Data Mining


Generally, data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases.

we offer our clients high value professional services that are tailored to enhance your bottom line. Our consultants have years of experience in our field and are here to provide you with the best service we can deliver.


Why Data Mining?


                  

                  Facing to enormous volumes of data, human analysts with no special tools can no longer make sense. However, Data mining can automate the process of finding relationships and patterns in raw data and the results can be either utilized in an automated decision support system or assessed by a human analyst. This is why to use data mining, especially in science and business areas which need to analyze large amounts of data to discover trends which they could not otherwise find. Accurately interpreting your data can lead to better decisions, improved processes, quality, and even customer satisfaction.Data mining aids in knowledge discovery in databases, and is therefore also termed as the analysis step of Data Analytics.

  • Business Understanding

    This initial phase focuses on understanding the project objectives and requirements from a business perspective, and then converting this knowledge into a data mining problem definition, and a preliminary plan designed to achieve the objectives.

  • Data Understanding

    The data understanding phase starts with an initial data collection and proceeds with activities in order to get familiar with the data, to identify data quality problems, to discover first insights into the data, or to detect interesting subsets to form hypotheses for hidden information.

  • Modeling

    In this phase, various modeling techniques are selected and applied, and their parameters are calibrated to optimal values. Typically, there are several techniques for the same data mining problem type. Some techniques have specific requirements on the form of data. Therefore, stepping back to the data preparation phase is often needed.

  • Evaluation

    At this stage in the project you have built a model (or models) that appears to have high quality, from a data analysis perspective. Before proceeding to final deployment of the model, it is important to more thoroughly evaluate the model, and review the steps executed to construct the model, to be certain it properly achieves the business objectives. A key objective is to determine if there is some important business issue that has not been sufficiently considered. At the end of this phase, a decision on the use of the data mining results should be reached.

  • Deployment

    Creation of the model is generally not the end of the project. Even if the purpose of the model is to increase knowledge of the data, the knowledge gained will need to be organized and presented in a way that is useful to the customer. Depending on the requirements, the deployment phase can be as simple as generating a report or as complex as implementing a repeatable data scoring (e.g. segment allocation) or data mining process. In many cases it will be the customer, not the data analyst, who will carry out the deployment steps. Even if the analyst deploys the model it is important for the customer to understand up front the actions which will need to be carried out in order to actually make use of the created models.

Two main reasons to use data mining:

  •    Too much data and too little information
  •    There is a need to extract useful information from the data and to interpret the data

If we know how to reveal valuable knowledge hidden in raw data, data might be one of our most valuable assets. while data mining is the tool to extract diamonds of knowledge from your historical data and predict outcomes of future situations.


The services we  provide are:

  • Data mining
  • Text mining
  • Web data mining
  • Web data extraction
  • Screen scraping
  • Data warehousing

Mining for Priceless Information

Data Mining services help your organization make sense of data, transforming it from incomprehensible volumes to priceless information.Empowered with strong research team and vast experience .Datamining is important part of knowledge discovery process that we can analyze enormous set of data and get hidden and useful knowledge. Data mining is applied effectively not only in business environment but also in other fields such as weather forecast, medicine, transportation, healthcare, insurance, government…etc. Data mining has a lot of advantages when using in a specific industry.

  • Marketing / Retail

    Data mining helps marketing companies build models based on historical data to predict who will respond to the new marketing campaigns such as direct mail, online marketing campaign…etc. Through the results, marketers will have appropriate approach to sell profitable products to targeted customers.

    Data mining brings a lot of benefits to retail companies in the same way as marketing. Through market basket analysis, a store can have an appropriate production arrangement in a way that customers can buy frequent buying products together with pleasant. In addition, it also helps the retail companies offer certain discounts for particular products that will attract more customers.

  • Finance / Banking

    Data mining gives financial institutions information about loan information and credit reporting. By building a model from historical customer’s data, the bank and financial institution can determine good and bad loans. In addition, data mining helps banks detect fraudulent credit card transactions to protect credit card’s owner.

  • Marketing / Retail

    By applying data mining in operational engineering data, manufacturers can detect faulty equipments and determine optimal control parameters. For example semi-conductor manufacturers has a challenge that even the conditions of manufacturing environments at different wafer production plants are similar, the quality of wafer are lot the same and some for unknown reasons even has defects. Data mining has been applying to determine the ranges of control parameters that lead to the production of golden wafer. Then those optimal control parameters are used to manufacture wafers with desired quality.

  • Manufacturing

    By applying data mining in operational engineering data, manufacturers can detect faulty equipments and determine optimal control parameters. For example semi-conductor manufacturers has a challenge that even the conditions of manufacturing environments at different wafer production plants are similar, the quality of wafer are lot the same and some for unknown reasons even has defects. Data mining has been applying to determine the ranges of control parameters that lead to the production of golden wafer. Then those optimal control parameters are used to manufacture wafers with desired quality.

  • Governments

    Data mining helps government agency by digging and analyzing records of financial transaction to build patterns that can detect money laundering or criminal activities.