Data Scientist vs. Decision Scientist

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What is the difference between a Data Scientist and a Decision Scientist?

The differences between the job roles of a data scientist and a decision scientist is subtle. While a data scientist is only involved with finding meaning in the chaos of big data, a decision scientist looks at big data with a view to solve a business problem. Decision scientists are nurtured and valued in the organization and they have an acute understanding of the businesses' goals and vision, can clearly define business problems and have the necessary acumen to solve these problems using data science skills. What follows is an elaborate discussion on the differences between a Data Scientist and a Decision Scientist job roles.

“Data Scientists are hot property, but Decision Scientists are what you need to nurture.” -said Deepinder Dhingra, Head of Products and Strategy at Mu Sigma.

Organizations have realized the importance of big data but the value of big data is difficult to quantify and it is unquestionably huge. As organizations continue to leverage big data, numerous big data jobs have been reviewed and profiled as the most appealing jobs of 21^st century. The two sought after major big data job roles that cannot be overlooked are- Decision Scientist and Data Scientist. At times, these professionals are professed as data analysts but in reality they are the ones who help organizations understand the importance of voluminous data, make the data talk and continuously strive to provide fascinating business insights that can be easily understood. You can call them the best data storytellers.

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Data Scientist vs. Decision Scientist- Two Disparate but Interconnected Job Roles

Data Scientists are professionals capable of applying technology, mathematics and statistics to well-defined business problems but the challenging part of this job role is that business problems keep changing constantly and are often poorly defined. To tackle such business problems, expertise in technology, mathematics and statistics is not enough. There is a need for professionals who have broad and strong business acumen, ability to effectively communicate with different stakeholders of the business, thinking ability to design and simplify poorly defined business problems and have an in-depth understanding of decision making processes within the organization. A rare breed of such professionals are termed as Decision Scientists who aim at helping organizations not only with deriving and translating meaningful insights but also assist in effective and profitable decision making.

Decision scientists must possess exceptional knowledge of the business domain along with expertise in technology, math and statistics. Decision scientists have intellectual and quantitative horsepower, have the ability to think from business synthesis and principles and have that special X factor i.e. the right curiosity quotient.

Deepinder Dhingra, Head of Products and Strategy at Mu Sigma correctly said in his blog post titled “Data science misses half the equation: An argument for decision science”– “Data science is essentially an intersection of math and technology skills. Individuals with these skills have been labelled data scientists and organizations are competing to hire them. But what organizations need are individuals who, in addition to math and technology, can bring in the right business perspective. These individuals must have the ability to artfully blend left-brained and right-brained thinking to solve complex business problems. They should possess the requisite analytical skills to understand, translate and generate insights that can then be consumed effectively.” He refers to them as decision scientists “who complete the data-driven decision making process started by data scientists.”

Data Scientist vs. Decision Scientists- Differences Unleashed

1. Decision scientists are rare. Professionals who can deviously combine technology, mathematics, business and behavioural science are referred to as decision scientists. Decision scientists have the ability to create and gain buy-in for novel ideas with the main motive to produce a working model that can help business make effective data driven decisions. Data scientists are professionals with expertise in math and technology but when you add in behavioural science, business acumen and design thinking to these professionals they can be termed as decision scientists who help in - informed decision making and improved insights.
2. Decision scientists are professionals who don’t essentially work with big data but data scientists are dedicated to work with big data and all kinds of problems associated with the big data domain.

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1. Data scientists strive to create stories from number crunching whereas decision scientists strive to make those stories realistic.
2. Being a decision maker plays a vital role in differentiating the two popular job roles- data scientist and decision scientist. A data scientist aims at creating a framework that is provided to a machine for decision making whereas a decision scientist provides framework for decision making to human beings.
3. A data scientist begins with the data by focusing on the previous data trends to make the best decision whereas a decision scientists begins from values by focussing on what customers want in the future to make the best decision.
4. The job role of a data scientist is at one side of the equation - linking the predominant analytics talent shortage and a decision scientist is at the core of analytics that help organizations make informed decisions. Decision scientist completes the equation of analytics successfully.

Data Scientist+Decision Scientist=Perfect Analytics Equation

7. The insights drawn by data scientists can be meaningful if they can be transformed into a decision by the decision scientist otherwise it is a mere waste of time and resources. To sum it up, data analytics that can be used to drive business impact is decision science otherwise it is not analytics but just statistics termed as data science.

  8. Data scientists helps organizations create analytics whereas decisions scientists help organizations consume analytics.

  9.The job roles of a decision scientist and data scientist intersect each other but none of them is a subset of the other. One can study about decision making using big data and also various other tools like experiments and mathematical models.

10. A decisions scientist’s problem is similar to a marketing analytics problem - where the customer base is segmented, high margin target market is identified, to search for qualified leads, etc. In this case, the cost of making a bad decision is comparatively low as the decision making is under uncertainty whereas a data scientists’ problem can be something like-

• How to achieve reliable and stable output by analysing a jet engine’s performance to ensure uptime and maximum efficiency during the light?
• How to optimize the consumption of fuel or how to decrease the maintenance cost?
• How to increase the life span of the engine?

If we consider these problems, the impact and cost incurred due to failure in flight is considerably high. The data scientists need a reliable machine learning algorithm which in this case can make computations and decisions in real time based on in-flight streaming data and ground data to monitor the condition in real time.

11. A data scientist builds machines to make decisions about complex and important dynamical processes that are too fast for a human operator to tackle. They are not bothered whether the data science algorithm is understandable or socializable but they focus on the reliability, accuracy, robustness and functionality of the algorithm. A decision scientist on the other hand develops decision support tools for taking actions or decisions with a data centric preference under uncertainty.Decision scientists generally like linear solutions which are simple, can be understood and are socializable decision making framework.

In the epic war of big data, for organizations to build a winning strategy, they need the data storytellers-decision scientists and data scientists. The two disparate interconnected job roles- decision scientist and data scientist can help organizations realize the complete potential of data driven decision making. It is extremely important for organizations to institutionalize decision support by hiring data scientists and decision scientists to complete the analytics equation.If you work at an organization that hires data scientists and decision scientists- contribute to the discussion above in comments below to understand the differences between the two.

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