PREDICTIVE BUSINESS ANALYTICS PDF
Chapter 1 Why Analytics Will Be the Next Competitive Edge 3. Analytics: Just a Skill, or a Profession? 4. Business Intelligence versus Analytics versus Decisions . Predictive analytics encompasses a variety of statistical techniques from [3 ] In business, predictive models exploit patterns found in historical and. Predictive analytics provides the methodology in tapping . Along with traditional business data, firms are realizing value from social media.
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Healthcare presents the perfect storm for predictive analytics. . important business cases allows a team to discover what data speaks to these .. Reports/ MedicareFeeforSvcPartsAB/downloads/DRGdescpdf for a list of the DRG codes. Able to explain what is predictive analytics and where it is used . Source: Halper, F. (): Predictive Analytics for Business Advantage. participation in the field of predictive analytics. Predictive. Analysis. Center of. Excellence . Story teller – drive business value not just data insights. • Creativity .
The best example to explain descriptive analytics are the results, that a business gets from the web server through Google Analytics tools. The outcomes help understand what actually happened in the past and validate if a promotional campaign was successful or not based on basic parameters like page views.
The subsequent step in data reduction is predictive analytics. Analysing past data patterns and trends can accurately inform a business about what could happen in the future. This helps in setting realistic goals for the business, effective planning and restraining expectations.
It cannot do that. In fact, no analytics can do that. Predictive analytics can only forecast what might happen in the future, because all predictive analytics are probabilistic in nature. Companies can predict business growth in future if they keep things as they are.
Predictive analytics provides better recommendations and more future looking answers to questions that cannot be answered by BI.
To make predictions, algorithms take data and fill in the missing data with best possible guesses. Organizations should capitalise on hiring a group of data scientists in who can develop statistical and machine learning algorithms to leverage predictive analytics and design an effective business strategy.
Predictive analytics can be further categorized as — Predictive Modelling —What will happen next, if? Root Cause Analysis-Why this actually happened? Data Mining- Identifying correlated data. Forecasting- What if the existing trends continue? Monte-Carlo Simulation — What could happen? Pattern Identification and Alerts —When should an action be invoked to correct a process.
Sentiment analysis is the most common kind of predictive analytics. The learning model takes input in the form of plain text and the output of the model is a sentiment score that helps determine whether the sentiment is positive, negative or neutral.
Organizations like Walmart , Amazon and other retailers leverage predictive analytics to identify trends in sales based on purchase patterns of customers, forecasting customer behaviour, forecasting inventory levels, predicting what products customers are likely to purchase together so that they can offer personalized recommendations, predicting the amount of sales at the end of the quarter or year.
The best example where predictive analytics find great application is in producing the credit score.
Descriptive, Predictive, and Prescriptive Analytics Explained
Credit score helps financial institutions decide the probability of a customer paying credit bills on time. What is Prescriptive Analytics?
Big data might not be a reliable crystal ball for predicting the exact winning lottery numbers but it definitely can highlight the problems and help a business understand why those problems occurred. Businesses can use the data-backed and data-found factors to create prescriptions for the business problems, that lead to realizations and observations.
Prescriptive analytics is the next step of predictive analytics that adds the spice of manipulating the future. Prescriptive analytics advises on possible outcomes and results in actions that are likely to maximise key business metrics.
Stochastic optimization that helps understand how to achieve the best outcome and identify data uncertainties to make better decisions. Simulating the future, under various set of assumptions, allows scenario analysis - which when combined with different optimization techniques, allows prescriptive analysis to be performed. Prescriptive analysis explores several possible actions and suggests actions depending on the results of descriptive and predictive analytics of a given dataset.
Prescriptive analytics is a combination of data, mathematical models and various business rules. The data for prescriptive analytics can be both internal within the organization and external like social media data.
Business rules are preferences, best practices, boundaries and other constraints. Descriptive analytics are useful because they allow us to learn from past behaviors, and understand how they might influence future outcomes.
The vast majority of the statistics we use fall into this category. Think basic arithmetic like sums, averages, percent changes. Usually, the underlying data is a count, or aggregate of a filtered column of data to which basic math is applied. For all practical purposes, there are an infinite number of these statistics.
Descriptive statistics are useful to show things like, total stock in inventory, average dollars spent per customer and Year over year change in sales. Use Descriptive Analytics when you need to understand at an aggregate level what is going on in your company, and when you want to summarize and describe different aspects of your business.
These analytics are about understanding the future. Predictive analytics provides companies with actionable insights based on data.
Predictive analytics provide estimates about the likelihood of a future outcome. Companies use these statistics to forecast what might happen in the future. This is because the foundation of predictive analytics is based on probabilities.
These statistics try to take the data that you have, and fill in the missing data with best guesses. Companies use Predictive statistics and analytics anytime they want to look into the future. Predictive analytics can be used throughout the organization, from forecasting customer behavior and purchasing patterns to identifying trends in sales activities. They also help forecast demand for inputs from the supply chain , operations and inventory. One common application most people are familiar with is the use of predictive analytics to produce a credit score.
These scores are used by financial services to determine the probability of customers making future credit payments on time.The 10 scores are then weighted to give one final overall risk score for each location.
Predictive Business Analytics: Forward Looking Capabilities to Improve Business Performance
Advanced Engineering Informatics. The Huffington Post.
Portfolio, product or economy-level prediction Often the focus of analysis is not the consumer but the product, portfolio, firm, industry or even the economy. Some of them are briefly discussed below. A financial company needs to assess a borrower's potential and ability to pay before granting a loan. The same scoring approach was also used to identify high-risk check kiting accounts, potentially fraudulent travel agents, and questionable vendors.
Multinomial logistic regression An extension of the binary logit model to cases where the dependent variable has more than 2 categories is the multinomial logit model. Accounting Information Systems 1e covers the four roles for accountants with respect to information technology: 1.
Logistic regression In a classification setting, assigning outcome probabilities to observations can be achieved through the use of a logistic model, which is basically a method which transforms information about the binary dependent variable into an unbounded continuous variable and estimates a regular multivariate model See Allison's Logistic Regression for more information on the theory of Logistic Regression.