Tools for business Intelligence

Once data have been captured and organized in data warehouses and  data marts, they are available for further analysis using tools for business intelligence. Business intelligence tools enable users to analyze data to see new patterns, relationships, and insights that are useful for guiding decision making. Principal tools for business intelligence include software for database querying and reporting, tools for multidimensional data analysis (online analytical processing), and tools for data mining.

Online analytical processing (OLAP): Multidimensional data analysis OLAP supports multidimensional data analysis, enabling users to  view the same data in different ways  using multiple dimensions (data cube). Multidimensional data models are designed expressly to support data analyses. The goal of multidimensional data models is to support analysis in a simple and faster way by executives, managers and business professionals. These people are not interested in the overall architecture.

Suppose your company sells five different  products—Laptops, Computers, TVs, Camera and Mobiles—in the East, West, North and Central regions. If you wanted to ask a fairly straightforward question, such as how many Computers were sold in the last week, you could easily find the answer by using sales database. But what if you wanted to  know how many Computers sold in each of your sales regions and compare actual results with projected sales, then the querying becomes complicated. In such a case OLAP is used.

Each aspect of information—product, pricing, cost, region, or time period—represents a different dimension. So, a product manager could use a multidimensional data analysis tool to learn how many Computers were sold in the East reason in this week, how that compares with the previous week, and how it compares with the sales forecast. OLAP enables users to obtain online answers to ad hoc questions such as these in a fairly rapid amount of time, even when the data are stored in very large databases, such as sales figures for multiple years.

Fig: Tabular representation

Data mining

Data mining refers to extracting or ―mining‖ knowledge, interesting information or patterns from large amount of data. Data mining is a process of discovering interesting knowledge from large amounts of data stored either, in database, data warehouse, or other information repositories.

It is the semi-automatic process of extracting and identifying patterns from stored data. A data mining application, or data mining tool, is typically a software interface which interacts with a large database containing customer or other important data. Data mining is widely used by companies and public bodies for such uses as marketing, detection of fraudulent activity etc. That is, data mining deals with ―knowledge discovery in databases. There are a wide variety of data mining applications available, particularly for business uses, such as Customer    Relationship                      Management                      (CRM). These applications enable marketing managers to understand the behaviors of their customers and also to predict the potential behavior of prospective clients.

Data mining is a logical process that is used to search through large amount of data in order to find useful data. The goal of this technique is to find patterns that were previously unknown. Once these patterns are found they can further be used to make certain decisions for development of their business.

Functions of data mining

The types of information obtainable from data mining include associations, sequences, classifications, clusters, and forecasts.

• Association: Association is one of the best known data mining technique. In association, a pattern is discovered based on a relationship between items in the same That is the reason why association technique is also known as relation technique. The association technique is used in market basket analysis to identify a set of products that customers frequently purchase together. For instance, books that tends to be bought together. If a customer buys a book, an online bookstore may suggest other associated books. If a person buys a camera, the system may suggest accessories that tend to be bought along with cameras.
• Prediction: The prediction, as it name implied, is one of a data mining techniques that discovers relationship between independent variables and relationship between dependent and independent variables. For instance, the prediction analysis technique can be used in sale to predict profit for the future if we consider sale is an independent variable, profit could be a dependent For instance, when a person applies for a

credit card, the credit-card company wants to predict if the person is a good credit risk. The prediction is to be based on known attributes of the person, such as age, income, debts, and past debt repayment history.

• Classification: Classification is a classic data mining technique based on machine Basically classification is used to classify each item in a set of data into one of predefined set of classes or groups. Classification method makes use of mathematical techniques such as decision trees, linear programming, neural network and statistics. For example, we can apply classification in application that ―given all records of employees who left the company; predict who will probably leave the company in a future period.‖ In this case, we divide the records of employees into two groups that named ―leave‖ and ―stay‖. And then we can ask our data mining software to classify the employees into separate groups.
• Clustering: Clustering is a data mining technique that makes meaningful or useful cluster of objects which have similar characteristics using automatic The clustering technique defines the classes and puts

objects in each class, while in the classification techniques, objects are assigned into predefined classes. For example in a library, there is a wide range of books in various topics available. The challenge is how to keep those books in a way that readers can take several books in a particular topic without hassle. By using clustering technique, we can keep books that have some kinds of similarities in one cluster or one shelf and label it with a meaningful name.

Text mining

Text mining is the discovery of patterns and relationships from large sets of unstructured data—the kind of data we generate in e-mails, phone conversations, blog postings, online customer surveys, and tweets.

Web mining

The discovery and analysis of useful patterns and information from the World Wide Web or simply web is called web mining. Web mining is the application of data mining technique to find interesting and potentially useful knowledge from web data. So web mining is the application of data mining technique to extract knowledge from web data, including web documents, hyperlinks between documents, usage logs of web sites etc.

Businesses might turn to Web mining to help them understand customer behavior, evaluate the effectiveness of a particular Web site, or quantify the success of a marketing campaign. For instance, marketers use Google Trends and Google Insights for Search services, which track the popularity of various words and phrases used in Google search queries, to learn what people are interested in and what they are interested in buying.