BUSINESS ANALYTICS VS PREDICTIVE ANALYSIS VS DATA SCIENCE

With increasing globalization and liberalization and recent advancement in information technology have transformed every facet of human life and revolutionized the world as never before.

Computer soft wares are gradually becoming a decisive force in making important decision. The use of software’s has penetrated in every walk of life be it transportation ,Medicare ,telecommunication ,defense ,industrial processing ,entertainment, office utilities ,space research, environmental predictions and so on.

BUSINESS ANALYTICS:

Business analytics is basically the use of data, information technology, statistical analysis, quantitative methods and mathematical or computer based models to help managers gain a better understanding about their business operations and make better fact based decisions.

Business analytics has a strong relationship with revenue of business, profitability of business and shareholder return. Business analytics enhances the understanding of data and Is vital for businesses to remain competitive.

Business analytics helps in recognition of problem, defining and structuring of the problem followed by analyzing the problem and then finally interpreting results and making a decision .

Managers interpret the results from the analysis phase and then translates the results to real world

Business analytics is basically interpreting and analyzing huge sets of data and applying skills, technologies and practices to derive insights that can be used for future business planning

It is a field that is now applied across all domains and industries.

With more and more data being generated, the demand for business analysts is increasing

Business analytics is the study of data through statistical and operational analysis, the formation of predictive models and trends ,application of optimization techniques and communication of these results to customers and business partners.

It requires quantitative methods for business modeling and decision making such as use of big data.

It uses statistical analysis to find out and explain why certain results occur.  Business analytics provides support for companies in process of making tactical decisions as well as helps in automating decision making to support real time responses.

Business analytics provides a competitive advantage to companies. In this digital era , flow of information is almost equal to all players in the market ,it depends on how this information is being analyzed and utilized by different companies which gives them a competitive edge over other companies.

Business analytics combines available data with various well thought models to improve business decisions.

It helps in converting available data into valuable information that can be presented in any required format

Some examples of using business analytics include finding patterns and relationships between processes and outcomes ,explaining why certain result was achieved and determining if a previous decision was inappropriate or predicting the likelihood of a certain result happening.

Decisions are based on evidence rather than guess. This is called data driven decision making

BUSINESS ANALYTICS APPLICATIONS:

  • Customer relationship management
  • Supply chain management
  • Financial and marketing activities
  • Human resource planning
  • Pricing decisions
  • Building effective strategies
  • Forecasting and inventory management
  • Fraud detection

DATA SCIENCE:

Data science is a multidisciplinary field with a blend of algorithm development, data inference and technology in order to solve analytically complex problems.

Data science generates business value by using data in creative ways.

Data science is a thing that encapsulates some programming skills, some statistical readiness and some visualization techniques.

A data scientist in my understanding is a person who connects the dots between the business world and the data world. The process of data science consists of data munging,data mining and delivering actionable insights

IMPORTANCE OF DATA SCIENCE:

  • HELPS IN UNDERSTANDING THE COMPLEX BEHAVIOURS ,TRENDS AND INFRENCES
  • IT SURFACES HIDDEN INSIGHTS THAT CAN HELP ENABLE COMPANIES TO MAKE SMARTER BUSINESS DECISIONS
  • DATA SCIENCE HELPS IN IDENTIFYING MAJOR CUSTOMER SEGMENTS AND STUDIES THE UNIQUE SHOPPING BEHAVIOURS WITH THOSE SEGEMENTS.
  • HELPS IN PREDICTING THE FUTURE DEMAND THAT FURTHUR HELPS TO PLAN PRODUCTION LEVELS MORE OPTIMALLY.
  • HELPS IN PROVIDING STRATEGIC GUIDANCE
  • BETTER RISK ANALYSIS
  • RECRUIT BETTER IN LESSER TIME
  • BETTER BUSINESS VALUE

DATA SCIENCE is a blend of skills in three major areas

  1. Mathematical expertise
  2. Statics
  3. Computer science

ANALYTICS is a term used loosely meant to describe science of analysis, or the practice of analyzing information to make decisions.

Analytics is somewhat synonymous to data science but depending upon the context.

It sometimes represents something else.

A non-technical business user interpreting pre build dashboard reports is also in the realm of analytics whereas a data scientist using raw data to build a predictive algorithm falls into the scope of analytics.DATA SCIENCE projects can have enormous returns on investment both from guidance through data insight and development of data product.

There is not enough supply of data scientists in the market to meet the ever increasing demand.

DIFFERENCE BETWEEN DATA SCIENCE AND BUSINESS ANALYTICS:

  • Data scientists play a special role with abilities in math, technology and business acumen. They work at a raw database level to derive insights from data and build data product. Data science in other words can be used for connecting information and data points to find connections that can be made useful for business.
  • Whereas a business .analyst can mean a lot of things like data analyst, marketing analyst, financial analyst, etc. the main aim of analysts is to try to gain insights from data for informed decision making. Analyst may interact with data at both database level and summarized report level. Business analytics refer to the qualitative and quantitative techniques and processes used gain profit and enhance productivity.
  • Data scientist and data analysts are different .data analysts start by mining the data whereas data scientist start by asking the right question. Data scientist requires expertise and technical skills whereas data analysts should have soft skills and analytical skills.

SCOPE OF DATA SCIENCE

While data science is fast growing and enlarging both as a field of learning and career choice, many are yet exploring specific skill sets required for exploring career opportunities.

It revolves around reading and processing data and pulling knowledge from the data.

Data science career is ideally suited for mathematical and analytical minds to analyze data.

An aspiring data scientist must also be a good communicator so that he can present complicated data insights in an interesting manner.

There are numerous job openings opportunities for data scientists and the demand is ever increasing,

PREDICTIVE ANALYSIS:

Predictive analytics is basically used to forecast activity ,behavior and trends by using both new and historical data . it is a form of advanced analytics.

Predictive analytics involve analytical queries ,applying statistical techniques and automated machine learning algorithms to data sets to create predictive models that place a score on likelihood of a particular event happening .

Predictive analysis helps in analyzing and predicting likely behavior of individuals, machinery or other entities

For example, an insurance company will take into account potential driving safety variables, such as gender, age ,location type of vehicle and driving record, when pricing and issuing insurance policies. .

Business applications for predictive analytics :

  • Targeting online advertisements,
  • Analyzing customer behavior to determine buying patterns,
  • Flagging potentially fraudulent financial transactions,
  • Identifying patients at risk of developing particular medical conditions a
  • Detecting impending parts failures in industrial equipment before they occur.
  • Customer segmentation
  • Risk assessment
  • Sales forecasting
  • Market analysis
  • Financial modeling
  • Optimaztion of marketing campaigns
  • Enhanced operations

Predictive analysis refers to using machine learning ,historical data and artificial intelligence to predict what will happen in the future .

This historical data is converted into a mathematical model that analyzes the key trends and patterns in data .this model is then applied to current data to predict what will happen next

Predictive analysis tools can help companies and business applications and suggest actions that reinforce positive operational changes.

Analysts use predictive analysis techniques to foresee if a change will reduce risk ,gain profits or improve operations

PREDICTIVE ANALYSIS VS DATA SCIENCE:

  • Predictive Analytics is a process derived from data miningmachine learningand predictive modeling whereas data science is the study of various types of structured, semi structured and unstructured data in order to get some information out of it.
  • Predictive analytics is used for predicting trends and outcomes pattern whereas data science starts with data mining, data storing data transformation in order to make it effective and efficient
  • Predictive analyticsis not only used for predicting an unknown future event but also for the present and past events whereas data science is useful in studying the customer behavior and patterns.
  • Predictive analysis is used to predict a business of a company whereas data science is used to manage a company’s data
  • Predictive analysis is used to run a company smoothly whereas data science is used to reduce data redundancy and avoid confusion

CONCLUSION

Irrespective of whichever industry you belong to, if you look around the existing processes, one will find out how predictive analysis helps in better and informed decision making.

Predictive analysis is used to innovatively supercharge business processes and make the system more data dependent.

Every industry is now a days relying on various predictive analysis to improve how they run their business.

The information that is collected from predictive analysis is used to make decisions that impact the business’s bottom line and influence results

Hence predictive analysis has become a popular concept with interest steadily rising over past few years.

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