The word analytics has come into the foreground in last decade or so. The proliferation of the internet and information technology has made analytics very relevant in the current age. Analytics is a field which combines data, information technology, statistical analysis, quantitative methods and computer-based models into one. This all are combined to provide decision makers all the possible scenarios to make a well thought and researched decision. The computer-based model ensures that decision makers are able to see performance of decision under various scenarios.
Importance of Business Analytics
- Business analytics is a methodology or tool to make a sound commercial decision. Hence it impacts functioning of the whole organization. Therefore, business analytics can help improve profitability of the business, increase market share and revenue and provide better return to a shareholder.
- Facilitates better understanding of available primary and secondary data, which again affect operational efficiency of several departments.
- Provides a competitive advantage to companies. In this digital age flow of information is almost equal to all the players. It is how this information is utilized makes the company competitive. Business analytics combines available data with various well thought models to improve business decisions.
- Converts available data into valuable information. This information can be presented in any required format, comfortable to the decision maker.
Evolution of Business Analytics
Business analytics has been existence since very long time and has evolved with availability of newer and better technologies. It has its roots in operations research, which was extensively used during World War II. Operations research was an analytical way to look at data to conduct military operations. Over a period of time, this technique started getting utilized for business. Here operation’s research evolved into management science. Again, basis for management science remained same as operation research in data, decision making models, etc.
As the economies started developing and companies became more and more competitive, management science evolved into business intelligence, decision support systems and into PC software.
Scope of Business Analytics
Business analytics has a wide range of application and usages. It can be used for descriptive analysis in which data is utilized to understand past and present situation. This kind of descriptive analysis is used to asses’ current market position of the company and effectiveness of previous business decision.
It is used for predictive analysis, which is typical used to asses’ previous business performance.
Business analytics is also used for prescriptive analysis, which is utilized to formulate optimization techniques for stronger business performance.
For example, business analytics is used to determine pricing of various products in a departmental store based past and present set of information.
Data for Analytics
Business analytics uses data from three sources for construction of the business model. It uses business data such as annual reports, financial ratios, marketing research, etc. It uses the database which contains various computer files and information coming from data analysis.
Challenges
Business analytics can be possible only on large volume of data. It is sometime difficult obtain large volume of data and not question its integrity.
Different Types Of Data Analytics
Let me take you through the main types of analytics and the scenarios under which they are normally employed.
1. Descriptive Analytics
As the name implies, descriptive analysis or statistics can summarize raw data and convert it into a form that can be easily understood by humans. They can describe in detail about an event that has occurred in the past. This type of analytics is helpful in deriving any pattern if any from past events or drawing interpretations from them so that better strategies for the future can be framed
This is the most frequently used type of analytics across organizations. It’s crucial in revealing the key metrics and measures within any business.
2. Diagnostic Analytics
The obvious successor to descriptive analytics is diagnostic analytics. Diagnostic analytical tools aid an analyst to dig deeper into an issue at hand so that they can arrive at the source of a problem.
In a structured business environment, tools for both descriptive and diagnostic analytics go hand-in-hand!
3. Predictive Analytics
Any business that is pursuing success should have foresight. Predictive analytics helps businesses to forecast trends based on the current events. Whether it’s predicting the probability of an event happening in future or estimating the accurate time it will happen can all be determined with the help of predictive analytical models.
Usually, many different but co-dependent variables are analyzed to predict a trend in this type of analysis. For example, in the healthcare domain, prospective health risks can be predicted based on an individual’s habits/diet/genetic composition. Therefore, these models are most important across various fields.
4. Prescriptive Analytics
This type of analytics explains the step-by-step process in a situation. For instance, a prescriptive analysis is what comes into play when your Uber driver gets the easier route from Gmaps. The best route was chosen by considering the distance of every available route from your pick-up route to the destination and the traffic constraints on each road.
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