Trends and Challenges
Databases today can range in size into the terabytes — more than 1,000,000,000,000 bytes of data. Within these masses of data lies hidden information of strategic importance. But when there are so many trees, how do you draw meaningful conclusions about the forest?
The newest answer is data mining, which is being used both to increase revenues ( through improved marketing ) and to reduce costs ( through detecting and preventing waste and fraud ). Worldwide, organizations of all types are achieving measurable payoffs from this technology.
Data mining finds patterns and relationships in data by using sophisticated techniques to build models — abstract representations of reality. A good model is a useful guide to understanding your business and making decisions.
Data mining is the process of extracting hidden knowledge from large volumes of raw data. It can also be defined as the process of extracting hidden predictive information from large databases.
There are two main kinds of models in data mining:
- Predictive models can be used to forecast explicit values, based on patterns determined from known results. For example, from a database of customers who have already responded to a particular offer, a model can be built that predicts which prospects are likeliest to respond to the same offer.
- Descriptive models describe patterns in existing data, and are generally used to create meaningful subgroups such as demographic clusters.
Data mining is only one step in the knowledge discovery process. Other steps include identifying the problem to be solved, collecting and preparing the right data, interpreting and deploying models, and monitoring the results. The real key to success, however, is to have a thorough understanding of your data and of your business. Algorithms can provide meaningful results only when sensibly directed.
The potential payoffs are enormous. Innovative organizations are already using data mining to locate and appeal to higher-value customers, reconfigure their product offerings to increase sales, and minimize losses due to error or fraud. The list of data mining applications is surprisingly broad.
Data mining is not an “intelligence” tool or framework. Business intelligence, typically drawn from an enterprise data warehouse, is used to analyze and uncover information about past performance on an aggregate level. Data warehousing and business intelligence provide a method for users to anticipate future trends from analyzing past patterns in organizational data. Data mining is more intuitive, allowing for increased insight beyond data warehousing. An implementation of data mining in an organization will serve as a guide to uncovering inherent trends and tendencies in historical information. It will also allow for statistical predictions, groupings and classifications of data.
Most companies collect, refine and deduce massive quantities of data. Data mining techniques can be implemented rapidly on existing software and hardware platforms to enhance the value of existing information resources, and can be integrated with new products and systems, as they become part of the system. When implemented on high performance client/server or parallel processing computers, data mining tools can analyze massive databases to deliver answers to many different types of predictive questions.
Data mining tools allows users to analyze large databases to solve business decision-making problems. Data mining tools predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. Data mining solution can answer business questions that traditionally were too time-consuming to resolve. Data mining is, in some ways, an extension of statistics, with a few artificial intelligence and machine learning twists thrown in. Like statistics, data mining is not a business solution, it is just a technology
Data mining techniques are the result of a long process of research and product development. This evolution began when business data was first stored on computers, continued with improvements in data access, and more recently, generated technologies that allow users to navigate through their data in real time. Data mining takes this evolutionary process beyond retrospective data access and navigation to prospective and proactive information delivery. Data mining is ready for application in the business community because it is supported by three technologies that are now sufficiently mature:
- Massive data collection
- Powerful multiprocessor computers
- Data mining algorithms
In what areas is data mining profitable?
A wide range of companies have deployed successful applications of data mining. While early adopters of this technology have tended to be in information-intensive industries such as financial services and direct mail marketing, the technology is applicable to any company looking to leverage a large data warehouse to better manage their customer relationships. Two critical factors for success with data mining are: a large, well-integrated data warehouse and a well-defined understanding of the business process within which data mining are to be applied (such as customer prospecting, retention, campaign management, and so on).
Our Solution
The key to appropriate use of data mining lies in a structured methodology to find problems, define solutions, set expectations, and deliver results. We call this process Discovering Knowledge.
Data mining for maximum value is difficult unless a structured plan is followed. We at SEEinfobiz describe the solution to get the most out of data mining. It introduces and defines exactly what our solution is, and how the pieces fit together.
- Using data mining tools
SEEinfobiz’s comprehensive data mining solution includes services and training that allow you to explore large quantities of data and discover relationships and patterns that lead to proactive decision making.
Data mining tools are used to ensure flexibility and the greatest accuracy possible. Essentially, data mining tools increase the effectiveness of data mining applications. Since no two organizations or their data are alike, no single technique delivers the best results for everyone. Not only do data mining tools deliver in-depth techniques, but data mining tools also deliver flexibility to use combinations of techniques to improve predictive accuracy.
Because data mining tools are so flexible, a set of data mining guidelines and a data mining methodology have been developed to help guide the process.
- SEEinfobiz's data mining transforms data into actionable results
SEEinfobiz's data mining technology enables every organization to quickly develop predictive data mining models and deploy those data mining models into the organization's operations - improving decision-making. Using this powerful, visual data-mining interface and business expertise, one can quickly interact with the data and begin discovering patterns you can use to change organization for the better.
- The latest data mining advances — text mining and Web mining
SEEinfobiz's recent advances have led to the newest and hottest trends in data mining — Text mining and Web mining. These two data mining technologies open a rich vein of customer data in the form of textual comments from survey research and log files from Web servers, which were previously unusable. Applying data mining to these data adds a richness and depth to the patterns already uncovered through your data mining efforts.
Text Mining – SEEinfobiz’s Text Mining enables you to extract key concepts, sentiments, and relationships from this unstructured data, and convert it to structured format for predictive modeling with SEEinfobiz's solution. So your base critical decisions on 100 percent of your available data—not 20 percent.
SEEinfobiz's Text Mining uses the most advanced extraction technology to access and process virtually any type of unstructured data. After key concepts are extracted from text, data mining techniques such as classification, clustering, and predictive modeling are applied to them. This has been shown to improve the “lift” or accuracy of predictive data models and significantly improve results. Whether you’re analyzing call center conversations in real time to provide better recommendations, or looking for associations between people and events that may indicate potential security threats, text mining ensures more accurate insight.
- Easy access and deployment
SEEinfobiz's Text Mining uses interactive interface and visualization capabilities to make text mining accessible to business users. Access text data directly within the solution, and deploy streams and models for use in other applications, such as analyzing customer e-mails for information about products and service preferences. SEEinfobiz's Text Mining open system works with many types of applications.
- Fast and accurate
To handle enterprise-sized volumes of information, SEEinfobiz's Text Mining analyzes approximately 1 gigabyte of text (or 250,000 pages) per hour, with 90 percent or better accuracy. In addition, SEEinfobiz's Text Mining can process all common document types, including plain text, HTML, XML, Excel, etc.
Web Mining – SEEinfobiz's Web Mining is an add-on module that makes it easy for analysts to perform ad hoc predictive Web analysis within intuitive visual workflow interface. By bringing together the leading technologies for both Web analytics and data mining—SEEinfobiz's Web Mining sets a new standard for Web analysis. Easily transform raw Web data into analysis-ready business events using the integrated Web mining source node. These business events are available within predictive Web analysis applications that quickly deliver actionable insight.
SEEinfobiz's Web Mining enables online business decision makers to take more effective action with the ability to:
- Automatically discover user segments
- Detect the most significant sequences
- Understand product and content affinities
- Predict user propensity to convert, buy, or churn
SEEinfobiz's Data mining solution reaches across industries and business functions, such as –
- Telecommunications, stock exchanges and credit card and insurance companies use data mining to detect fraud, optimize marketing campaigns and identify the most profitable strategies
- The medical industry uses data mining to predict the effectiveness of surgical procedures, medical tests and medications
- Retailers use data mining to assess the effectiveness of coupons and special events, and predict which offers are most appropriate for different consumers.
Benefits
Seek and retain your most profitable customers - Use demographic data and customer buying patterns to develop lifelong relationships with your customers, anticipating and fulfilling their needs.
Segment markets for a targeted approach - Target marketing campaigns to dramatically increase response rates, analyze click stream data and sharpen your e-commerce strategy.
Predict the future and identify factors to secure a desired effect - Improve production process quality by anticipating problems before they occur, forecast resource demands, increase acquisitions and assess the risk of customer credit applications.
A broad set of tools supports the complete data mining process - Going from raw data to accurate, business-driven data mining models becomes a seamless and efficient process with SEEinfobiz's Data Mining solution. The solution also provides an integrated, collaborative environment so the statistical modeling group, business managers and the IT department can work together more efficiently.
An easy-to-use GUI helps both business analysts and statisticians build more models, faster - SEEinfobiz's Data Mining solution’s process flow diagram environment reduces the need for manual coding and dramatically lessens the time to develop models, for both business analysts and statisticians.
Ability to surface reliable business information more easily - SEEinfobiz's Data Mining solution offers numerous integrated assessment features for comparing results of different modeling techniques in both statistical and business terms within a single, easy-to-interpret framework. Model results can be easily shared throughout the enterprise through the first model management system on the market.
Deploy models across the enterprise with unprecedented ease - SEEinfobiz's Data Mining solution automates the tedious model scoring process and supplies complete scoring code for all stages of model development.
Integrated suite of unmatched modeling techniques - SEEinfobiz's Data Mining solution provides advanced predictive and descriptive modeling tools and algorithms, including decision trees, neural networks, auto neural networks, memory-based reasoning, linear and logistic regression, clustering, associations, time series and more.
Sophisticated set of data preparation, summarization and exploration tools - SEEinfobiz's Data Mining solution includes several tools to help with preprocessing data. Extensive descriptive summarization features are included as well, and the platform-independent GUI provides advanced statistical graphics with very flexible actions and controls.
Integrated model comparisons and extensive results packages - SEEinfobiz's Data Mining solution offers numerous features for comparing the results of different modeling techniques in business terms as well as through statistical diagnostics. This provides the unique ability to gauge model effectiveness in terms of overall profitability, enabling the quantitative analyst to easily share and discuss essential results with business users.
An open, extensible design - SEEinfobiz's Data Mining solution offers a customizable and extensible data-mining environment providing the ability to add tools and integrate personalized code.
Reduced time-to-decisions and a more accurate organizational view - By combining structured data and unstructured text, and automating the process of analyzing the data, SEEinfobiz's Text Mining solution helps organizations gain meaningful insights that successfully drive overall business direction.
Improved organizational performance - While most software vendors offer classification of one text field into a single class, SEEinfobiz's Text Mining solution enables the classification of multiple structured and unstructured fields. It improves your organization's performance by distilling information from multiple business units into a format that is easy to manage and analyze.
Transform data into a compact, information-rich structure - SEEinfobiz's Text Mining solution distills key concepts contained in large document collections and analyzes relationships between isolated terms or phrases and documents. This reduces the complexity of text and structures it for use in data exploration, clustering and predictive modeling.
An interactive results browser enables analysts to interactively explore concepts and relationships between documents and dynamically make modifications to further tailor analyses.