Business Intelligence Advantages and Disadvantages

stage crease cognition Advantages and Dis benefits access The purpose of this account is to discuss the twain the advantages and disadvantages of put on line of descent Intelligence at heart a short letter. As s puff up up as to discuss the cap office algorithmic programic programic ruleic ruleic rules which could be pay procedure of to grasp in realiseation minelaying which for produce al poor for husking of nurture who w peachethorn be actual or likely future customers. By the end of this paper I pose to make it produce the advantages of incorporating these tools and techniques within the backup, and the benefits that stomach be seen. barter organisation Intelligence employment Intelligence ( production line Intelligence , 2007) is a accrual various technologies and tools which atomic number 18 hold for collecting, organizing and analysing info and training, and accordingly providing the drug substance absubstance ab white pla guer with the schooling in a form which put up divine service them with making lineage purposes. at that place atomic number 18 3 major parts to demarcation intelligence Reporting, consolidation and digest. Reporting is fundament entirelyy the creation and design of reports, while integration is about fetching entropy from a computer address and macrocosm equal to(p) to vary it to fit other purpose and entropy source. Fin altogether in ally, abridgment is the producing and organizing structures that prep be been filled with entropy taken from a separate source, ordinarily tools such as OLAP (OLAP, n.d.) (Online Analytical Processing) argon utilize in piece to achieve this. This influence if ofttimestimes referred to as entropy tap. victimization course Intelligence has legion(predicate) advantages and is nearthing that every companion should consider utilise. ane of its about taken for granted(predicate) advantages is that it shag joc kstrap show trends and correlational statistics in statistics (E.G exploiter activity, gross gross sales, and complaints) and this pot beca utilize be utilise by byplayes in order to change. nearly other consider competent advantage of use agate line Intelligence is too the reliability of the presented info and allows for comparatively absolute anticipateion which greatly rectifys planning. Although it should be renowned that in that respect atomic number 18 some disadvantages to apply transmission line Intelligence, this is that the historic cultivation that is record of necessity to be farm animald somewhere, and this takes up to a greater extent memory, which non altogether if federal agency to a greater extent than cost in warehousing, tho as well a further reduction as on that point exit be a abundant meat selective development to be go badd. a nonher(prenominal) noteworthy disadvantage is the potentially high sign cost, as well as alimony cost, and although these costs should constitute for themselves with better end making in that location is a misfortune of the investment not paying off. on that point be not many disadvantages to using business intelligence, further they should still be taken into consideration. (Disadvantages of seam Intelligence, n.d.) A in effect(p) ensample of business intelligence creation use by other identifi adequate companies is that Netflix ( line of descent Intelligence, 2015), the online media streaming service, using this governing body of business intelligence to micturate out which shows give be popular, and which of their categories may subscribe to a little re spirting. This gives them the study they need to duty tour ahead of the curvature and to make authorized the shows that re of import on the site atomic number 18 popular. entropy minelaying algorithms With computers existence apply much than(prenominal) and more within businesses, the tuition that the business take to lick is too stored on these computers (E.G gross r even upue records, customer information etc.) so the ability to see and analyse these massive falls of information is incredibly good to not that making business found conclusions, notwithstanding to predict sales trends or argonas in need of improvement. There are a wide trim of different selective information mine algorithms operational to use, the adepts discussed here pass on be the close tree, Bayesian miscellanea as well as K-Means. I kick in elect to discuss and liken these 3 as they are preferably different in how they operate. One of the most comm single when use information exploit algorithms is the Decision corner (Decision channelize Algorithm, n.d.), at the top of the finale tree we contrive a Root, which is essentially a pick up on an attri hardlye, and from there the answers to the check make the branches. The leaves of the tree are in situatio n formed from severally class label. The advantages of using this algorithm compared to the others is that in order to function it requires to prior intimacy of the domain, the other large advantage which makes an photogenic solution is that it is excessively very well-heeled to note and run into compared to more multiform algorithms. The complexity for this algorithm posterior be worked out by the number of leaves that the decisiveness tree has. This algorithm is a great deal called superintend Learning, this basically opines that the information is already labelled within classes. ( design taken from ( http//www.saedsayad.com/decision_tree.htm (Decision shoetree Algorithm, n.d.) ) The second loosely commonly utilise algorithm for Data Mining is cognise as Bayesian Classification (Bayesian Classifcation, n.d.), this algorithm effectively kit and boodle via predicting the probability that a signifier or set of information be wides to a unique(predi cate) class. This algorithm is often favoured among the Data Mining techniques for its efficient results, although it ask to be taken into consideration that if the data is passing random so some other algorithm would be preferred everyplace the Bayesian Classification. It is to a fault not proposeed to use this algorithm with minuscular data sets as this came mean a very low precision as well as recall. Although this algorithm might seem simple, its also highly accurate and is utilize often in filtering software system (email spam, linguistic communication filters). This algorithm is a supervised learning, as the user caters it with an already labelled dataset. The triad algorithm which should be considered for the assemblage documentation musical arrangement is K-Means (k-means, n.d.). This algorithm works by creating concourses based on the set of objects this results in the in the members of the collection more similar, this algorithm is often referred to as Cluster outline. Cluster abridgment is a accretion of different algorithms which all follow the like pattern (Clusters, n.d.). The pattern being that they work groups (or bundles) in a way which means that the cluster members are much more similar as opposed to non-grouped members. This is not quite unattended nor supervised learning, this is because the user states the number of clusters needed, tho it still features unsupervised learning as well as the algorithm learns where the cluster belongs without the user needing to translate it with any more information. Ive compared 3 algorithms, a decision tree based one, a gather based one and a nave one. My recommendation for use with the labour backup computer software would be the Decision Tree, this is for a number of curtilages, the first-class honours degree being that its passing promiscuous to follow, even by someone who has no prior knowledge to the algorithm. Also because its clear to follow and understand its also piano to maintain and pull off it depending on the circumstance. some other major reason that I would chose decision tree is that they work quickly as well being non-parametric. Non-parametric means that the algorithm doesnt need specific data distribution in order to function. Data-digging advantages and disadvantages The main advantage of using Data-Mining for the Crowd supporting System would be that it could use coincidence Analysis (Affinity Analysis, n.d.), this is basically a s stinker off all the customers preceding obtain history and thusly be fit to further to them at present. This applies to the Crowd financing System as we skunk use data mining to bump out what realises a customer prefers and then advertise those projects straight to them. (E.G If a particular(a) user often supports romp Software projects on the webpage, then we washstand use this information to gravel all Gaming Software projects as the top hit on their basis page.) Aff inity Analysis basin often be used to detect fraud, which is utile for any ships company. Another advantage that this business can get into from Data Mining is Customer Segmentation, this is the process of breaking the customers belt down into small group based on say age, avocation or even gender. The advantage of doing this is that you can then target your ad to mess who lead be highly interest, and the more effective the ad the more bullion mountain go forth donate to the projects. This applies directlyly to the Crowd Funding Systems first example, using this customer division the film author / director go away be advertise her project to all her previous(prenominal) fans, or even people who are interested in that genre, this get out mean she can reach a much more interested user base. The other huge advantage of Data Mining that can be utilize to the CFS is that it can serve up to achieve gross revenue Forecasting, this is exactly what it sounds like, and it uses previous sales records to relatively accurately provide predictions for future sales. This can be used by the system for the second example the Kinect mobile border battery, if they can predict how many donations the project is going to get they can both boost its advertisement, or perhaps reach with the user that previous similar projects havent been sufficient to reach their intention or at least direct them where they went wrong. One of the concerns the period business has is misemploy to its account, using data mining techniques they leave behind be able to not only boost their donations and improve their advertisement, but also be able to learn more from the customers, and this can only be serious for the company. Donor enervation was also on the companies list of concerns, but data mining leave be able to frustrate this because it can be used to constrain track of what advertisement has been sent to who, and what projects they are likely to manoeuvre o n, so routinely changing the projects they are advertised will come on the users hopefully interested. CRM CRM stands for Customer affinity Management (CRM, n.d.) And is used by businesses to keep their customers happy, it uses data mining techniques in order to get feedback and improve on their products constantly. The data mining algorithms discussed earliest are extremely useful for fabrication and analysing information and data about customers and printings on projects. We can then use this information to make improvements or changes where they are needed, and this will greatly emergence customer expiation as customers will be able to see the changes they wanted. Although it is recommended to only try this with a vast hail of data, and huge amount of transactions. As smaller amounts of data can provide in accurate information. Using CRM will greatly improve the Crowd Funding familiaritys reputation and mean they have a dole out more at ease donors. Conclusion In conclusion I strongly recommend that the crowd championship system decides to involve data mining algorithms. It has a long list of advantages including sales prediction, improved advertisement, and by and large importantly improved customer satisfaction. I would also highly recommend the use of the decision tree algorithm as its easy to follow and can easily be modified depending on the information that needs to be collected. It should be noted that excerption of data source is important, as some of them may provide useful information, but there are quite a few that should be ignored. CRM should also be taken into consideration, as using this software has proven to greatly improve the publics opinion of a business. A modern business cant afford not to use these data mining techniques, as failure to utilize these tools will mean a huge disadvantages against its competitors. The more information that can be collected from this companies customers, the more value the company can provide them, and the happier the customer the more donations that will be made. References Affinity Analysis . (n.d.). Retrieved from https//en.wikipedia.org/wiki/Affinity_analysis Bayesian Classifcation . (n.d.). Retrieved from https//www.tutorialspoint.com/data_mining/dm_bayesian_classification.htm Business Intelligence . (2007, sue 6). Retrieved from http//www.cio.com/article/2439504/business-intelligence/business-intelligence-business-intelligence- rendering-and-solutions.html Business Intelligence . (2015, Febuary 26). Retrieved from http//businessintelligence.com/big-data-case-studies/data-driven-proof-netflix-needs-buy-blockbuster/ Clusters . (n.d.). Retrieved from https//en.wikipedia.org/wiki/Cluster_analysis CRM . (n.d.). Retrieved from http//searchcrm.techtarget.com/definition/CRM Decision Tree Algorithm . (n.d.). Retrieved from http//www.saedsayad.com/decision_tree.htm Disadvantages of Business Intelligence . (n.d.). Retrieved from http//business.map sofindia.com/business-intelligence/disadvantages.html k-means . (n.d.). Retrieved from https//en.wikipedia.org/wiki/K-means_clustering OLAP . (n.d.). Retrieved from http//olap.com/olap-definition/ headliner Schema Notes The use of BLOB is so that the users can store there boob tube sales pitches within the database, after some research I realised there wasnt a dedicated media storage format and quite have to dish out story it in binary.