Introduction
In 1918, four years after he was hired by the DuPont Corporation, electrical engineer F. Donaldson Brown was given the task of untangling the finances of a company in which Du Pont had just invested. (This company was General Motors and DuPont had purchased 23 percent of its stock.)
Brown's work led to the development of a system of planning and control for all operating decisions within a firm - the analytical system which became the dominant form of financial analysis in corporations throughout the world. (The Dupont Analysis)
Fast-forward to 2010, when Ted Plush, a 32-year veteran of DuPont, was given the task of making DuPont "a predictive enterprise" -- using analytics not just to report and analyze the past, but provide the insights that will guide the global organization
strategy going forward.
The market is witnessing an unprecedented shift in business intelligence (BI), largely because of technological innovation and increasing business needs. The latest shift in the BI market is the move from traditional analytics to predictive analytics. Although predictive analytics belongs to the BI family, it is emerging as a distinct new software sector.
Analytical tools enable greater transparency, and can find and analyze past and present trends, as well as the hidden nature of data. However, past and present insight and trend information are not enough to be competitive in business. Business organizations need to know more about the future, and in particular, about future trends, patterns, and customer behavior in order to understand the market better. To meet this demand, many BI vendors developed predictive analytics to forecast future trends in customer behavior, buying patterns, and who is coming into and leaving the market and why.
Traditional analytical tools claim to have a real 360° view of the enterprise or business, but they analyze only historical data—data about what has already happened. Traditional analytics help gain insight for what was right and what went wrong in decision-making. Today’s tools merely provide rear view analysis. However, one cannot change the past, but one can prepare better for the future and decision makers want to see the predictable future, control it, and take actions today to attain tomorrow’s goals.
What is Predictive Analytics?
Predictive analytics are used to determine the probable future outcome of an event or the likelihood of a situation occurring. It is the branch of data mining concerned with the prediction of future probabilities and trends. Predictive analytics is used to automatically analyze large amounts of data with different variables; it includes clustering, decision trees, market basket analysis, regression modeling, neural nets, genetic algorithms, text mining, hypothesis testing, decision analytics, and more.
The core element of predictive analytics is the predictor, a variable that can be measured for an individual or entity to predict future behavior. For example, a credit card company could consider age, income, credit history, other demographics as predictors when issuing a credit card to determine an applicant’s risk factor.
Multiple predictors are combined into a predictive model, which, when subjected to analysis, can be used to forecast future probabilities with an acceptable level of reliability. In predictive modeling, data is collected, a statistical model is formulated, predictions are made, and the model is validated (or revised) as additional data become available.
Predictive analytics combine business knowledge and statistical analytical techniques to apply with business data to achieve insights. These insights help organizations understand how people behave as customers, buyers, sellers, distributors, etc.
Multiple related predictive models can produce good insights to make strategic company decisions, like where to explore new markets, acquisitions, and retentions; find up-selling and cross-selling opportunities; and discovering areas that can improve security and fraud detection. Predictive analytics indicates not only what to do, but also how and when to do it, and to explain what-if scenarios.
A Microscopic and Telescopic View of Your Data
Predictive analytics employs both a microscopic and telescopic view of data allowing organizations to see and analyze the minute details of a business, and to peer into the future. Traditional BI tools cannot accomplish this functionality. Traditional BI tools work with the assumptions one creates, and then will find if the statistical patterns match those assumptions. Predictive analytics go beyond those assumptions to discover previously unknown data; it then looks for patterns and associations anywhere and everywhere between seemingly disparate information.
Predictive Analytics and Data Mining
The future of data mining lies in predictive analytics. However, the terms data mining and data extraction are often confused with each other in the market. Data mining is more than data extraction It is the extraction of hidden predictive information from large databases or data warehouses. Data mining, also known as knowledge-discovery in databases, is the practice of automatically searching large stores of data for patterns. To do this, data mining uses computational techniques from statistics and pattern recognition. On the other hand, data extraction is the process of pulling data from one data source and loading them into a targeted database; for example, it pulls data from source or legacy system and loading data into standard database or data warehouse. Thus the critical difference between the two is data mining looks for patterns in data.
A predictive analytical model is built by data mining tools and techniques. Data mining tools extract data by accessing massive databases and then they process the data with advance algorithms to find hidden patterns and predictive information. Though there is an obvious connection between statistics and data mining, because methodologies used in data mining have originated in fields other than statistics.
Data mining sits at the common borders of several domains, including data base management, artificial intelligence, machine learning, pattern recognition, and data visualization. Common data mining techniques include artificial neural networks, decision trees, genetic algorithms, nearest neighbor method, and rule induction.
Major Predictive Analytics Vendors
Traditional powers such as SAS and SPSS(IBM) still sit atop the predictive analytic market. The predictive analytic and data mining leaders have supported large enterprise customers' advanced analytics needs for many years. All offer mature, high-performance, scalable, flexible, and robust [predictive analytic and data mining] solutions that combine a wide range of statistical algorithms with integrated support for in-database analytics and a broad range of information types
Other players such as KXEN Inc., Oracle Corp., and Portrait Software Fair Isaac Corp. (FICO), Angoss Software Corp., and TIBCO Software Inc. are creditable competitors and field very functional or respected offerings
User Recommendations
Depending on an organization’s needs, some predictive analytics tools will be more relevant than others. Each has its strengths and weakness and can be highly industry-and model-specific—the algorithms and models built for one industry are not applicable to other industries. Financial industries, for example, have different models than what are used in manufacturing and research industries.
Selecting the appropriate predictive analytics tools is not a simple task. The following capabilities must be taken into consideration: algorithm richness, degree of automation, scalability, model portability, web enablement, ease of use, and the capability to access large data sets. The more diversified the business, the more functions and unique models are required. Model portability is important even within different business units in the same company. The scalability of the solution and its ability to handle expanded functionality should also be verified and based on a business’ growth.
Users require extensive training and expertise to use the core functionalities of the predictive analytics solutions, such as identifying data, building the predictive model with right predictors, data mining knowledge to align with business strategy etc. Furthermore, predictive analytics automates model building, but does not automate the integration of business processes and knowledge. Thus expertise and training are required to evaluate the best software relevant to an organization’s unique business model.
If a company has or is willing to attain the expertise required to use predictive analytics it can definitely benefit from the tool. Although most large enterprises use some sort of traditional BI tool or platform, their tools do not provide predictive analytics functionality. Incorporating predictive analytics into an existing BI infrastructure can provide organizations’ a competitive advantage in their industry. Consequently, the integration of BI tools is a key consideration when selecting a predictive analytical tool, as is its integration with key applications such as enterprise resource planning, (ERP), customer resource management (CRM), and supply chain management (SCM) etc. Ultimately, since predictive analytics is currently the only way to analyze and monitor the business trends of the past, present, and future, selecting the right tool can be a key success factor in a company’s BI strategy.
Submitted By
Amiteshwar Singh
Roll No 12067
Compiled from various souces:
http://blogs.hbr.org/events/2010/05/moving-analytics-from-what-hap.html
http://www.slideshare.net/robertdpalmer/hbr-competing-on-analytics
http://searchbusinessanalytics.techtarget.com/news/1506983/Predictive-analytics-software-next-battleground-for-BI-vendors?ShortReg=1&mboxConv=searchBusinessAnalytics_RegActivate_Submit&
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