This Tutorial, I will guide What is Decision Science? Learn about the skills required to become a Decision Scientist and the different roles of a Decision Scientist.
For decades now, data has been sprinting its way up towards the leading edge of businesses. With the availability of sophisticated storage systems, it is now possible to store huge amounts of data generated by sales, customer interactions, and digital experiences. Mechanisms for easy integration of disparate systems, have ensured that enormous volumes of data are continually flooding the business world.
Data technologies have managed to tame this data into actionable insights. However, data is going to continue to spawn in the future. If exploited efficiently, massive repositories of data can provide a huge opportunity for businesses. This is where decision science comes in.
Decision science requires the ability to embrace data and utilize it in a manner that can help stakeholders arrive at critical business decisions. Utilizing data effectively to make informed business decisions translates to making intelligent inferences from data, creating effective stories, identifying relevant challenges, and then applying this knowledge correctly to the right set of problems in the business world.
A career in decision science involves creating solutions based on sound fundamentals of probability, forecasting, experimentation, and computational expertise.
Decision scientists require a combination of certain indispensable skills, which they use in collaboration to extract value from data and solve problems. These skills include the ability to use advanced knowledge of mathematics to understand patterns and trends. Then, statistical science helps to perform technical analysis of data based on patterns and trends.
Machine learning helps to assess possibilities galore and make predictions without human intervention. Further, decision scientists require business acumen, so they can perform an astute analysis of critical challenges and make important decisions to tackle them.
They play a very important role in using the data extracted from small pockets that exist in silos and assembling the chunks together, using the knowledge of business dynamics coupled with intuition and far-sightedness to create the big picture. In short, decision scientists are artists who combine the various sciences of maths, technology, and business for doing their job.
Read: Data Science vs Decision Science
The expertise of decision scientists is useful in many industries. These include driving sales in the retail industry, providing value to the banking industry, transforming the aviation industry, contributing to the healthcare industry, transportation, communication, education, and so many more. The kind of work that one can expect to do is described using the examples of retail and banking sectors.
As a decision scientist in the retail industry, you will see yourself using data for developing pricing strategies, discount techniques, and sales tactics. You will determine the right price points that would be favourable to a retail store and will contribute to the increase in revenue.
Similar contributions can be made in the areas of vendor management, inventory management, and store planning to arrive at important decisions such as the kinds of products that should be displayed together depending on the behaviour of consumers and their triggers for impulse purchases. Not only this, but you will also see yourself using trends in social media to analyze the interests of your customers and conducting marketing campaigns to drive sales.
In the banking industry, decision scientists use information about customer data and customer trends to categorize their clients. Based on customer lifetime value and other relevant factors, you will use predictive analysis to personalize services and products for diverse groups of your clients. This will also involve strategizing personalized marketing initiatives and relevant interactions to acquire as well as retain customers. Nevertheless, it will involve contribution towards designing products that can help achieve business growth.
Similarly, decision scientists play a major role in the areas of fraud prevention, and risk mitigation resulting in banks saving a huge amount of money. Huge customer data can be leveraged to understand, for example, the probability of fraud even before it is committed or chances of default on payments right at the stage of handing loans out.
Based on how customers in a specific group use technology, decision scientists ensure investments to optimize the development and use of appropriate technological systems to derive value. Demographics play an important role in this area. For example, in a specific region where there is a large population of young people who use their mobile phones for payments, a sound business strategy may require spending on the development and maintenance of a mobile application for the bank.
However, for a different group of customers, who do not have expertise in technology, are probably retired and have time may prefer personal interaction. Serving this group may mean the deployment of relationship management personnel to create satisfied customers and win brand loyalty. This may further extend to creating specialized products for a group that is highly likely to invest in specific financial products, thus promoting cross-selling.
Must Read: Data Science Career Path
You may be familiar with technology or may have had a start in a field that is entirely different, yet you can work your way in the field of decision science. Here are the various career options that you can explore:
Business analysts conduct the initial research to understand a customer’s business. They form crucial links in the chain of exploring requirements and pain areas of a customer’s business. They perform an end-to-end analysis of business processes and workflows. Then, they convert this information in a language that is easily understandable by technical users and software architects who further create designs and develop software to meet the customers’ expectations.
Business analysts must have excellent communication skills. As a business analyst, you will need to build a fantastic relationship with customers. You will need to ask a lot of relevant questions to be able to find out what exactly a customer needs. Next, you will set up numerous meetings with your technical team to ensure what they build concurs with what the customer has in mind. You will also need to understand how to document business flows and building prototypes in the form of sketches and wireframes.
According to PayScale, the average salary earned by business analysts in India is ₹607,209.
Software development requires creating software to enable business processes and workflows.
The discipline requires understanding requirements, creating high-level and detailed designs for a project, creating software, and finally testing the code to meet specific business requirements of a customer. Software engineering requires the use of programming languages and various tools, for example, Git, GitHub, IntelliJ, etc. As a software engineer, you will also need a thorough understanding of software architecture and knowledge of backend and front-end development techniques.
Software developers require expertise in programming languages, experience with a database, mathematical prowess, problem-solving skills, attention to detail, ability to work in a team and to collaborate.
According to Glassdoor, the average salary developed by software engineers in India is ₹607,209.
Data scientists automate manual processes. They identify the correct sets of data for analysis and use statistical methods of data analysis for visualizing, classifying, and segregating data. A data scientist wears multiple hats for their work, including that of a software engineer, data analyst, business analyst, etc.
Data scientists should know how to work with Statistics. They should know at least one programming language, for example, R or Python. They should be able to perform data extraction, transformation, and loading, preferably using ETL tools. Data scientists should be able to work with data wrangling and data exploration, data visualization, and machine learning.
According to PayScale, the average salary developed by software engineers in India is ₹812,528
Data analysts gather information from various sources and query data to obtain information. They make joins, combine data, and present it as requested for various data requests.
Data analysts should be able to work with database tables to collate data and create reports. You should be able to use tools like SQL and PostgreSQL and dashboard tools such as Tableau, PowerBI, and so on.
According to PayScale, the average salary developed by software engineers in India is ₹419,465
In Conversation With Dr Suman Sanyal, NIIT University,he shares his insights on how universities can contribute to this highly promising sector and what aspirants can do to build a successful data science career.
Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments. Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.
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