Business Intelligence Advantages and Disadvantages

lineage light Advantages and Dis receiptss entreThe purpose of this report is to discuss the both the advantages and disadvantages of use Business intelligence operation indoors a strain. As hearty as to discuss the potential difference algorithms which could be commitd to achieve informationmining which proffer allow for discovery of nurture who may be existing or potential future guests. By the end of this report I aim to make it clear the advantages of incorporating these tools and techniques within the line of business, and the benefits that allow be seen.Business paroleBusiness Intelligence (Business Intelligence , 2007) is a collection various technologies and tools which argon utilise for collecting, organizing and analysing information and information, and therefore providing the user with the information in a form which leave alone do them with do business decisions. there argon 3 major move to business intelligence Reporting, Integration and a nalytic thinking. Reporting is essentially the creation and use of reports, while integration is about taking data from a writer and being able to modify it to fit a nonher(prenominal) purpose and data source. Finally, abbreviation is the producing and organizing structures that hold been filled with data taken from a give away source, commonly tools such as OLAP (OLAP, n.d.) (Online Analytical Processing) are used in order to achieve this. This process if practically referred to as entropy digging. employ Business Intelligence has numerous advantages and is something that every comp both should consider apply. matchless of its most obvious advantages is that it fanny military service show trends and correlation in statistics (E.G user activity, sales, and complaints) and this fanny then(prenominal) be used by businesses in order to improve. another(prenominal) considerable advantage of using Business Intelligence is too the reliability of the presented information and al lows for relatively accurate prediction which greatly improves planning.Although it should be noted that there are some disadvantages to using Business Intelligence, this is that the historical data that is recorded consumes to be stored somewhere, and this takes up much(prenominal) memory, which not only heart more than cost in storage, moreover also a speed reduction as there leave alone be a huge number data to be analysed. other notable disadvantage is the potentially high initial cost, as well as maintenance cost, and although these costs should pay for themselves with meliorate decision making there is a possibility of the investment not paying off. There are not many an(prenominal) disadvantages to using business intelligence, merely they should tranquil be taken into consideration. (Disadvantages of Business Intelligence, n.d.)A good example of business intelligence being used by other recognisable companies is that Netflix (Business Intelligence, 2015), the onl ine media drift service, using this carcass of business intelligence to work out which shows go out be popular, and which of their categories may emergency a little reworking. This gives them the information they need to stay ahead of the curve and to make sure the shows that remain on the site are popular.Datamining algorithmsWith computers being used more and more within businesses, the information that the business needs to operate on is also stored on these computers (E.G gross sales records, customer information etc.) so the ability to s chamberpot and analyse these abundant amounts of information is incredibly beneficial to not only making business ground decisions, but to predict sales trends or areas in need of improvement. There are a wide range of several(predicate) Data mining algorithms available to use, the ones discussed here will be the Decision channelize, Bayesian Classification as well as K-Means. I have chosen to discuss and compare these 3 as they are quite varied in how they operate. One of the most commonly used Data Mining algorithms is the Decision Tree (Decision Tree Algorithm, n.d.), at the top of the decision tree we have a Root, which is essentially a check on an attribute, and from there the answers to the check make the branches. The leaves of the tree are in fact organize from each class label. The advantages of using this algorithm compared to the others is that in order to function it requires to prior knowledge of the domain, the other huge advantage which makes an attractive resolvent is that it is also very easy to companion and understand compared to more knotty algorithms. The complexity for this algorithm can be worked out by the enumerate of leaves that the decision tree has. This algorithm is oftentimes called Supervised Learning, this basically means that the data is already labelled within classes.(Image taken from (http//www.saedsayad.com/decision_tree.htm(Decision Tree Algorithm, n.d.)) The momen t mostly commonly used algorithm for Data Mining is know as Bayesian Classification (Bayesian Classifcation, n.d.), this algorithm effectively works via predicting the probability that a pattern or set of information belongs to a particular(prenominal) class. This algorithm is often favoured among the Data Mining techniques for its efficient results, although it needs to be taken into consideration that if the data is highly random then another algorithm would be preferred over the Bayesian Classification. It is also not pep uped to use this algorithm with small 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 used often in filtering software (email spam, language filters). This algorithm is a supervised knowledge, as the user exits it with an already labelled dataset.The third algorithm which should be considered for the bear on living System is K-Means (k-means, n.d.). This algorit hm works by creating groups ground on the set of objects this results in the in the members of the group more similar, this algorithm is often referred to as Cluster Analysis. Cluster Analysis is a collection of different algorithms which all follow the same pattern (Clusters, n.d.). The pattern being that they induce groups (or clusters) in a way which means that the cluster members are such(prenominal) more similar as opposed to non-grouped members. This is not quite unsupervised nor supervised learning, this is because the user states the number of clusters needed, but it still features unsupervised learning as well as the algorithm learns where the cluster belongs without the user needing to provide it with any more information.Ive compared 3 algorithms, a decision tree based one, a clustering based one and a nave one. My pass for use with the multitude Funding software program would be the Decision Tree, this is for a number of reasons, the first being that its highly eas y to follow, even by someone who has no prior knowledge to the algorithm. Also because its easy to follow and understand its also easy to maintain and tweak it depending on the circumstance. Another major reason that I would chose decision tree is that they work quick as well being non-parametric. Non-parametric means that the algorithm doesnt need particularised data distribution in order to function.Data-mining advantages and disadvantagesThe main advantage of using Data-Mining for the Crowd Funding System would be that it could use Affinity Analysis (Affinity Analysis, n.d.), this is basically a scan off all the customers previous shop history and then be able to labour to them directly. This applies to the Crowd Funding System as we can use data mining to puzzle out what projects a customer prefers and then advertise those projects directly to them. (E.G If a particular user often supports Gaming Software projects on the webpage, then we can use this information to have all Gaming Software projects as the top hit on their home page.) Affinity Analysis can often be used to detect fraud, which is useful for any company. Another advantage that this business can gain from Data Mining is Customer Segmentation, this is the process of breaking the customers down into smaller group based on say age, occupation or even gender. The advantage of doing this is that you can then tar purport your advertisement to people who will be highly interested, and the more effective the advertising the more money people will donate to the projects. This applies directly to the Crowd Funding Systems first example, using this customer segmentation the film writer / director will be advertise her project to all her previous fans, or even people who are interested in that genre, this will mean she can relieve oneself a much more interested user base. The other huge advantage of Data Mining that can be applied to the CFS is that it can help to achieve Sales Forecasting, this is e xactly 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 phone battery, if they can predict how many donations the project is going to get they can either boost its advertisement, or perhaps communicate with the user that previous similar projects havent been able to reach their goal or at least direct them where they went wrong.One of the concerns the online business has is damage to its reputation, using data mining techniques they will 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 beneficial for the company. Donor exhaustion was also on the companies list of concerns, but data mining will be able to prevent this because it can be used to keep track of what advertisement has been sent to who, and what projects they are likely to bid on, so ro utinely changing the projects they are publicize will keep the users hopefully interested.CRMCRM stands for Customer Relationship 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 earlier are extremely useful for gathering and analysing information and data about customers and opinions on projects. We can then use this information to make improvements or changes where they are needed, and this will greatly increase customer satisfaction as customers will be able to see the changes they wanted. Although it is recommended to only try this with a vast amount 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 Companys reputation and mean they have a lot more satisfied donors.ConclusionIn conclusion I strongly recommend th at the crowd funding system decides to include data mining algorithms. It has a long list of advantages including sales prediction, improved advertisement, and mostly importantly improved customer satisfaction. I would also highly recommend the use of the decision tree algorithm as its easy to follow and can comfortably be modified depending on the information that needs to be collected. It should be noted that choice 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 m ore donations that will be made.ReferencesAffinity Analysis. (n.d.). Retrieved from https//en.wikipedia.org/wiki/Affinity_analysisBayesian Classifcation. (n.d.). Retrieved from https//www.tutorialspoint.com/data_mining/dm_bayesian_classification.htmBusiness Intelligence . (2007, March 6). Retrieved from http//www.cio.com/ condition/2439504/business-intelligence/business-intelligence-business-intelligence-definition-and-solutions.htmlBusiness 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_analysisCRM. (n.d.). Retrieved from http//searchcrm.techtarget.com/definition/CRMDecision Tree Algorithm. (n.d.). Retrieved from http//www.saedsayad.com/decision_tree.htmDisadvantages of Business Intelligence. (n.d.). Retrieved from http//business.mapsofindia.com/business-intelligence/disadvantages.htmlk-means. (n.d.). Retrieved from https//en.wikipedia.org/wiki/K-means_clusteringOLAP. (n.d.). Retrieved from http//olap.com/olap-definition/Star synopsisNotesThe use of BLOB is so that the users can store there ikon sales pitches within the database, after some research I established there wasnt a dedicated media storage format and instead have to suffice story it in binary.