Hexaware E-book on Predictive Analytics Business Intelligence & Analytics Actionable Intelligence Enabled Published on : Feb 7, 2012
Hexaware E-book on Predictive Analytics What is Data mining? Data mining, or knowledge discovery, is the computer-assisted process of finding hidden patterns in data. Data mining tools predict behaviors and future trends, allowing businesses to make proactive, knowledge-driven decisions. Hence it is also called predictive analytics. Data mining tools can answer business questions that traditionally were time consuming to resolve. They scour databases for hidden patterns, finding predictive information that experts may miss because it lies outside their expectations. When and where the data mining and predictive analytics could be useful? The amount of raw data stored in corporate databases is exploding. From trillions of point-of-sale transactions and credit card purchases to pixel-by-pixel images of galaxies, databases are now measured in gigabytes and terabytes. Raw data by itself, however, does not provide much information. In today's fiercely competitive business environment, companies need to rapidly turn these terabytes of raw data into significant insights into their customers and markets to guide their marketing, investment, and management strategies. In these scenarios data mining would help you unlock the hidden potential of your data and deliver actionable insights. Can you name some specific uses of data mining? Some Specific uses of data mining include: Market segmentation - Identify the common characteristics of customers who buy the same products from your company. Customer churn - Predict which customers are likely to leave your company and go to a competitor. Fraud detection - Identify which transactions are most likely to be fraudulent. Direct marketing - Identify which prospects should be included in a mailing list to obtain the highest response rate. Interactive marketing - Predict what each individual accessing a Web site is most likely interested in seeing. Market basket analysis - Understand what products or services are commonly purchased together; e.g., beer and diapers. Trend analysis - Reveal the difference between typical customers this month and last. Will sharing data to do data mining raise privacy related issues? What needs to be done in such scenarios? There is a way to deal with sensitive data like credit card numbers, insurance policy numbers and account numbers. Data need to be masked or recoded to maintain the privacy. 1
What are all steps involved in data mining? Data mining process involves predefined steps starting from Business case understanding or Problem understanding, Data understanding Data extraction Pre-processing Mining model building Testing and Evaluation Is there any maintenance to be done for the mining model once it is deployed? Yes, the mining model needs to be calibrated at least once in six months. The frequency varies based on the business need and the volume of data flow Calibration involves Checking the predicted output with the actual output Modifying the mining model if required Public Can data mining solution be offered on the cloud? Yes, Data mining solution can be offered on the cloud. Organizations can adopt Pay per use method without investing on the infrastructure required. Here the challenge is to upload huge amount of data on the cloud. Name some tools used for Data mining? a. Microsoft BI stack has SSAS as part of SQL Server 2008 b. There is an open source tool called R which offers data mining solution c. Rapid Miner is an another open source tool d. SAS is of course is a highly sophisticated tool with enormous computational power e. IBM has it s tool called SPSS f. Oracle s ODM Oracle Data Miner The above list is not exhaustive. What are all the industries in which Predictive Analytics is applicable? We have recently provided predictive analytics solutions for following Industries: Insurance Education Mining Logistics Health & Hygiene In short, predictive analytics can be deployed for diverse industries. 2
What are all the risks involved in using predictive analytics solution? Wrong understanding of business problems and data will result in a prediction model with complex statistical algorithms, but it will be of no use to the business. Wrong interpretation of the results would lead to wrong decisions. Poor data quality would result in poor predictions. Absence of maintenance of the mining model would make predictions obsolete. Building the predictive analytics solution with resources with less statistical knowledge will lead to less accurate models. What is Text mining? Text Mining is the process of deriving high quality information from unstructured text data. There are various techniques used to derive high quality information from textual data, such as computational linguistics, information retrieval, statistics, machine learning, etc. Various forms of text mining include categorization, classification, clustering, concept extraction, summarization, sentiment analysis, etc. Are the open source tools sufficient and robust in providing answers to tough business cases? Open source tools like R and Rapid miner provide excellent flexibility to build the model. Online R community constantly updates algorithms and industry specific solutions as packages to R after validating. So far there are around 3500 packages built in R. R s popularity has been increasing over the other predictive analytics tools. In a recent survey Kdnuggets.com reports that R has 24% of market share and R is the most sought after statistical programming language. Can you list some of the best practices in Data mining and Predictive analytics? Executive Support: Support from the decision makers and middle management would make a world of difference. Business problem specificity: Identification of correct business problem to apply predictive analytics is vital to the success of the mining model. Availability of historical data: Richer the data, the more robust will be the mining model. Good quality data: It is the most important factor for an accurate mining model. Pre-processing: While building the mining model, one of the main activities is pre-processing where the data is cleansed, sliced, diced and categorized to suit to the mining model. Good business knowledge and a sound data mining knowledge is required to do this as this is the base for the predictive model. Selection of statistical techniques: Experienced data mining resource can choose the correct statistical technique and can compare the accuracy of other techniques. Interpretation of output: It is extremely important to interpret the output in the correct way and link it back to the business problem stated initially. 3
4 Thank you for reading our E- Book, in case you have any queries please write back to us at corporatemarketing@hexaware.com If you want to keep up with the industry's latest trends, please visit our blog on BI http://blogs.hexaware.com/index/business-intelligence For more information on our Business Intelligence & Analytics services please visit http://hexaware.com/business-intelligence-analytics.htm About Hexaware Hexaware is a leading global provider of IT and BPO services. The company has achieved leadership position in domains such as Banking, Financial Services, Insurance, Transportation, Logistics and HR-IT solutions. Hexaware focuses on delivering business result leveraging technology solution and specializes in Business Intelligence & Analytics, Enterprise Applications, Independent Testing and Legacy Modernization. Hexaware has been providing business technology solutions for over 20 years and offers world class services delivery, technology leadership and skilled human capital.