotheses and guide decisions.

7. Incorporate Uncertainty and Risk

Every decision carries a set of alternatives, and every alternative typically comes with uncertainty. To make informed choices, it is crucial to recognize the risks inherent in each alternative and factor them in the analysis. A data person will apply techniques like sensitivity analysis to assess how much of the insights and decisions will shift when key variables change. This helps in understanding the factors that hold the most weight in the analytics model. The data person will also try to embrace Bayesian thinking for refining the decision-making process as new data surfaces. This approach allows one to update the models based on evolving evidence or data, making decisions more adaptable and resilient. Overall, the key to successful decision-making isn’t just selecting the optimal option or alternative – it’s understanding the risks, evaluating uncertainties, and adapting quickly as new insights emerge.

8. Communicate Clearly and Persuasively

Data and insights are often complex to comprehend due to volume, hidden patterns, biases, technical challenges, and context dependence, requiring clear communication and literacy. Hence, data and insights must be presented to decision-makers in a clear, actionable, and impactful manner and supported by a compelling story. A data person knows this very well. Based on the data types and the stakeholder’s data literacy levels, he or she will use data visualizations to simplify complex data and insights. A well-designed chart or graph can reveal insights in seconds that would otherwise take minutes to explain. The data person knows to contextualize insights so that the stakeholders know what actions are required to solve the problem at hand and improve business performance.

9. Optimize for the Long-Term, Not Just the Short-Term

A data or decision scientist thinks beyond the immediate insights and outcomes derived from data. He or she might bolster specific insights derived from analytics with broader and general trends, by summarizing vast data sets and generating new insights from AI/LLM tools such as ChatGPT and Gemini. This data person leverages intuition, analytics, and LLMs, the three main insight sources, concurrently with feedback loops, as they know that the three insight sources are not mutually exclusive or independent.

10. Collaborate and Be Cross-Functional

Data scientists actively collaborate with business leaders, product managers, marketers, data engineers, and analysts to unlock the true power of data and insights. The key to impactful insights lies in understanding diverse perspectives. By engaging with different teams, they ensure that data and insights are aligned with solving business problems that are both relevant and actionable. They value the power of cross-functional collaboration to solve complex problems and deliver tangible value to the business.

Thinking like a data person is essential for making smarter, more informed decisions in today’s fast-paced, data-driven world. Data is not just numbers; it is a powerful asset that, when analyzed correctly, drives growth, innovation, and competitive advantage. However, data alone is not enough. Success lies in asking the right questions, identifying patterns, recognizing biases, and applying insights strategically. Organizations and leaders who embrace data literacy, critical thinking, and analytical skills will be better equipped to navigate complexity, mitigate risks, and uncover opportunities. Ultimately, data-driven decision-making is a mindset – one that transforms uncertainty into clarity and action.