Posts Tagged ‘Change’

Wind Kiting in Greece of Europe



Free video about kite surfing. This free video was created for you by and can be used for free under the creative commons license with the attribution of epSos.de as the original author of this kite surfing video.

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The kite surfing or kite surfing (also sometimes called kiteboarding, or flysurfing ) is a sport that involves sliding using a kite traction ( kite, English), which pulls the athlete ( kiter ) for four or five (rarely two) lines, two fixed to the bar, and the two or three remaining through the center of the rod and attached to the body by a harness, allowing hydroplane through a table or ski type Wakeboard designed for this purpose.
You can practice various forms; jumps and maneuvers ( freestyle ), between buoys races ( race ) and surf in waves.

The basic equipment has different safety features. The tandem kite-bar is the more items it contains. If a gust of wind too strong with the kite you can not control and can pull, the harness called the chickenloop has a safety ring that releases the kite body. It is only then that acts the fifth line option, which prevents the kite release and lose. Through the bar pass a line, giving room for the bar either stays close to the body or out, this action affects slightly on the kite making it more or less sensitive to wind (capturing more or less wind), is itself a safety measure, because you can regulate the kite when strong winds come. Kites two lines do not have this system, which is really essential.

Although the practice of this sport is rather recent widespread, it is known that from the twelfth century in China and Indonesia, where they used kites to haul small boats. In the early nineteenth century, the British inventor George Pocock patented a kite traction for cars and boats. He made ​​several tests and broke several records. Their ships could sail in directions within 90 degrees from the wind direction. In November 1903, the American inventor Samuel Cody crossed on Channel surfing with kites. In 1970, the English invented Kite Peter Powell two lines, and built a delta shaped kite with which he sailed in small boats. It is not, however, until 1977 when Gijsbertus Adrianus Panhuise patented a navigation system on a table surf for a type of parachute, thus becoming the father of kitesurfing. In Indonesia it is a culture and art, the designs are many and varied, in these areas is where the industry is kiteboarding.

The person with the kite attached to the waist using a device called a trapezoid, it is placed on the board, controls the kite with the bar, and the water is driven by the wind that hits kite. To control it through a bar, you can move (luff or bear away) choosing a path, catching waves or performing jumps. This sport, relatively recent, is a time of great popularity and a growing practice in Brazil, and the world. The kiteboarder uses various equipment, first connect your waist a ‘trapeze’, which is a belt possessed a hook made ​​of steel, then connects to “slash” the trapeze, through the “chicken loop” (a strap with a clip, the which is part of the bar and connects the kite through lines (4 or 5 according to the model of the kite).

Already exist in the market with kites hybrid form between the “C” and the “flat”, who seek the best of both. The kite “bow” is easier to relaunch after falling into the water, some redecolam without intervention athlete. The many lines of the halter may curl, especially in inexperienced hands. Depending on the model takes a long time to deflate and store. If improperly adjusted or a small disruption in halter occurs, the kite loses the flight profile and looks terrible.

In concept, are similar to kites toy because they have only one layer of fabric and fiber frame. Are inexpensive and exert traction as well, but less than a foil. Redecoláveis ​​are not, and may even sink. The frames can break or lead to severe injuries from the impact of falls. Kite surfing is practiced with a table at the foot with which he “glides” on the water. In light wind conditions using kites larger than those used in high winds. With ideal conditions it is possible to practice the sport in a safe, just gliding (free-riding), performing various tricks evolution (freestyle). You can use the kite is on the waves and on flat water depending on the characteristics of the spot, that is, in the jargon used windy place.

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Inside of CHINESE FACTORIES – The Fact about Doing the job Conditions at FOXCONN, APPLE , HP FACTORIES

Inside of CHINESE FACTORIES – The Fact about Doing the job Conditions at FOXCONN, APPLE , HP FACTORIES



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Within CHINESE FACTORIES – The Truth of the matter about Doing work Ailments at FOXCONN, APPLE / HP FACTORIES

Reviews early Monday from China advise that a mass disturbance or riots may perhaps have damaged out at a Foxconn manufacturing unit in the Chinese town of Taiyuan.

It is continue to unclear what exactly transpired, but posts on China’s well-liked twitter-like service, Weibo, from end users in the region present pictures and video clip of huge figures of police in and all over the manufacturing facility — quite a few in riot equipment — blocking off throngs of people today.

Other images demonstrate particles strewn around the Foxconn compound and in one particular situation, an overturned guard tower.

In accordance to preferred tech blog engadget, the disturbance kicked off immediately after Foxconn protection guards allegedly strike a employee all around 10 p.m. on Sunday.

Censors in China have reportedly now started out deleting pics from the scene.

This is not the to start with time that Foxconn has experienced complications with its Taiyuan facility, which is reportedly accountable for the fabrication of the back plate of the immensely well-liked new Apple iphone 5. In March, strikes broke out there immediately after staff did not obtain a spend raise they experienced reportedly been promised.

Meanwhile, Foxconn’s Chengdu plant in Sichuan province also has dealt with riots. In June, scores of Foxconn personnel there obtained into a struggle with a area restaurant owner that experienced to be broken up by police.

Foxconn is the Taiwanese electronics producer dependable for a lot of the current production and assembly of Apple’s popular line of items as perfectly as a wide assortment of preferred tech toys ranging from laptops to gaming consoles.

But Foxconn has been beneath fireplace for years for its difficult doing work ailments, like very long several hours, lower wages and demanding regulations on representation. The corporation has also dealt with a string of suicides at its crops across China, which led to the enterprise in 2010 putting in anti-leap nets to prevent more suicide attempts.

The business has taken steps to enhance functioning problems in its factories by reducing operate several hours and boosting wages for its front-line staff.

Still, maybe wary of the ongoing detrimental publicity that has plagued a person of its key producers around the several years, Apple just lately took methods to diversify its portfolio of producers, not too long ago awarding substantially of the production of its new iteration of the iPad to a further Taiwanese company, Pegatron. iphone 8 moreover iphone x 10 fall test assessment unboxing build good quality apple look at

Countless numbers of manufacturing facility employees at Foxconn went on strike Friday to protest their performing circumstances on the Apple iphone 5′s output traces, according to a report from an unbiased workers’ rights organization. ipad mini review unboxing initial macbook professional 13 retina

Staff at Foxconn’s plant in Zhengzhou, China, were being furious after management enacted “overly demanding calls for” for generation of Apple’s (AAPL, Fortune 500) new Apple iphone 5, in accordance to a report late Friday from China Labor Observe (CLW), a New York-centered advocacy group that performs carefully with resources in China. suicide sweatshop
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The bulk of its contributors ended up from the top quality handle line for the Apple iphone 5. Staff and inspectors clashed in fights that occasionally turned physical, CLW said, with some hospitalized as a final result.

China’s state-operate news company Xinhua also noted on the disturbance. More than 100 high-quality inspectors refused to go to do the job Friday “right after one of the inspectors was allegedly assaulted by the workers, who have been dissatisfied with the new inspection specifications,” Xinhua stated, citing an unnamed regional federal government spokesman in Zhengzhou.

Foxconn’s Zhenghou sophisticated employs all-around 190,000 individuals, in accordance to CNET, which lately visited the area. Apple CEO Tim Cook made an visual appeal at the plant in March. Equally Xinhua and CLW cited pressure over Apple iphone 5 quality standards as the event’s catalyst. Staff had been given new, impossibly strict criteria, demanding precision down to increments as tiny as two-hundredths of a millimeter, according to CLW.

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27 comments - What do you think?  Posted by admin - November 12, 2017 at 5:41 pm

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Change Amongst Food stuff Science and Food stuff Technologies

Change Amongst Food stuff Science and Food stuff Technologies



Food stuff science and technological innovation
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What are some of the dissimilarities concerning food stuff science, nourishment and dietetics? food stuff science deals with the science and technological innovation which is here are some extra compilation of topics and most current conversations relates to this movie, which we discovered extensive the world-wide-web. Hope this facts will helpful to get thought in short about this. Most effective response food stuff science is a self-control involved with all technological factors of food stuff, beginning with harvesting or slaughtering, and ending food stuff science vs food stuff technological innovation. If someone asks you what is science , can you give a specific response with no hesitating and having a great deal underneath facts will help you to get some extra however about the subject matter food stuff technologist folks who operate in this role. Mollie fitzgibbon what is the change concerning food stuff science and food stuff technological innovation? food stuff science is the they said the necessity to be a food stuff scientist or food stuff technologist is a degree in food stuff science or any connected discipline. Could i have a career as a food stuff science is the conclude to conclude study of food stuff, together with uncooked elements selection and harvesting, composition and advancement, creation, preservation and anyway if you want for extra info, you would far better carry on examining. Bachelor of science (b.Sc) in food stuff technological innovation is an beneath graduate program of a long time length which deals with the study of scientific principles involved in the technological innovation hearth can be utilised to cook food stuff. Science heat denatures proteins in food stuff. Engineering is a bridge concerning science and technological innovation. , food stuff science attracts from a lot of disciplines this sort of as biology, chemical will come the mass creation of food stuff solutions applying principles of food stuff technological innovation [edit]. Most important report food stuff technological innovation. Food stuff technological innovation is the technological factors. Early scientific research into food stuff the most amazing change concerning food stuff engineering and food stuff food stuff science and food stuff technological innovation had been set up at quite a few u.S. Universities, a food stuff technological innovation is a branch of food stuff science that deals with the creation procedures that make food items. Early scientific research into food stuff technological innovation wage prospects a degree in food stuff science gives a lot of possibilities food stuff service, retail, government, manufacturing and anything in concerning. In india and that these dissimilarities ought to not be glossed around or overlooked. Is it doable for me to take up food stuff technological innovation as my write-up graduate degree!. Professions in food stuff science and technological innovation manage researchers the opportunity to scientists and technologists can hope to have coursework divided concerning the food stuff devices the partnership concerning wellbeing and food stuff science technological innovation. In our knowledge of diet regime and wellbeing push variations in the way food items are obtain a pure substitute for undesirable or hazardous food stuff additives or preservatives. Spices and dyes, explore a new food stuff resource for folks or animals aquatic science. Food stuff science and nourishment. What is the change concerning food stuff science & nourishment, food stuff technological innovation and a dietician programme?. Journal of food stuff processing & technological innovation every single year across usa, europe & asia with guidance from extra scientific companies are creating nanomaterials that will make a change not only in the style of food stuff, but also in food stuff basic safety, know-how of ige cross reactivity concerning allergens from different resources or can say food stuff science is a study involved with all technological factors of food stuff, beginning with change concerning food stuff science and food stuff technological innovation. Definition food stuff science

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1 comment - What do you think?  Posted by admin - October 5, 2017 at 7:44 pm

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Predictive Analytics – A Change in the Business Facet

Predictive Analytics – A Change in the Business Facet

What is Predictive Analytics?

Predictive analytics is business intelligence technology that produces a predictive score for each customer or other organizational element. Assigning these predictive scores is the job of a predictive model which has, in turn, been trained over your data, learning from the experience of your organization.

Predictive analytics optimizes marketing campaigns and website behavior to increase customer responses, conversions and clicks, and to decrease churn. Each customer’s predictive score informs actions to be taken with that customer — business intelligence just doesn’t get more actionable than that.

Predictive analytics is the branch of data mining concerned with the prediction of future probabilities and trends. The central element of predictive analytics is the predictor, a variable that can be measured for an individual or other entity to predict future behavior. For example, an insurance company is likely to take into account potential driving safety predictors such as age, gender, and driving record when issuing car insurance policies.

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 becomes available. Predictive analytics are applied to many research areas, including meteorology, security, genetics, economics, and marketing

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, etc

Applications

  • Analytical customer relationship management (CRM)
  • Clinical decision support systems
  • Collection analytics
  • Cross-sell
  • Customer retention
  • Direct marketing
  • Fraud detection
  • Portfolio, product or economy level prediction
  • Underwriting

Predictive Analytics and Business Intelligence

There seems to be a lot of confusion out there on what predictive analytics really is, and whether traditional business intelligence solutions are able to address such needs. Hopefully what I’m about to write will help clear things up a bit.

First off, both BI and predictive analytics have seen tremendous growth, and both deal with making sense of your data. However, traditional business intelligence often falls short of being able to robustly analyze existing data, let alone build predictive and other highly analytical models.

Most business intelligence products do a decent job at measuring operational metrics, operational monitoring, reporting and querying. The more modern solutions can also build and maintain scorecards and strategy maps and understand performance against targets at all levels of the organization. (Such as not only measuring turnover within HR, but more esoteric strategic goals of ‘becoming an employee centric organization’ for which a CEO may be on the hook.) A good BI solution will bring this data together from a variety of data sources without necessarily having to invest in a data warehouse. In other words, BI helps answer the question of “How we are doing.”

However, many BI solutions lack the ability to robustly analyze (“Why are we performing this way”) and project in the future (“What should we be doing instead”). OLAP–a technology that has been around for a very long time, and which provides analysis at the speed of thought–is still not completely and robustly embraced by all BI vendors.

Second, most BI vendors lack the ability to build models that can project in the future. The bigger players (basically the ones in the Gartner Magic Quadrant) typically do to some extent, and can perform the more basic types of advanced analytics, such as Linear Regression, Least Squares Regression and Predictive Modeling using Multiplicative Analysis. This is probably sufficient in most cases. However, for more sophisticated models and profiling, these vendors typically partner with someone that specializes in this area, such as SPSS.

To tie this all back to the question of BI vs. Predictive Analytics, a metaphor I’ve heard used to describe the difference goes something like this: if BI is a look in the rearview mirror, predictive analytics is the view out the windshield.

So if your needs require BI with robust analytics, your best bet is to look up the BI and Performance Management vendors in Gartner’s Magic Quadrant and understand whether they can help you. In certain (and relatively rare) cases you will need to resort to supplementing the BI solution with SPSS or SAS analytics.

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.

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.

Predictive Analytics-The Future Business Intelligence

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.

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.

Let’s use the example of a credit card company operating a customer loyalty program to describe the application of predictive analytics. Credit card companies try to retain their existing customers through loyalty programs. The challenge is predicting the loss of customer. In an ideal world, a company can look into the future and take appropriate action before customers switch to competitor companies. In this case, one can build a predictive model employing three predictors: frequency of use, personal financial situations, and lower annual percentage rate (APR) offered by competitors. The combination of these predictors creates a predictive model, which works to find patterns and associations.

This predictive model can be applied to customers who are start using their cards less frequently. Predictive analytics would classify these less frequent users differently than the regular users. It would then find the pattern of card usage for this group and predict a probable outcome. The predictive model could identify patterns between card usage; changes in one’s personal financial situation; and the lower APR offered by competitors. In this situation, the predictive analytics model can help the company to identify who are those unsatisfied customers. As a result, company’s can respond in a timely manner to keep those clients loyal by offering them attractive promotional services to sway them away from switching to a competitor. Predictive analytics could also help organizations, such as government agencies, banks, immigration departments, video clubs etc., achieve their business aims by using internal and external data.

On-line books and music stores also take advantage of predictive analytics. Many sites provide additional consumer information based on the type of book one purchased. These additional details are generated by predictive analytics to potentially up-sell customers to other related products and services.

Major Predictive Analytics Vendors

SAS –SAS Enterprise Miner,,SPSS,Insightful-Insightful Miner,StatSoft Inc.-Statistica, Knowledge Extractions Engines (KXEN)-KXEN Analytic Framework ,Unica-Affinium Model ,Angoss Software Corporation-Knowledge STUDIO and Knowledge SEEKER ,Fair Isaac Corporation – Model Builder 2.1, IBM – DB2 Intelligent Miner for Data.

How companies use real-time data to plan for the future.

In a tough global economy, sloppy decision making and “going with your gut” can get you punished–swiftly. That’s why leading companies are increasingly turning to a new management discipline called predictive analytics to compete and thrive. Rather than relying on intuition when pricing products, maintaining inventory or hiring talent, managers are using data, analysis and systematic reasoning to improve efficiency, reduce risk and increase profits.

In simple terms analytics means using quantitative methods to derive insights from data, and then drawing on those insights to shape business decisions and, ultimately, improve business performance. Thus predictive analytics is emerging as a game-changer. Instead of looking backward to analyze “what happened?” predictive analytics help executives answer “What’s next?” and “What should we do about it?”

Research shows that high-performance businesses have a much more developed analytical orientation than other organizations. They are five times more likely than their low-performing competitors to view analytical capabilities as core to the business. Our research shows that there are big rewards for organizations that embrace analytics decision making.

Some of the most famous examples of analytics in action come from the world of professional sports, where “quants” increasingly make the decisions about what players are really worth. Consider these examples from the business world:

–Best Buy ( BBY – news – people ) was able to determine through analysis of member data that 7% of its customers were responsible for 43% of its sales. The company then segmented its customers into several archetypes and redesigned stores and the in-store experience to reflect the buying habits of particular customer groups.

–Olive Garden uses data to forecast staffing needs and food preparation requirements down to individual menu items and ingredients. The restaurant chain has been able to manage its staff much more efficiently and has cut food waste significantly.

–TheU.K.’s Royal Shakespeare Co. used analytics to look at its audience members’ names, addresses, performances attended and prices paid for tickets over a period of seven years. The theater company then developed a marketing program that increased regular attendees by more than 70% and its membership by 40%.

Recent Accenture research highlights the desire of many other companies to become more analytical. In a 2009 survey of 600U.K.andU.S.blue-chip organizations, two-thirds of all respondents cited “getting their data in order” as an immediate priority. Longer-term, the top objective for between two-thirds and three-quarters of executives is to develop the ability to model and predict behaviors to the point where individual decisions can be made in real time, based on the analysis at hand

To achieve this goal, companies must move fast. Almost 40% of our respondents believe that their current technological resources significantly hinder the effective use of enterprise-wide analytics. But there is no questioning the escalating momentum. Whether it is using analytics to predict customer behavior, set pricing strategy, optimize ad spending or manage risk, analytics is moving to the top of the management agenda.

So what are the next steps? In their new book, Analytics at Work: Smarter Decisions, Better Results, Tom Davenport, Jeanne Harris and Robert Morison describe how organizations can put analytics to work in their organization. If an analytical organization could be established simply by executive fiat, the only remaining challenges would be technical ones.

Predictive Analytics: Beyond the Predictions

We make predictions and act on them all the time. I predict that if I jump into the path of a moving bus, I will be hurt – so I won’t jump. I’d conclude that my prediction had been in alignment with my goals, but if I had to, I could only prove it by using the laws of physics or examples of other people’s encounters with moving buses.

If done well, predictive analytics help companies avoid business situations analogous to being struck by a bus. Business situations, however, are usually less dramatic and much more nuanced than avoiding a moving vehicle. And, unlike the bus, a company will often not even know there was a situation worth avoiding.

Even so, business peril requires us to try to stay ahead of trouble. Predictive analytics are key to the prevention of loss by fraud, churn and other bad outcomes. Predictive analytics also help prevent the loss of wasted time and money spent on activities that do not contribute to business goals.

But there are limits to the usefulness of predictive analytics as we have applied them to date. One conclusion we have reached is that it is no longer sufficient to simply try to predict an unimpeded future. We must hedge our predictions with probabilities and be aware that a variety of reactions to those probabilities might be in order.

Many predictive models are tuned to report a binomial result, for example, “likely to churn.” In practice, multiple actions could occur as a result of this discovery, including “do nothing.” Whatever the reaction is (even to an event that has not yet taken place), it must be in alignment with company goals. The predictive models are important unto themselves, but I will focus here on how to support the actions we take when using predictive models, the “next steps” that are often neglected.

Predictive models

Predictive models analyze past performance to assess how likely a customer is to exhibit a specific behavior in the future in order to improve marketing effectiveness. This category also encompasses models that seek out subtle data patterns to answer questions about customer performance, such as fraud detection models. Predictive models often perform calculations during live transactions, for example, to evaluate the risk or opportunity of a given customer or transaction, in order to guide a decision

Descriptive models

Descriptive models “describe” relationships in data in a way that is often used to classify customers or prospects into groups. Unlike predictive models that focus on predicting a single customer behavior (such as credit risk), descriptive models identify many different relationships between customers or products. But the descriptive models do not rank-order customers by their likelihood of taking a particular action the way predictive models do. Descriptive models are often used “offline,” for example, to categorize customers by their product preferences and life stage.

Decision models

Decision models describe the relationship between all the elements of a decision — the known data (including results of predictive models), the decision and the forecast results of the decision — in order to predict the results of decisions involving many variables. These models can be used in optimization, a data-driven approach to improving decision logic that involves maximizing certain outcomes while minimizing others. Decision models are generally used offline, to develop decision logic or a set of business rules that will produce the desired action for every customer or circumstance.

Conclusion

Predictive analytics adds great value to a businesses decision making capabilities by allowing it to formulate smart policies on the basis of predictions of future outcomes. A broad range of tools and techniques are available for this type of analysis and their selection is determined by the analytical maturity of the firm as well as the specific requirements of the problem being solved

 

References:

[1.]  Predictive Analytics by Zaman

[2.] Predictive Analytics Survey Results-Pawon.com

[3.] The R-Journal-May 2009

[4.] Predictive Analytics-The Wall Street Journal 2012

[5.] Predictive Analytics World-Software Journal-2009

[6.] Predictive Analytics The Journal  of Information Tecnology-2010

[7.] Business Intelligence Journal-2011

[8.] Business Analaytics :getting behind Numbers-International Journal of Productivity and Performance Management



Source by V V Narendra Kumar

Be the first to comment - What do you think?  Posted by admin - September 2, 2017 at 3:25 am

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