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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What makes a Data-Driven Manager better?
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?
They make fact-empowered decisions
With data at their fingertips, managers can make decisions based on hard evidence and backed by their intuition. While intuition is undoubtedly a vital characteristic to have for managers, they can convert it into actionable insights through data. Data analytics for managers enables them to look at past performance metrics and develop solutions that address business problems tactically.
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.
They improve products & services to meet customer needs
Data driven product management gives hard evidence about consumer sentiment and preferences. Data science deep dives into vast amounts of data to explore feedback, analyze the market for a company’s product or service, and share suggestions to improve them.
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.
They know the target audience
Because data science deep dives into customer sentiment, buying behavior, demographics, and needs, a data science product manager knows his target market. He also uses data to assess potential markets and determine if they are profitable for the business.
Organizations capture vast amounts of data on customers through multiple sources – customer surveys, social media analytics, Google Analytics, etc. But a data-driven manager knows that without applying data science to raw data, they could miss out on important information. So, they employ data science models to extract relevant data points from a mound of information.
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They think of the future
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.
How can Managers apply Data Science?
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.
Deep Learning for Excellent Customer Service
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.
Machine Learning to restructure Business Operations
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.
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Predictive Models for Future-Forward Decisions
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.
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Recommendation Engines for Customer Engagement
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.
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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.
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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.