Relevance of Data Science for Managers
By Rohit Sharma
Updated on Apr 02, 2025 | 8 min read | 6.6k views
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By Rohit Sharma
Updated on Apr 02, 2025 | 8 min read | 6.6k views
Share:
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Today, the world’s largest and most successful organizations use data-driven decision-making that impacts high-level business decisions. Leaders and managers are expected to be equipped with widespread and fundamental knowledge of data science and its techniques. Data science for managers encourages them to be better decision-makers and align with an organization’s growth mindset.
Data-driven managers are in huge demand owing to their particular skill set of applying complex data to business problems and solving them through applicable insights. But why are they preferred over traditional managers?
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Data has come to hold significant weight in business decision-making and problem-solving. Unfortunately, traditional managers tend to rely on intuition backed by unimaginative and short-sighted inputs from their team. Business decisions that arise out of such inputs can’t succeed in today’s economic environment, where one extra data point can tip the scales in favor of a competitor. Traditional managers lose sight of future growth opportunities because they are comfortable operating in a narrow spectrum. Often, this leads to biased problem-solving and a lack of initiative to scale up.
So, what is it that sets apart data driven management from a traditional one?
With data at their fingertips, managers can make decisions based on hard evidence and backed by their intuition. While intuition remains an important managerial trait, data science for managers helps transform gut instincts into actionable insights. By analyzing past performance metrics, managers can develop well-informed solutions to address business challenges.
For instance, a manager may think that gel-based dishwashing liquid is a new way of cleaning utensils for rural areas, and the audience will want to use something different. But data finds out that customers in rural areas are varied and don’t want to switch from dishwashing soap. So, the manager may have to change tactics based on in-depth insights from the data.
Data driven product management gives hard evidence about consumer sentiment and preferences. Data science for managers helps analyze customer feedback, market trends, and product performance to refine offerings.
Constant evaluation of product- or service-related data gives managers an upper hand over competitors. As a result, they can work faster and rethink business models quickly to satisfy customer needs and maintain brand loyalty.
Data science for managers enables a deep understanding of customer sentiment, buying behavior, demographics, and preferences. A data-driven manager doesn’t just rely on raw data from surveys, social media analytics, or Google Analytics; they apply data science models to extract relevant insights.
By leveraging data-driven strategies, managers can assess potential markets and determine profitability, ensuring the business focuses on the right audience with the right approach.
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Data-driven managers always have an eye on future opportunities that are beneficial for organizational growth. Through data science models, managers can track upcoming predictions and utilize this information to develop plans for these opportunities. Forward or future-based thinking helps businesses and managers achieve wins over their competitors in significant ways.
For instance, finance services use models to assess credit and fraud risk before lending to a customer to know if they will lose money in the future.
Managers are at the helm of understanding their business problems. To solve these problems, they must come up with actionable and meaningful insights. Data driven decision management provides these insights by deep diving into data. But unless a manager gives the right direction, data gathered will have no use. Managers are the ones who set goals and tell data scientists what exactly they should look for.
Data science has many applications that managers use to solve problems and fulfill objectives. Here are some.
Data science for product managers uses Deep Learning technologies to show what human vision would look like through computers. For instance, Deep Learning uses multiple instore cameras to monitor customer buying behavior when setting up a retail store. In turn, it will enable a manager to change product placement or improve store design. Deep Learning also has applications in solving cybersecurity problems.
Data science uses Machine Learning (ML) algorithms and models to solve various problems. For example, managers use ML to better customer interactions through customer service robots or assistants, streamline complex processes such as using ML-based models for documentation, and gain a competitive edge by improving operational and employee productivity.
Managers are leaders, but they’re not superheroes. No human can analyze vast amounts of data without the help of technology and advanced algorithms. Here’s where data science comes in. Predictive models employ Big Data to collect information, provide evidence-based solutions and upgrade decision-making processes. Human involvement with such models is necessary to guide technology in providing relevant results and maximize outcomes.
Recommendation engines use Artificial Intelligence (AI) and other data science technologies to offer suggestions for customers based on their past buying decisions. They also help discover new opportunities for growth by continuously learning from consumer patterns. A most prominent example would be Amazon which seems to know what a particular customer wants magically and suggests that accurately. Practical recommendations helped Amazon convert into sales and revenue as well as keep customers engaged with the business.
Data science project management technologies are used to enable automation in business processes. For example, AI and ML can aid the quick collating of information from various sources. Data science algorithms sort through vast amounts of data in a short period and come up with techniques to solve problems or improve existing processes. For instance, Google launched a people analytics initiative, Project Oxygen, which sorted through over 10,000 employee performance reports and identified common behavioral traits of excellent managers. They then launched special training programs to promote their growth and retain them.
The data scientists are considered the trusted advisors to the stakeholders. Through their expertise and skills, they are able to create an environment of sheer transparency which allows for better decision-making.
The data scientists use their skills to get into the analytics systems and ask the hard questions about the existing systems and figure out the ways to get out of them. Through a rigorous process, they can identify new opportunities.
It is very important to test how certain decisions affect the organisation. The data scientists measure the key metrics and figure out the strengths and weaknesses of the made decisions for future references as well.
The data scientists understand consumer behaviour, search patterns, etc. All of this information is useful in the identification of potential consumers.
Data Science for managers is very useful, and it becomes all the more critical because the managers are given the responsibility of managing a whole team and creating a vision for them which aligns with the company goals. Moreover, data analytics for managers helps them to use their skills for business growth and delve into the areas which help in achieving goals.
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Businesses today are increasingly employing data science to scale up growth. Having leaders aligned with this mindset is a huge plus. As an employee, being data-driven will help you climb up the leadership ladder faster. By providing innovative solutions to problems, you can become an invaluable asset.
Not just that, managers who use data science to make business decisions also earn higher salaries. Data analytics for product managers is in high demand, and any manager who has fundamental knowledge about it possesses a skillset only highly skilled personnel can replicate. Being data-driven also encourages constant learning, which further contributes to growth.
From scratch or owing to a shift, those who are setting out on a new career path have an excellent opportunity to upskill and hone data-driven decision-making. At upGrad, the Professional Certificate Program in Data Science for Business Decision Making aims to empower young and mid-level professionals alike to take up data-driven managerial roles. Through innovative curriculum, industry exposure, business case studies and projects, expert mentoring, and personalized feedback for interviews, this course aims to build professionals of tomorrow who can adapt and run businesses in a data-driven world.
In today’s fast-paced business environment, data science for managers is no longer optional—it’s essential. Data-driven managers make smarter decisions, improve products and services, understand their target audience better, and plan for the future with confidence. By leveraging data, they can identify new opportunities, test strategies effectively, and drive business growth.
Embracing data science for managers helps professionals stay ahead of the competition and enhances their leadership potential. Whether you are starting fresh or looking to upskill, gaining expertise in data science can open doors to exciting career opportunities.
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