Data Mining: This is a logical method to explore data in search of a steady patterns and systematic connection between variables, and then to validate the findings by applying the detected patterns to new subsets of data. The overall goal of the date mining process is to extract information from a data set and transform it into an understandable structure for further use.
Data mining combines three technologies- 1) Powerful computer application (upgraded multiprocessor computers), 2) Statistical and learning algorithms (Data mining algorithms) and 3) improved Data collection and Management (Massive data collection).
Data mining Process: A standard data mining process should include the followings.
a) Job (Business) understanding, b) Data understanding, c) Data preparation, d) Process modelling, e) process evaluation and f) Deployment.
Popular Uses of Data Mining: Direct mail marketing, Website personalization, Credit card fraud detection, Market segmentation, Trend analysis, Customer churn and Market basket analysis etc.
From the practical view, data mining is good because it enables corporations to minimize risk and increases profit. It also helps to form the powerful security system and brings benefits to the society by speeding up the technological advancement.
Now-a-days various companies, having a strong consumer focus, who associated with retail, financial, communication and marketing sectors, use this system extensively. The process enables these organizations to discover the relationships between Internal (product price, product positioning, and staff skills) and external (economic indicators, competition and customer demographics) factors. It can help them to detect the impact on sales, consumer's satisfaction and business profits. By applying this method, companies can access data that belongs to lower level of a hierarchically database & put these into summary information to view detail transactional data.
We Provide High Level Of Accuracy, Timely Deliveries, Total Confidentiality And Cost Effective Data Mining Services For The Following Users:
- Banking, insurance, e-commerce, airline, telecom and retail
- Organizers of trade fairs
- E-Marketing Companies
- Travel Agencies
- Publishers of Books, Magazines etc.
- Chambers of Commerce
- Web hosting & Design companies looking for clients
- Companies looking for specific products/suppliers
Our expert team handles all types of projects - small to big; simple to complex; common to unusual.
Finally, we can analyze your data & offer you the best useful report as per your requirement, after a stringent quality control check by our censorious quality control department.
We Provide Following Data Mining Outsourcing Services:
- Gathering data from websites and entering it into Excel spreadsheets.
- Online synchronization for different database.
- Searching the web, creating lists of target websites, and then collecting information from these sites.
- Mining process for different website's products, prices and description etc.
- Using software to extract Meta data from websites.
- Searching online newspapers for the latest information.
- Extracting and summarizing news stories from online news sources.
- Gathering precise and updated information about competitors pricing and offers.
- Pooling market research data and blog to find out what users are saying about their latest purchases, and much more.
The Life Cycle of our Data Mining Consulting Processes work is as follows:
- Understand your objective, business and data
- Prepare the data for analyses
- Build and test the models
- Generate reports.
Our experienced teams of business analysts, statistical modelers, IT professionals & professional data mining operators are dedicated to provide complete and accurate data mining work using a combination of industry experience, state-of-the-art modeling science and new information from surveys of customers.
Data Integrity: One of the key issues raised by data mining technology is data integrity. Clearly, data analysis can only be as good as the data that is being analyzed. A key of implementation the challenge is integrating, conflicting or redundant data from different sources. For example, a bank may maintain credit cards accounts on several different databases. The addresses (or even the names) of a single cardholder may be different in each. Data cleansing processes must translate data from one system to another and select the address most recently entered.