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Documents related to » security of data


Six Steps to Manage Data Quality with SQL Server Integration Services
Six Steps to Manage Data Quality with SQL Server Integration Services. Read IT Reports Associated with Data quality. Without data that is reliable, accurate, and updated, organizations can’t confidently distribute that data across the enterprise, leading to bad business decisions. Faulty data also hinders the successful integration of data from a variety of data sources. But with a sound data quality methodology in place, you can integrate data while improving its quality and facilitate a master data management application—at low cost.

SECURITY OF DATA: company, gender and social security number. You can select from exact matching, Soundex, or Phonetics matching which recognizes phonemes like ph and sh. Data matching also recognizes nicknames (Liz, Beth, Betty, Betsy, Elizabeth) and alternate spellings (Gene, Jean, Jeanne) 5. Enrichment Data enrichment enhances the value of customer data by attaching additional pieces of data from other sources, including geocoding, demographic data, full-name parsing and genderizing, phone number verification, and
9/9/2009 2:32:00 PM

Data Quality: A Survival Guide for Marketing
Data Quality: a Survival Guide for Marketing. Find Free Blueprint and Other Solutions to Define Your Project In Relation To Data Quality. The success of direct marketing, measured in terms of qualified leads that generate sales, depends on accurately identifying prospects. Ensuring data accuracy and data quality can be a big challenge if you have up to 10 million prospect records in your customer relationship management (CRM) system. How can you ensure you select the right prospects? Find out how an enterprise information management (EIM) system can help.

SECURITY OF DATA: fields–and others like social security, account number, log-in ID, or account name–are blank, your ability to identify similar or related records across systems, or even within the same repository, is impacted–accentuating your duplicate record problem because you needed those fields to match against to identify similarities. THE WRONG DATA Wrong data is simply that–the data is incorrect. There are a number of ways that wrong data grows within your system. Age is one. People move (change
6/1/2009 5:02:00 PM

Oracle Database 11g for Data Warehousing and Business Intelligence
Oracle Database 11g for Data Warehousing and Business Intelligence. Find RFP Templates and Other Solutions to Define Your Project In Relation To Oracle Database, Data Warehousing and Business Intelligence. Oracle Database 11g is a database platform for data warehousing and business intelligence (BI) that includes integrated analytics, and embedded integration and data-quality. Get an overview of Oracle Database 11g’s capabilities for data warehousing, and learn how Oracle-based BI and data warehouse systems can integrate information, perform fast queries, scale to very large data volumes, and analyze any data.

SECURITY OF DATA: all of your data security policies? How easily can you integrate the results of your statistical analysis with your data warehouse data? Within Oracle Database, all of these issues are solved simply due to the deep integration of OLAP, Data Mining and statistics. Data Mining Oracle Data Mining is powerful software embedded in the Oracle Database that enables you to discover new insights hidden in your data. Oracle Data Mining helps businesses to target their best customers, find and prevent fraud,
4/20/2009 3:11:00 PM

Four Critical Success Factors to Cleansing Data
Four Critical Success Factors to Cleansing Data. Find Guides, Case Studies, and Other Resources Linked to Four Critical Success Factors to Cleansing Data. Quality data in the supply chain is essential in when information is automated and shared with internal and external customers. Dirty data is a huge impediment to businesses. In this article, learn about the four critical success factors to clean data: 1- scope, 2- team, 3- process, and 4- technology.

SECURITY OF DATA: advantage Regulatory compliance (Homeland Security, etc.) - Raised visibility Sarbanes-Oxley HIPPA ER/ES Global Data Sharing Requirements Electronic Commerce Spreadsheets versus master data repository Supply chain collaboration & optimization - And the list goes on... Simply put data impacts: Ability to process orders Ability to share data among internal systems Ability to share data electronically with external sources Reliable reports Correct information for good decision making At a 2004 UCCNet
1/14/2006 9:29:00 AM

The Why of Data Collection
Data collection systems work; however, they require a investment in technology. Before the investment can be justified, we need to understand why a data collection system may be preferable to people with clipboards.

SECURITY OF DATA: The Why of Data Collection The Why of Data Collection Olin Thompson - November 3, 2005 Read Comments Introduction Data collection systems work. However, they mean an investment in technology. Before we can justify that investment, we need to understand why we may want to use a data collection system in place of people with clipboards. What is data collection? In a general sense, it is the manual or automated acquisition of data. That definition has evolved to mean various automated methods of data
11/3/2005

Governance from the Ground Up: Launching Your Data Governance Initiative
Although most executives recognize that an organization’s data is corporate asset, few organizations how to manage it as such. The data conversation is changing from philosophical questioning to hard-core tactics for data governance initiatives. This paper describes the components of data governance that will inform the right strategy and give companies a way to determine where and how to begin their data governance journeys.

SECURITY OF DATA: Governance from the Ground Up: Launching Your Data Governance Initiative Governance from the Ground Up: Launching Your Data Governance Initiative Source: SAP Document Type: White Paper Description: Although most executives recognize that an organization’s data is corporate asset, few organizations how to manage it as such. The data conversation is changing from philosophical questioning to hard-core tactics for data governance initiatives. This paper describes the components of data governance that will
3/21/2011 1:41:00 PM

Data Quality Strategy: A Step-by-step Approach
Success start with data quality strategy: a step-by-step approach.Read Technology Evaluation Centers (TEC) whitepapers. To realize the full benefits of their investments in enterprise computing systems, organizations must have a detailed understanding of the quality of their data—how to clean it, and how to keep it clean. The companies that approach this issue strategically are the companies that will be successful. Learn the six factors that go into a good data quality strategy, and find out how to go from strategy to implementation.

SECURITY OF DATA: addresses, phone numbers, social security numbers, and so on Financial data—dates, loan values, balances, titles, account numbers, and types of account (revocable or joint trusts, and so on) Supply chain data—part numbers, descriptions, quantities, supplier codes, and the like Telemetry data—for example, height, speed, direction, time, and measurement type Context can be matched against the appropriate type of cleansing algorithms. For example, title is a subset of a customer name. In the
1/25/2010 1:13:00 PM

Data, Data Everywhere: A Special Report on Managing Information
Data, Data Everywhere: a Special Report on Managing Information. Explore data management with sap netweaver MDM. Free white paper. The quantity of information in the world is soaring. Merely keeping up with, and storing new information is difficult enough. Analyzing it, to spot patterns and extract useful information, is harder still. Even so, this data deluge has great potential for good—as long as consumers, companies, and governments make the right choices about when to restrict the flow of data, and when to encourage it. Find out more.

SECURITY OF DATA: Databases | Ensuring Data Security | Proliferation of Data | Data Deluge | Data Everywhere | New Ways of Visualising Data | Data Streams | Flow of Data | Cross-system Data | Crunch Data | Exploiting Data | Basket Data | Data Mining | Seismic Data | Database | Social-Security Data | Industrial Revolution of Data | Masters of Data Mining | Collection of Data | Big Data | Raw Data | Proliferating Data | Data Scientist | Mountains of Data | Managing Data Better | Analysing Data Better | Data Exhaust | Data-ce
5/19/2010 3:20:00 PM

The Evolution of a Real-time Data Warehouse
Real-time data warehouses are common in some organizations. This article reviews the basic concepts of a real-time data warehouse and it will help you determine if your organization needs this type of IT solution.

SECURITY OF DATA: The Evolution of a Real-time Data Warehouse The Evolution of a Real-time Data Warehouse Jorge García - December 23, 2009 Read Comments Understanding Real-time Systems Today, real time computing is everywhere, from customer information control systems (CICSs) to real-time data warehouse systems. Real-time systems have the ability to respond to user actions in a very short period of time. This computing behavior gives real-time systems special features such as instant interaction: users request information
12/23/2009

The Truth about Data Mining
It is now imperative that businesses be prudent. With rising volumes of data, traditional analytical techniques may not be able to discover valuable data. Consequently, data mining technology becomes important. Here is a framework to help understand the data mining process.

SECURITY OF DATA: The Truth about Data Mining The Truth about Data Mining Anna Mallikarjunan - June 19, 2009 Read Comments A business intelligence (BI) implementation can be considered two-tiered. The first tier comprises standard reporting, ad hoc reporting, multidimensional analysis, dashboards, scorecards, and alerts. The second tier is more commonly found in organizations that have successfully built a mature first tier. Advanced data analysis through predictive modeling and forecasting defines this tier—in other
6/19/2009

Massive Data Requires Massive Measures
There’s a lot of “big” money to be made these days when it comes to the analysis of big data. Find out what the key components are in this special report. One thing we learned in the data warehouse and data management world is that when it comes to the analysis of big data, there is also a lot of big money involved in order to gain position. But is the analysis of extensive amounts of data really a key component for the corporate business world?

SECURITY OF DATA: Massive Data Requires Massive Measures Massive Data Requires Massive Measures Jorge García - November 5, 2010 Read Comments   From Sun Tzu’s The Art of War : In the operations of war, where there are in the field a thousand swift chariots, as many heavy chariots, and a hundred thousand mail-clad soldiers, with provisions enough to carry them a thousand Li, the expenditure at home and at the front, including entertainment of guests, small items such as glue and paint, and sums spent on chariots and
11/5/2010 10:03:00 AM


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