‘Decision Science’ is a collaborative approach involving business tactics, analytics, technological applications and behavioural sciences to help senior management make data driven decisions. Business plus Analytics plus Data Technology plus Behavioral Sciences = Decision Science
Business is an organizational entity for profit or social cause, whose endeavor is to improve, improvise and inflate growth parameters. It is important to understand the pulse of respective domain through experience and expertise, operational in-sights, market and consumer behavior and ability to take balanced risk at the right time to ensure right decision making.
With the advent of competition and technology, the need for a business to stay ahead of competition and know of market/customer preference in advance is the only way to predict and secure it’s future. Business Intelligence shall help an organization understand what has happened. What is to happen, when is to happen and how is to happen demands deep domain understanding, advanced analytical in-sights and Big Data skills to opt for the optimal decision options to embrace leadership position.
Introduction of enterprise resource planning (ERP) systems has ensured availability of data in many organizations; however, traditional ERP systems lacked data analysis capabilities that can assist the management in decision making. Business Analytics is a set of mathematical and statistical techniques that can be used to analyze data to improve business performance through fact-based decision making. The role of Business Analytics in approaching business problems has increased manifold in the recent years leading to increased demand for Business Analytics professionals. Business Analytics creates capabilities for companies to compete in the market effectively. Business Analytics has become one of the main functional areas in most companies. Business Analytics companies develop the ability to support their decisions through analytical reasoning using a variety of statistical and mathematical techniques. Thomas Devonport in his book titled, “Competing on Business Analytics: The new science of winning”, claims that a significant proportion of high-performance companies have high analytical skills among their personnel. On the other hand, a recent study has also revealed that more than 59% of the organizations do not have information required for decision making.
The theory of bounded rationality proposed by Nobel Laureate Herbert Simon is ever more significant today with the increased complexity of business problems; the human mind is constrained in its capacity to evaluate alternatives, given limited time to make conclusions.
Algorithms that work well on “small” datasets crumble when the size of the data extends into the gigabytes. Time series techniques must be revamped to handle streaming data in continuous time. Social media messages have data formats that are unfit to be represented by traditional databases. While these may appear to be difficult problems, there has been a tremendous progress in big data Business Analytics.
For example, columnar databases have significantly boosted query speeds. File systems can seamlessly distribute datasets on multiple hard drives, and facilitate Business Analytics on them in real time. Finally, the free and open source nature of several big data platforms promotes rapid adoption.
Large and small enterprises are forced to grapple with problems of big data, which challenge the existing tenets of data science and computing technologies. Techniques in predictive Business Analytics rely heavily on the validity of statistical concepts such as independent and identically distributed (IID) random variables and the central limit theorem (CLT). When dealing with big data, the validity of these assumptions becomes questionable. Straightforward tasks such as interpreting descriptive statistics have their share of issues. We begin to question the utility of summary measures and diagrams.
The essence of “big data” is volume, velocity and variety. The volume and pace at which data is created can challenge existing computing infrastructure. For example, a flight can generate up to 1 terabyte (~1000 gigabytes) of data. Making sense of this data is imperative for decision making and troubleshooting. The time to understand and appreciate in-sights through Big Data is fast approaching to be in tune with real-time.
The term behavioural sciences (behavioral science) encompass the various disciplines and interactions among organisms in the natural world. It involves the systematic analysis and investigation of human and animal behaviour through the study of the past, controlled and naturalistic observation of the present, and disciplined scientific experimentation. It attempts to accomplish legitimate, objective conclusions through rigorous formulations and observation.