Over the past decade, the growing importance of big data and analytics has earned an attractive place among the general public. The term data science is no longer just a reference used by tech enthusiasts and computer geeks; it is now one of the most innovative and sexiest jobs of the 21st century. The shift is not so surprising, when in fact, data science drives almost everything we interact with on a daily basis.
Data science describes the individual that works with analytical models and data to generate business insights that can be extended to a range of business processes. The appeal perhaps comes from the idea that data scientists have a strong mix of machine learning, computer science, programming and hacking skills.
The truth is, the field of data science requires much more than just the simple study of data. Leveraging data to enable better decision making fundamentally refers to the application of technology and math on data to elicit insights for problems, which are clearly defined.
However, business problems are not always clearly defined in the real world. To help solve them, a person needs to appreciate and understand its business context. This requires an interdisciplinary approach using a variety of skills, including behavioral sciences, technology, applied math, and business.
Data-driven decisions can be thought as a journey from data engineering into decision sciences.
- Data-driven decisions begin with the application of technology and data engineering to assist with the collection, storing, processing, transformation, and structuring of data to enable its use as decision support.
- Data science builds on data engineering models through the application of technology and math to solve business-focused problems. This involves mathematical/algorithmic, visualization, and analysis computation to extract specific insights using clearly defined data elements.
- Decision science is the interdisciplinary application of design, technology, math, business, and behavioral sciences to enable better decision-making. This field addresses business problems and shifting factors that are ill-defined or not very well understood, facilitating the design-thinking paradigm.
For example, a data scientist will take a mysterious business problem or one that comes from a hunch, to heuristic, judgment and rule-based, and into pattern-seeking algorithms that become tools in the operationalized system. This further promotes the ongoing creation, transformation, and consumption of insights to assist organizations in better decision-making, building on aspects of behavioral sciences, design thinking, and business context.
While better decision-making through data science is clear enough in concept, we are constantly reminded of the overall fallacy of rationalized decision-making. As we read the latest economic, business, and societal tragedies both domestically and internationally, it is apparent that poor decision-making is still all around us. The Internet and access to information continue to grow and with it the need data-driven organizational decision-making. Most importantly, we are left to examine just how can decision-making skills be improved for our business, careers, and customers.
What does better decision-making entail? When we examined decision science, we learned that the major challenges to effective business management are uncertainty, ambiguity, and complexity of the environment where we make decisions. The way that this environment is set up and analyzed is the very basis of our decision-making techniques.
In fact, there is an apparent distinction between good, better, and great decisions. Good decisions are made possible in business environments that examine the data at hand. Unfortunately, many organizations decisions are taken in presumptive environments in which the right decisions cannot be discovered from the available data. Better decision-making skills begin with minimizing the causes of uncertainty. Last but not least, great decisions are those in which challenges, insight, and context allow us to re-examine the very nature of decision-making process itself.
While machine learning is often presented to management as a magical problem solver, it is actually nothing more than a technical tool that has a proprietary focus to explore the nature of the problems we face. We know that the answer is data, the challenge is deducing it. This is where data science has experienced its rise.
Data and Decision Scientist Shortage
Industry experts and research organizations have declared a critical shortage of data scientists. As the concept of data and technology continues to expand at a rapid rate this number is only expected to intensify in the future. While there is an inherent focus on the need for filling this talent shortage, there is more to the story. At the core, data science also requires analytics to help a business make better data-driven decisions. Decision science is the key to completing this equation.
Decision scientists, on the other hand, are truly rare. They are the ones who artfully blend behavioral science, technology, math, and business. They need to have good and precise communication, with the ability to buy-in and synthesize new ideas. The ultimate objective is to not only provide a working model but to help the organization make well-informed, data-driven decisions.
In fact, data science is much less about the theory than it is about the practice of integrating a decision-making foundation into the way that we work and run our organizations.
So how exactly does data science lead to better decision-making? In short, business analytics is a simple yet critical process designed to help people make great decisions in the context of their very work.
- First, we need to capture a complete scan of the environment (digital and physical) to understand the very nature of the problems in which we are trying to solve.
- Second, one must explore the quality of data with which he or she has to work.
- Next, the correct methodology will be applied to further explore and formulate solutions in response to the analysis of the data at hand.
- Finally, the data will be transformed into dialogue that is used to motivate organizational communities and structural teams to take appropriate steps of action.