The Age Of Infancy And Early Childhood - 1358 Words

Beginning with the first years of life the early influences of biological and social clocks how children develop and how they gain confidence and curiosity are exposed. The age of Infancy and early childhood we don’t need lot stuff for them. The Story starts in the countryside in Pennsylvania a family farm a story starts beginnings with a simple, wondrous story on this family farm a story where everyone is awaiting a child. It’s the story of human Development that what the season of life is all about. When we look a long period of time over a lifetime, we see a change in our lifestyle. We are taking what is called a life-span perspective, it an exciting way to look at our lives from a different angle. This Movie talks about growth and†¦show more content†¦Justin was born, he was 6lb 30z everyone has come and visits him. His great grandfather is the keeper of all the stories. He is the one that tells the family stories when they meet up together. He is the one t hat tell Justin all the crazy stuff he did to his parents and him growing up. Have a child so small is hard because she is not going to be able to do the work she did before. Having a child is a hand full and a lot of responsililtes. Fiscal policy uses changes in taxes and government spending to affect overall spending and stabilize the economy. When lowering taxes the people have more to spend than the government decreases spending and the economy slows down therefore the economy stabilizes. The objective of fiscal policy is the governments’ typical use fiscal policy to promote strong and sustainable growth and reduce poverty. Their family was making money out of there farm. Family stories are very go because they tell us who we are as a family what we think about natural and the way life is. It likes a social; womb that our child is in. Justin looks just like his father in his picture, but his picture was black and white. He looks like his father, but does he grow up to be like his father a farmer. We all have different roads in our life sometimes our kids don’t like the stuff we do and they have a passion for something else. It hard to make our children follow the same footsteps as we did with our parents. There areShow MoreRelatedPlay Time : A Child s Work1135 Words   |  5 Pageschild’s development in early childhood. Play is a child’s work, it is how they begin to learn and grow intellectually, socially, and emotionally at a young age. In addition, play-time also helps introduce and initiate proper motor skills and cognitive thinking. Play-time involving mother and child is equally important because it is helping build the connection between parent and child. Without play-time, a child can be deprived of the most important aspect of his/her childhood and may grow up withoutRead MoreThe Birth Weight Of A Newborn1261 Words   |  6 Pagestime they reach six months old and triple at the end of their first year of life. While w eight is steadily increasing there is also an expansion of the babies head and chest as internal organs such as the brain, heart, and lungs develop as well. (Infancy, 2016) On average the head of a newborn makes up about 25% of their total length, which is the height. This can easily be seen by the fact that newborns have extreme difficulty raising their heads. In line with head of a newborn is the physical growthRead More7 Stages of Development1002 Words   |  5 PagesThese stages include infancy, early childhood, middle childhood, adolescence, early adulthood, middle adulthood and old age. Infancy is recognized as the stage of life from a human s birth up until he or she learns how to speak: generally until the age of one or two. During this stage, the child transitions from a dependent toddler to a relatively active child; he or she is typically able to crawl, roll over and walk. In terms of physical development, the stage of infancy witnesses the most growthRead MoreThe Formation Of Secure Attachments With The Primary Caregiver1616 Words   |  7 PagesAttachment from infancy to childhood). Yet, attachments, in this case insecure ones, are not the only reason as to why we develop into the individuals we are. There are other causes that are unrelated to attachment that explain our later development. In this discussion, the primary caregiver will be the mother. Attachment is considered vital for later life in terms of social development. Evidence for this was provided by Waters et al. (1979); children who were securely attached in their infancy years wereRead MoreDeveloping A Healthy Attachment For Children1087 Words   |  5 Pageschildren. Even though children were around him, they were restricted of any interaction. He did not learn how to speak until he was illegally trafficked into the United States at the age of seven, and that was very limited. The purpose of this case study is to determine the factors during infancy and early childhood that prevented him in developing a healthy attachment. According to Joan Greg Cook (2007), attachment can be defined by, â€Å"An emotional tie to a specific other person or people thatRead MoreThe Impact Of Sensory Processing Disorder On Development And Development1500 Words   |  6 Pagesdivided into eight different developmental periods. This essay will focus on the early childhood period that ranges from two to five or six years of age. Firstly, this essay will describe the typical developmental milestones during early childhood and how they play an important role in each developmental stage that follows. Secondly, it will analyse how the home and educational environments influence early childhood. Finally, it will define Auditory Processing Disorder and the characteristics ofRead MoreStudy Analysis : Breast Feeding Vs. Formula Feeding And Overweight Infants1183 Words   |  5 PagesSame Risk of Childhood Overweight as Formula Fed Overweight Infants, investigates whether exclusively breastfed overweight infants have the same risks of becoming overweight in childhood as overweight infants who are formula fed. This study found that exclusively breastfed infants who are overweight encounter the same risk of becoming overweight in childhood as infants who are overweight and formula fed. Therefore, researchers emphasize the importance of preventing overweight in infancy regardlessRead MoreChildhood Development Essay946 Words   |  4 PagesChildhood begins a new era of development, one that is filled with exploration and a new understanding of the world. Children are finally beginning to understand aspects of their environment that they were unable to comprehend during infancy. While development during childhood occurs less rapidly than that which is experienced during infancy, there are still many major changes that children go through during this time. During childhood, children experience physical and cognitive growth, create newRead MoreThe Role Of Nature And Nurture Affect Development Of A Child, Conce ption, And The Contemporary Theories1690 Words   |  7 Pagesconcepts have been discussed in the first nine chapters. These include, the theories in the study of human development, the question of whether nature and nurture affect the development of a child, conception, pre-birth and birth, infancy, early childhood and middle childhood. Theories in the Study of Human Development Human development has been explained using theories such as the classical and the contemporary theories. Contemporary theories include the sociocultural perspective and the ecologicalRead MoreThe Persistence Of Temperament And Personality1469 Words   |  6 Pagesresults a person’s personality it does not mean personality is unchangeable. The Persistence of Temperament and Personality According to data collected in 2013 by The National Institute of Mental Health ,forty-eight million adults of age eighteen and older were reported to suffer from some kind of mental illness and were not aware of it , therefore left untreated (2013, NIMH). The importance of that statistic is to illuminate how many people could have led better lives and had better

Ambition In William Shakespeares Macbeth And Banquo

There are many quotations regarding ambitious individuals and how it leads to their success. That’s because, for the most part as seen in our class demonstration, individuals view ambition in a positive aspect. In the play Macbeth, however, ambition carries quite a different connotation. Ambition by itself is not seen as dangerous in the play, but when it is paired with being proactive, these traits lead to deadly success. Being proactive is one of the seven habits of highly effective people, and is in direct contrast to reactive people. Whereas proactive people are constantly working to expand themselves, reactive people are waiting for circumstances to occur prior to displaying action. The characters of Macbeth, Lady Macbeth, and†¦show more content†¦However, Macbeth’s greediness turns his subjects against him with one saying, â€Å"This tyrant, whose sole name blisters our tongues, /Was once thought honest† (Act 4, Scene 3, Lines 14-15). Macbethâ₠¬â„¢s ambition to remain King and his actions toward that goal, such as killing Banquo, lead the people of Scotland to profess him a tyrant and seeking his death. Utilitarianism is happiness for the greatest people; however, Macbeth uses his ambition and actions to advance his title for personal victory, rather than focusing on the needs of his people. Lady Macbeth was not pleased with the means in which personal immoral ambition and proactivity lead to her success. Lady Macbeth, after reading the note from her husband regarding the witch’s predictions, had an intense wish to have Macbeth as King of Cumberland―with or without his aid. She, therefore, convinces Macbeth to kill King Duncan, and he obliges her demands. However, when he is too afraid to go back to the scene of the crime to place daggers by the servants, Lady Macbeth says â€Å"Infirm of purpose! /Give me the daggers† (Act 2, scene 2, Lines 68-69). It was Lady Macbeth who created a plot and strategized the murder of King Duncan, as she anticipated a delay in Macbeth devising a scheme of his own. She was being proactive while constantly attempting to convince Macbeth to kill Duncan and provided him with aShow MoreRelatedTaylor Travis . Mr. Ortiz. English 12. 27 February 2017.1321 Words   |  6 PagesTaylor Travis Mr. Ortiz English 12 27 February 2017 Unit 3: Comparison Essay Both Macbeth and Throne of Blood illustrate the negativity associated with extreme ambition and desire for power. The two titles tell the story of a greatly respected warrior and his wife and their eventual downfall after pursuing a higher position of power. Shakespeare’s play, Macbeth, and Akira Kurosawa’s movie, Throne of Blood, share many similar aspects, ranging from plot to characters to setting. However, the twoRead MoreLuis Sotelo. Mr. Ortiz. English 12. 27 February 2017. Macbeth1258 Words   |  6 PagesLuis Sotelo Mr. Ortiz English 12 27 February 2017 Macbeth vs Throne of Blood In â€Å"Tthe Tragedy of Macbeth† by William Shakespeare, there is a big focus on the character itself instead of the plot of the play. Each character is of high importance to the tragedy and the developing plot that leads to Macbeth’s downfall. Macbeth, Lady Macbeth, Banquo, and even minor characters such as the witches all mold and shape Macbeth’s fate and make it a tragic one. In the Throne of Blood (1957) by Akira KurosawaRead MoreMacbeth Critique1404 Words   |  6 PagesA critique on the main character in William Shakespeares Macbeth. So foul and fair a day I have not seen. This is a famous quote by Macbeth, the antagonist in William Shakespeares classic work, The Tragedy of Macbeth. This one line takes place when Macbeth and Banquo are returning from their victory in battle over the Norwegians. Following this quote further it could be looked at in a broader spectrum of Macbeths triumphs and failures. He goes from a warrior hero to a murderer, and lastly, hisRead MoreMacbeth Themes899 Words   |  4 Pages â€Å"What are the major themes in Macbeth† By Connor Maguire William Shakespeare’s Macbeth a play complete with many themes and viewpoints. The themes are exhibited by the main characters of the play, notably antagonist Macbeth. Themes seen in the play include ambition, where is is portrayed as both dangerous and unnatural. However, it does exist in both good and evil forms in the play. Another theme seen is whether Macbeths actions in the play are a result of fate, or free will. Although outsideRead MoreMacbeth Historical Context796 Words   |  4 PagesIn William Shakespeare’s Macbeth the male characters Macbeth, Macduff, Banquo and Duncan really give you and insight in to the time the play was written. Shakespeare’s Macbeth was written in the Elizabethan era during King James’ reign as King over 400 years ago. King Duncan is introduced to the play in act 1 scene 2. King Duncan is seen as a great, noble, highly thought of King. He is in The Monarch which is the highest in the social order. â€Å"Go pronounce his present death, And with his formerRead MoreMacbeth: The Tragic Hero Essay1026 Words   |  5 Pages In William Shakespeare’s Macbeth, Macbeth is a classic example of a tragic hero who is constantly struggling with his fate. In the opening scene of the play Macbeth receives a prophecy from three witches. They proclaim that he will be the thane of Cawdor. He responds by saying, â€Å"By Sinel’s death I know that I am thane of Glamis/ but how of Cawdor†(I, iii, 70-73)? At first, he does not realize to earn this title what he must do, but when he realizes he is taken aback. His bewilderment prefiguresRead MoreAmbition: a Path to Success or Failure?943 Words   |  4 PagesAmbition: a path to success or failure? William Shakespeare’s tragedy, Macbeth, is a play about a general from the King’s army whose ambition leads him to usurp the throne. Macbeth’s initial lie perpetuates him to commit numerous murders to ensure that the heir to the throne is still within his reach. The play highlights a common value held by our society which is that we are responsible for our actions. Although Lady Macbeth initially provoked Macbeth, ultimately, his demise was a result of hisRead MoreMacbeth Appearance Vs Reality1245 Words   |  5 PagesAn Exploration of Appearance and Reality in Macbeth As Plato famously said in Phaedrus, â€Å"Things are not always as they seem†, meaning that not everything is what it appears to be. In today’s society, many politicians appear trustworthy and are in reality crooked. Photoshop also manipulates appearance and reality. In literature, authors use appearance versus reality to create an interesting plot or characterize. In William Shakespeare’s tragedy, Macbeth, the theme of appearance versus reality is seenRead MoreA Comparison of Shakespeares Macbeth and Rupert Goolds Film Adaptation 954 Words   |  4 PagesWilliam Shakespeare’s masterpiece, Macbeth, is a tragedy brilliantly brought to the 21st Century by Rupert Goold. Although Shakespeare’s Macbeth is a play set in 16th Century Scotland, Rupert Goold modernizes the play by changing the setting to a Soviet-styled country and implementing modern elements into the characters and theme. Although Shakespeare’s Macbeth and Rupert Goold’s f ilm adaptation share many ideologies and a general storyline, a difference exists in the setting, the characters, andRead More Destructive Ambition in Shakespeares Macbeth Essays1671 Words   |  7 PagesDestructive Ambition in Macbeth      Ã‚  Ã‚   William Shakespeares tragic play Macbeth presents the fizzled drive of an ambitious husband and wife. This essay is the story of their destructive ambition.    Fanny Kemble in Lady Macbeth refers to the ambition of Lady Macbeth:      [. . .] to have seen Banquos ghost at the banqueting table ... and persisted in her fierce mocking of her husbands terror would have been impossible to human nature. The hypothesis makes Lady Macbeth a monster

Predictive Analytics the Future of Business Intelligence Free Essays

string(142) " into a targeted database; for example, it pulls data from source or legacy system and loading data into standard database or data warehouse\." 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. We will write a custom essay sample on Predictive Analytics: the Future of Business Intelligence or any similar topic only for you Order Now 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. 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. 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. You read "Predictive Analytics: the Future of Business Intelligence" in category "Papers" 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 Some vendors have been in the predictive analytical tools sector for decades; others have recently emerged. This section will briefly discuss the capabilities of key vendors in predictive analytics. SAS SAS is one of the leaders in predictive analytics. Though it is a latecomer to BI, SAS started making tools for statistical analysis at least thirty years prior, which has helped it to move into data mining and create predictive analytic tools. Its application, SAS Enterprise Miner, streamlines the entire data mining process from data access to model deployment by supporting all necessary tasks within a single, integrated solution. Delivered as a distributed client-server system, it is well suited for data mining in large organizations. SAS provides financial, forecasting, and statistical analysis tools critical for problem-solving and competitive agility. SAS is geared towards power users, and is difficult to learn. Additionally, in terms of real-time analytics, building dashboards and scorecards, SAS is a laggard compared to competitors like Cognos, Business Objects, and Hyperion; however, its niche product in data mining and predictive analytics has made it stand out of the crowd. SPSS SPSS Inc. is another leader in providing predictive analytics software and solutions. Founded in 1968, SPSS has a long history of creating programs for statistical analysis in social sciences. SPSS today is known more as a predictive analytics software developer than statistical analysis software. SPSS has played a thought-leadership role in the emergence of predictive analytics, showcasing predictive analytics as an important, distinct segment within the broader business intelligence software sector. SPSS performs almost all general statistical analyses (regression, logistic regression, survival analysis, analysis of variance, factor analysis, and multivariate nalysis) and now has a full set of data mining and predictive analytical tools. Though the program comes in modules, it is necessary to have the SPSS Base System in order to fully benefit from the product. SPSS focuses on ease; thus beginners enjoy it, while power users may quickly outgrow it. SPSS is strong in the area of graphics, and weak in more cutting edge statistical procedures and lacks robust methods a nd survey methods. The latest SPSS 14. 0 release has improved links to third-party data sources and programming languages. Insightful Along similar lines is Insightful Corporation, a supplier of software and services for statistical data analysis, data mining of numeric, and text data. It delivers software and solutions for predictive analytics and provides enterprises with scalable data analysis solutions that drive better decisions by revealing patterns, trends, and relationships. Insightful’s S-PLUS 7, is a standard software platform for statistical data analysis and predictive analytics. Designed with an open architecture and flexible interfaces, S-PLUS 7 is an ideal platform for integrating advanced statistical techniques into existing business processes. Another tool offered by Insightful is Insightful Miner, a data mining tool. Its ability to scale to large data sets in an accessible manner in one of its strengths. Insightful Miner is also a good tool for data import/export, data exploration, and data cleansing tasks, and its reduces dimensionality prior to modeling. While it has powerful reporting and modeling capabilities, it has relatively low levels of automation StatSoft Inc. StatSoft, Inc. is a global provider of analytic software. Its flagship product is Statistica, a suite of analytics software products. Statistica provides comprehensive array of data analysis, data management, data visualization and data mining procedures. Its features include the wide selection of predictive modeling, clustering, classification and exploratory techniques made available in one software platform. Because of its open architecture, it is highly customizable and can be tailored to meet very specific and demanding analysis requirements. Statistica has a relatively easy to use graphical programming user interface, and provides tools for all common data mining tasks; however, its charts are not easily available for the evaluation of neural net models. Statistica Data Miner another solution that offers a collection comprehensive data mining solutions. It is one of two suites that provides a support vector machine (SVM), which provides the framework for modeling learning algorithms. Knowledge Extractions Engines (KXEN) Knowledge Extraction Engines (KXEN) is the other vendor that provides a suite that includes SVM. KXEN is a global provider of business analytics software. Its self-named tool, KXEN provides (SVM) and merges the fields of machine learning and statistics. KXEN Analytic Framework is a suite of predictive and descriptive modeling engines that create analytic models. It places the latest data mining technology within reach of business decision makers and data mining professionals. The key components of KXEN are robust regression, smart segmenter, time series, association rules, support vector machine, consistent coder, sequence coder, model export, and event log. One can embed the KXEN data mining tool into existing enterprise applications and business processes. No advanced technical knowledge is required to create and deploy models and KXEN is highly accurate data mining tool and it is almost fully automatic. However, one record must be submitted for every entity that must be modeled, and this record must contain a clean data set. Unica Affinium Model is Unica’s data mining tool. It is used for response modeling to understand and anticipate customer behavior. Unica is enterprise marketing management (EMM) software vendor and Affinium Model is a core component of the market-leading Affinium EMM software suite. The software empowers marketing professionals to recognize and predict customer behaviors and preferences—and use that information to develop relevant, profitable, and customer-focused marketing strategies and interactions. The automatic operation of the modeling engine shields the user from many data mining operations that must be manually performed by users of other packages, including a choice of algorithms. Affinium is an easy to use response modeling product on the market and is suitable for the non-data miner or statistician, who lacks statistical and graphical knowledge. New variables can be derived in the spreadsheet with a rich set of macro functions; however, the solution lacks data exploration tools and data preparation functions. Angoss Software Corporation Another leading provider of data mining and predictive analytics tools is Angoss Software Corporation. Its products provide information on customer behavior and marketing initiatives to help in the development of business strategies. Main products include KnowledgeSTUDIO and KnowledgeSEEKER, which are data mining and predictive analytics tools. The company also offers customized training to its clients, who are primarily in the financial services industry. Angoss developed industry specific predictive analytics software like Angoss Expands FundGuard, Angoss Telecom Marketing Analytics, and Angoss Claims Payments Analytics. Apart from financial industry Angoss software is used by telecom, life sciences, and retail organizations. Fair Isaac Corporation Along similar lines, Fair Isaac Corporation is the leading provider of credit scoring systems. The firm offers statistics-based predictive tools for the consumer credit industry. Model Builder 2. 1 addresses predictive analytics, and is an advanced modeling platform specifically designed to jump-start the predictive modeling process, enabling rapid development, and deployment of predictive models into enterprise-class decision applications. Fair Isaac’s analytic and decision-management products and services are used around the world, and include applicant scoring for insurers, and financial risk and database management products for financial concerns. IBM Not to be left out, the world’s largest information and technology company, IBM also offers predictive analytics tools. DB2 Intelligent Miner for Data is a predictive analytical tool and can be used to gain new business insights and to harvest valuable business intelligence from enterprise data. Intelligent Miner for Data mines high-volume transaction data generated by point-of-sale, automatic transfer machine (ATM), credit card, call center, or e-commerce activities. It better equips an organization to make insightful decisions, whether the problem is how to develop more precisely targeted marketing campaigns, reduce customer attrition, or increase revenue generated by Internet shopping. The Intelligent Miner Scoring is built as an extension to the DB2 tool and works directly from the relational database. It accelerates the data mining process, resulting in the ability to make quicker decisions from a host of culled data. Additionally, because D2B Intelligent Miner Scoring is compatible with Oracle databases, companies no longer have to wait for Oracle to incorporate business intelligence capabilities into their database product. 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. The tools also have to be tested by the right experts. To understand and interpret predictive analytics results, one has to be knowledgeable about statistical modeling. One should look for the main functions and features of the tool and try to match them with their main requirements, as well as measure the trade off between functionality and cost. For example, some functionalities might be more important for some companies and less important for others. Buyers should also beware. Although marketing campaigns for predictive analytics solutions claim †ease of use†, these tools are not for beginners. 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. Nonetheless, 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 your BI strategy. About the author Mukhles Zaman has more than twenty five years experience in the IT industry specializing in business intelligence (BI), customer relationship management (CRM), project management, database design, and reporting software. He is a leading BI expert and has worked as a senior project manager on IT projects for Fortune 1000 companies in India, the Middle East, US, and Canada. He has also developed call center systems, software architecture, and portfolio management systems. He holds an MA in Economics, and a BA in Economics and Statistics from the University of Dhaka and is an Oracle Certified Professional. He can be reached at mukhleszaman@yahoo. com. How to cite Predictive Analytics: the Future of Business Intelligence, Papers