Friday, July 30, 2010

Harness the Torrent!

Today's multichannel organizations need detailed, accurate, customer, process and behavioral data to drive their business applications. The online channel is a perfect source of exactly this information.

A Speed-Trap system can capture and harness the data flowing from your online channel and deliver it as structured, focused, real-time datastreams, customer datamarts and detailed operational data stores - a single solution to all your data needs. See a data sheet on our warehouse solutions

Speed-Trap discusses the emergence and use of rich media applications on the web, (whether it is a simple movie, a Flash banner campaign or an entire Flash-driven website) and the increasing importance of being able to measure how visitors interact with not just the web page, but the applications within it.

Speed-Trap's ability to capture this data, to track the take-up of mobile technology and record the movement of web sites towards CRM 2.0, means an exciting gear change for the online industry.Speed-Trap have also developed a solution for Teradata customers as part of Teradata's IWI initiative. This product known as Speed-Trap Online Customer Intelligence delivers near-real-time detailed behavioral , transactional and environmental data on every customer and visitor directly into the Teradata warehouse.

This solution is part of the Teradata IWI (Integrated Web Intelligence) solution, which provides an integrated view of a business's online channel.

Teradata IWI solution data sheet





SOURCE:
http://www.speed-trap.com/harnessthetorrent.aspx?cid=SpeedTrapCIO&cre=WebDataCapture&gclid=CM2B1OyWk6MCFcZA6wodMSHLmw

5 S Philosophy to LEAN Data Management

OR IMMEDIATE RELEASE

5 S philosophy to LEAN Data Management published by Data Quality experts



Fareham, United Kingdom - March 20th January 26th 2009: DQ Global, the experts in data quality improvement, today announced its take on LEAN data management and how adopting the 5 S process can lead to and increase in marketing ROI, cut in marketing spend and a positive CSR ethic



Based on five Japanese words that begin with 'S', the 5 S Philosophy focuses on effective work place organisation and standardised work procedures. By following DQ's advice, not only will organisations data be lean and green, they will be on their way to securing the new BSI PAS 2020 certification.



The reasoning behind the 5 S's is to simplify your work environment, reduce waste and non-value activity whilst improving quality efficiency and safety. DQ Global have related this back to the data industry and compiled the following advice



Sort (Seiri)

Eliminating unnecessary waste (data), often by red tagging obsolete, unused and incomplete data.

Set in Order (Seiton)

"A place for everything, and everything in its place". By considering what is needed to complete your task, you can store and organise your data in the most effective and efficient way. The questions to reflect on are:

What data do I need to do my job? (e.g. CRM, marketing campaign, finance)

Where should I locate this data?

How many of this item do I need?

Shine (Seiso)

Once your data has been red tagged, all duplicates have been eliminated and it has been interrogated into the correct order is time to cleanse the data. Such cleansing (suppression against TPS, deceased, goneaway, address correction etc) should be carried out regularly to ensure data is keep valid and up to date. If these changes to the data are ignored, this could lead to a negative impact on the company's bottom line by increased costs and brand damage

Standardise (Seiketsu)

Now your data is fit for business use, you need to concentrate on standardising data best practises. Ensure everyone understands the data capture process, try to align departments with different databases and make sure communication is kept open between all staff members

Sustain (Shitsuke)

Your data has now gone from a 'right state' to 'the right state'. By removing friction and enabling smooth operations you now have a clear picture of your data but ongoing prevention is required to ensure all your hard work is not wasted. Prevention is better than cure so ensure all previous S's are adhered to and sustaining your new way of doing things should be second nature.



Martin Doyle, DQ Global CEO says "The UK direct marketing industry is responsible for producing 550,000 tonnes of paper mailings each year. With 1-5% of all public and private sector data inaccurate, 27k tonnes of mailings never reach the intended recipient or our wasted mailings. He continues "When you print and transport duplicate mailings by road, rail, plane etc. you are damaging the environment, alienating your clients and squandering precious budgets. Through LEAN principles we can all make a huge difference to the environment"



About DQ Global

With over 10 years' experience in data quality improvement, DQ Global specialises in easy to use, database-independent, data cleansing software which saves money and provides a platform for better informed decisions. Solutions range from simple desktop, through larger departmental databases to complex enterprise-wide single customer view and master data management initiatives. Head Quartered in Fareham, Hampshire, UK; DQ Global also has regional offices in North America and South Africa serving over 500 clients worldwide, including: Toshiba, Harvey Nichols, Standard Charter Bank, Pfizer and Siemens. www.dqglobal.com



For more information, please contact Michelle Soper-Dyer

Tel: +44 (0)23 9298 8303 Email: michelle.soperdyer@dqglobal.com
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SOURCE:
http://www.articlesbase.com/marketing-articles/5-s-philosophy-to-lean-data-management-826347.html

Belwo Clinical Trial Data Management

he processing, handling and retrieval of clinical data, is at the heart of a successful clinical trial. Managing data in every sphere of business is highly essential. Data management enables how an entire organization flows in a methodical, systematic and processed way. However, above and beyond any other field, data management is critical in dealing with clinical trial research material.

BelWo Data Management System:
Once the data is acquired, the management of the data is essential to the success of the trial. The collected data is located on case report forms (CRF) and stored in the clinical data management system
. Each case report form is named to avoid any mistakes. The data is checked for any typographical errors as well as any logical errors. Data coding is also done by the management system. Coding is done mainly to introduce medical terms into the attained data. In the end, the data is analyzed by regulatory authorities to approve or disprove.
BelWo provides a range of Clinical Data Management services to fit your specific clinical trials needs. The BelWo clinical trial data management team handles individual stand-alone services for smaller research trials to more complex data management for larger clinical trial studies.
BelWo’s objective is to convert the data into a reliable, accurate and meaningful trial output that meets international quality standards and regulatory guidelines. BelWo’s clinical data trial management services have defined systems that enable faster decision-making.

BelWo Services:

* Case report form (CRF) design
* Database design and study setup
* Data entry and verification
* Data validation and query resolution

BelWo Benefits:

* Increase quality of data captured during clinical research process
* Reduced burden placed on healthcare professionals conducting clinical trials.
* Improved patient care
* Improved product and patient safety

Our clinical trial data management has provided a positive effect on speed, efficiency and the results of clinical trial studies since 2002. By utilizing technology, our data management services and clinical trial expertise we help you get the most from your data. BelWo’s clinical trial data management provides accuracy, security and more control at a cost effective rate.
Retrieved from "http://www.articlesbase.com/health-articles/belwo-clinical-trial-data-management-822228.html"

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SOURCE:
http://www.articlesbase.com/health-articles/belwo-clinical-trial-data-management-822228.html


What is Data Management?

Data management in itself is a small word but has got many facets to it. It includes many a features and different areas. First of them is Data Modeling. In data modeling basically a structure is created for the data and then the data is stored according to that structure. This stored data should be given nomenclature in such a way that it is easy to relate to the other data. Also the data should be easy to fetch for the analysis. Also classify the data in various categories as possible so that all the data is not to be searched for fetching a particular kind of data.


Next step is the Data warehousing. It is related to the storage of the data. Many big organizations have different fully dedicated departments for data warehousing. Now comes the Data Movement. It is related to the movement or the transmission of the data from the place where it is stored or collected to the common database and then to the end user. This is a very sophisticated and sensitive thing. It has to be ensured that the data will be fully transmitted and would not be lost and the user will be able to use it without much of a hitch. If the data or a part of data is lost in the transmission then the end user might use the data that is not complete for the reports and analysis.


Now the next and most important step in the data management is the database administration. In it the recoverability, safety, reliability, accessibility and the improvement & testing of the database are done. Then comes the Data Mining part of the data management. In this the collected data is analyzed to find out the emerging trends and patterns. These patterns and trends are used by the organizations for the development and marketing of there products.

Visit us to get more information on topics like What is Data Management? and What is Change Management?.

Tags: reliability, accessibility, marketing, storage, facets, change management, data management, trends and patterns, nomenclature, data mining, database administration


SOURCE:
http://www.a1articles.com/article_957422_11.html

Business Intelligence Basics

Business Intelligence is an integral part of successful business management. Unlike most IT projects, the goal of BI is not just functionality of an application, but the data itself must be useful as a tool. This end result of BI should be higher net income, increased growth and efficient decision making.

Most businesses have very similar data needs for effective business management. Some of these could be

* Strategic direction and management of employees, goals, and objectives
* Software capabilities for at-a-glance decision making
* Basic but effective and efficient decision support capabilities, including training, customer support and project management

Some companies have a difficult time keeping up with these three items. Not only is it difficult to keep up to date with latest technology innovations, but also with user requirements and customer demands. It must be understood that no technology stands alone or becomes successful on its own. There are key attributes that help contribute to success of this business intelligence arena.
Successful Business Intelligence depends upon several different attributes. Certainly, proven IT development methods, such as effective software development life cycle programs drive success. These methods start with documentation of the business user needs and how information will drive specific actions.

Two attributes make BI a little different. BI projects must be driven by how information will be used. A good business intelligence architect will have to become a knowledge expert on business processes that drive data entry. The IT group building out a business intelligence system must be focused on the business as a whole with the system development knowledge.

The second attribute that is unique to any business intelligence system is the linkage of day-to-day activities to the company’s strategic goals and visions. In the BI arena, some users need to know what happened yesterday and others need to know what business trends have been over the years. No matter what, though, the information delivered must be actionable, accurate and timely.




SOURCE:
http://businessmanagement.suite101.com/article.cfm/business_intelligence_basics

Articles, tagged with "data management system"

Hiring Line/ Hire Straight! New insights into Global HR Management
By liza252 in Business
Everyday is different. Change is the only constant in our globalized world. And global economy is also changing at an equally frenzied pace. Multinational companies
need the right people at the right place at the right time it would be great if they get t...
06th April 2010
How to Get Effective Online Data Entry Outsourcing Services
By Offshore Data Entry in Business
In this competitive world, data entry and storage of data in multiple formats is of great essence to every business. Data is a very important part of any business. It makes the convenience of data whenever it is needed. Sometimes it becomes headache, but ...
15th March 2010
Network Switches
By carvinjane in Computer Hardware
A data management system of any size will eventually need network switches. In fact, a system that does not already have network switches is losing power, running less than efficiently, and costing the owner money they could use in other ways. Even the si...
05th March 2010
Database Management increases your customer potential
By Richard Rabins in Technology
Usually owners of a small business do not use database software as a marketing tool. But the fact is that it helps a lot in achieving success. Therefore, it is advisable to have database software. One can achieve a grand success by learning about the use ...
23rd February 2010
Consistency is the Key to a good Customer Database System
By Richard Rabins in Computer Software
One of the most important points made by a noted management guru Jim Collins during a recent industry conference was “the signature of mediocrity is chronic inconsistency." Quite true indeed and this is true in case of the most organisations who find ha...
29th December 2009
The Growing Importance Of Document Management Systems At The Workplace
By hgfj in Business
One of the most popular features in the business world, document management solutions help to create, store and use data. Very simply put, this system helps you organize your data and any form of written documents in a systematic manner. When a business u...
24th November 2009
Cisco Switches
By coxon.simon in Computer Hardware
Cisco has been a leader for many years in advanced network management designs, and Cisco switches are a major part of that effort. Network switching capability is critical to the optimal operation of a system, whether it’s a small LAN or a larger data c...
24th November 2009
Network Switches
By coxon.simon in Computer Hardware
A data management system of any size will eventually need network switches. In fact, a system that does not already have network switches is losing power, running less than efficiently, and costing the owner money they could use in other ways. Even the si...
24th November 2009
Server Racks
By coxon.simon in Computer Hardware
Server racks are essential to any data management system. They allow for storing of multiple components in one central location. This serves two main purposes. The first is to create a sensible, practical system design that places routers, storage units, ...
24th November 2009
Comms Cabinet
By coxon.simon in Computer Hardware
The concept of using cabinets to house data processing equipment is an offshoot of railway switching systems, in which multiple relays and wiring harnesses could be placed in one central location. This idea is very suitable for data processing application...
30th September 2009
Database Management: Make It A Worthwhile Endeavor
By Richard Rabins in Technology
The contemporary system to ensure consistent success is very much related to astute networking. If you can establish a strong interlinking among different channels, things would certainly become a lot easier and effective in a longer run. Perceptibly, thi...
30th September 2009
Manage Your Data Astutely
By Richard Rabins in Technology
When it comes to synchronizing the diverse aspects of businesses, it is better to keep a backup system in place. Because the way things have been shaping up in the present world, there cannot a foolproof system – threats are always there. You need to eq...
25th September 2009
ERP software packages in a nut shell
By Antje Wilmer in Technology
Enterprise resource planning can be defined as company-wide computer software system that ensures management and coordination of all resources, information and productivity with in different departments of an organization. Complexity of this system is dir...
17th September 2009
Database Synchronization: Know The Basics
By Richard Rabins in Communications
There can be innumerable strategies and campaigns, which can be altered on a regular basis as per the changing marketing trends. But, when it comes to customer database, you cannot think to move ahead with your future plans, if there is not any database m...
17th September 2009
Database Management: Your Defined Move
By Richard Rabins in Communications
Modern corporate world presents a typical set-up of multifaceted applications, where all the features are connected among themselves. This is the well established harmonization of diverse processes that enables the big companies to do better amidst any so...
24th July 2009
Data Management: Efficient Controlling of Your Data
By Richard Rabins in Computer Software
Unquestionably, the most valuable assets of any company would be its data. Imagine if our computers stop working for one day, what would happen to our daily chores and pre-defined plans? We would certainly go back to stone ages – because these days all ...
24th July 2009
Maximize the Potential of a Database
By Richard Rabins in Computer Software
Many new technologies and innovative tools have fetched a distinctive and highly advanced form of data management system for us – after going through a rigorous phase of developments in a substantial time period. Every business whether it’s small or b...
23rd July 2009
Cache Your Data Ingeniously
By Richard Rabins in Computer Software
Frequent hacking threats, steady flow of data and considerable cost involved in data management – all these factors expound a typical modern day data related queries. Availability of latest tools and advent of innovative technologies have not only made ...
23rd July 2009
Clinicial Data Management
By domaingamer in Business
CDMS or Clinical Data Management System is put under use in relation with clinical researches. Clinical Data Management is the process of managing data for clinical trials. The data gathered during a clinical investigation is stated in a case report and i...
29th June 2009
Franchise Chocolate
By seoexprtz in Business
STACKS OF SNACKS snack franchise are contracted by MS Australia and Tackers Foundation to place these boxes in workplaces throughout Australia. MS Australia and Tackers Foundation recieve a payment for every piece of chocolate or sugar confectionery purch...
19th May 2009
Why Outsource Data Entry Service
By Anna in Business
In today’s world Data entry Services is the fastest growing and most popular segments in offshore outsourcing industry .The service range from simple text to alpha numerical entries. To meet the high quality of work, most outsourcing firms are employing...
19th May 2009
Why Outsource Data Entry Service
By Maximus Magnus in Business
In today’s world Data entry Services is the fastest growing and most popular segments in offshore outsourcing industry .The service range from simple text to alpha numerical entries. To meet the high quality of work, most outsourcing firms are employing...
25th March 2009
Trucking Software – Systematic Data Management
By Ray Donovan in Computer Software
Logical conclusion can be drawn from a set of data, only when it is systematically organized. Reasonable inferences cannot be extracted from randomly arranged data. Trucking software helps a trucking company to arrange all the operational data in such a s...
25th March 2009
BelWo Clinical Trial Data Management
By BelWo Inc. in Health & Healthcare
The processing, handling and retrieval of clinical data, is at the heart of a successful clinical trial. Managing data in every sphere of business is highly essential. Data management enables how an entire organization flows in a methodical, systematic.





SOURCE:
http://www.a1articles.com/tags-149663.html

What is Data Recovery? Is Data Recovery Software Needed? Read more at Suite101: What is Data Recovery? Is Data Recovery Software Needed? http://compu

The scenario is typically one such as this; John (fictional character) is going about his day-to-day life, minding his own business, working away on his computer when suddenly, it crashes. Maybe it was a virus or the motherboard that overheated. Or maybe John dropped his laptop or some other damage occurred which, regardless of what, he now needs to recover the information on his hard drive.
What is Data Recovery?

Wikipedia describes data recovery as “...the process of salvaging data from damaged, failed, corrupted, or inaccessible secondary storage media when it cannot be accessed normally”. To put it more plainly, some something has occurred which resulted in a person not being able to access their electronic device and they now need a way to recover their files. Thus, the actual process of data recovery involves using software and/or an alternate computer to recover files from the damaged computer.
Types of Data Recovery Situations

There are only two types of situations, where a person would need to have their electronic information ‘recovered’.


* Internal damage to the device
* External damage to the device

Internal damage refers to programs designed to alter the way the computer works. Commonly known as viruses (which can include adware and malware), the purpose of these programs is to disrupt how a computer normally functions. Given enough time, these programs can change file names, paths, directories, links, computer commands and partially or completely delete items, thus resulting in a ‘crash’.

External damage refers to something physical happening to the device. Exposure to heat or liquids (especially anything with sugar) can result in damage to the circuitry. Impact damage from dropping the device, or it being involved in an accident (such as a car accident), which may damage the memory storage unit (memory card or flash drive or hard drive).
Data Recovery: How Does it Work

Depending on the type of damage to the memory of the electronic device, data recovery can be as simple as plugging into a secondary device. For instance, if one were to drop their digital camera, the camera itself may be ruined, but the memory card may be have suffered no damage at all. In that instance, simply plugging the memory card into another device that could read it may be all that is required to retrieve the data. If there is no damage to the memory unit, then there is no need for data recovery software.


SOURCE:
http://computersoftware.suite101.com/article.cfm/what-is-data-recovery--is-data-recovery-software-needed



What is Master Data Management?

Master Data Management (MDM) can be defined as a way of providing consistent and core information across an organization. MDM is a challenging process and many professionals have been working on this for a long time. Master Data that essentially comprises core business entities such as customers, locations, products and so forth keeps on growing and basically consists of both traditional structured information and unstructured content such as documents and images. On the other hand, the augmentation of supply chain and compliance requirements has necessitated the upgrade of data quality.

However, the existing enterprise business application
lacks the ability to be used as a true system of record for the enterprise. Each of these applications is concerned only with the portion of master data that is required to process its own transactions. In fact, there is no existing system that looks at the master data with a holistic approach. However, when the business calls for an instant access to the high-quality information about chores business entities, the facts describing them become inaccurate, invalid and inconsistent which is buried in data transaction structures, content management systems, data marts, spreadsheets, emails, data bases and even on papers.



What Do Companies Use MDM?

The following are some of the benefits of using Master Data Management System.

Reduced Time To Market
Master Data Management offers a single system to create and maintain multiple product information, promotions, and accurate consumer communications using online as well as traditional media. It can also be used to reduce the time substantially in order to launch a new product in the market.

Supply Chain Improvement
Master Data Management can also be used to offer single, accurate definition of products and suppliers that eliminate duplication, increase buying power, and offer insight into supplier relationships.

Increased Revenue
Master Data Management adds to the customer relationship management. It helps create a complete view of customers there by enabling the sales, marketing, service teams to visualize the specific customer needs and improve customer services.


Better Integration
Many IT departments deploy Master Data Management to reduce integration cost, enable collaborating and improve business productivity. Besides, it can also be used cleanse and synchronize rich and accurate master information.



Maneet Puri leads LeXolution IT Services (LIT), a top-notch website design & development India and KPO company offering various web-based application and outsourcing web solutions to its international clients. Maneet has over 10 years of substantial experience in offering consultancy services on web-based applications including website maintenance services. For more information on web application and KPO solutions provided by LIT, visit www.lexolutionit.com. Maneet also owns a blog http://all-that-web-demans.blogspot.com where he shares his knowledge, experience and innovations related to web application services.

Tags: customer relationship management, content management systems, business application, data bases, single system, time to market, business entities, holistic approach, enterprise business, compliance requirements, existing system, data management system, accurate definition
This article is free for republishing
Source: http://www.a1articles.com/article_459378_16.html




SOURCE:
http://www.a1articles.com/article_459378_16.html

An introduction to ETL: Extract, transform and load for data warehouses

Building a data warehouse in a large and complex organisation is a complex task. The data being sourced usually resides in a number of transactional systems. These systems are built to manage transactions, to capture sales or purchases, add and maintain client information or to perform complex calculations on the data stored.

A data warehouse serves quite a different purpose. A data warehouse is built to supply management information. It provides a helicopter view of what is going on in the business. It provides a picture of a huge volume of information that is derived from the business. Often the data warehouse is used to populate multidimensional cubes known as OLAP - OnLine Analytical Processing - to allow for slicing, dicing and data mining.

The tables that comprise a data warehouse are built very differently from those used in the transactional systems. Transactional databases are built to be able to manage the access and manipulation of a large variety data for a single transaction. The design is usually relational using third of fourth 'normal form'. Typically there are many tables that can be linked using a series of keys. The problem of using this data to provide meaningful information is compounded by the fact that many organisations use many, sometimes hundreds of systems to manage their data.

The design of a data warehouse is quite different. One of the most popular methods is the 'star schema' as proposed by Ralph Kimball. The star schema consists of a fact table that contains all of the measurable information and a number of dimension tables that determine the levels at which the measures may be accessed.

A fact table may provide details of sales. The facts may include number of sales, value of sales and cost of sales. The dimensions could include product, organisational structure and region. Typically, you would use the dimensions to access the facts. You may use one or more dimension. You could use the regional dimension to obtain city, region and national sales results. You could add the product dimension to ascertain in which regions each product performs best.

A data warehouse allows for the mass extraction of information quickly and easily using aggregate or summary information.

One of the biggest tasks in building a data warehouse is the ETL - Extract, Transform and Load step. The design of a data warehouse (or a data mart) will determine what information is required.

Ralph Kimball identifies 38 processes that are required for the ETL





SOURCE:http://www.helium.com/items/1675374-an-introduction-to-etl-extract-transform-and-load-for-data-warehouses

The importance of managing information

Society is bombarded with data. Often so much information is received and as a result we feel overloaded! There's e-mail, web processing, instant messaging, voice mails, and a myriad of other means of communication that are constantly streamed to us daily. Dealing with the organization and management of information can be frustrating and make us want to pull our hair out, but nonetheless, it must be given serious attention.

Businesses are driven by data, and the information derived from data is an extremely valuable asset in today's environment and most organizations are dependent on its availability and accuracy. The electronic age has brought new challenges in managing information that previously was non-existent prior to computers and the Internet. While technology has generated higher efficiency, access, and integrity with the newer ways to organize and manage information, it can be overwhelming. Managing data information has become a job in itself, yet it is an important one.

In today's global economy, networks have created ways to rapidly transmit data. As a result managers must be able to obtain current data quickly in order to make informed and up to date decisions. This requires readily accessible information at their fingertips in order to analyze. Instantaneous recall information is often crucial, and in order to achieve this, efficient file management is a must. How can information management be effectively orchestrated?

In days past people used notebooks, filing cabinets and other manual file management systems. Unfortunately in today's competitive atmosphere this is counter-productive. The key to solving this dilemma is to install information systems that can acutely address business processes and meet the organizational goals in a timely manner. Software applications have revolutionized the way data is sorted, collected, stored and analyzed. Managerial understanding of how these programs can increase the value in a business is critical in order to remain viable in any competitive industry. Another benefit to this is that it gives the organization a shared knowledge base, something that was absent when working off of filed papers.

Information systems are implemented to manage data and turn it into something valuable - knowledge. A word of caution - computerized information management is only useful if strategically implemented. Misinterpreting the benefits of this will result in poor organization of information. If the latest technology is put into place without being in sync with organizational needs, it will be useless and this form of information management will create more liabilities than benefits. While the technical aspects are significant, what's more important is being able to correctly identify mission objectives and properly assess what is entailed in task processing. When this is sufficiently analyzed, it's easier to put together information systems that can reflect and meet the needs of the company.

An organization that is effectively is able to generate knowledge from data to and turn it into valuable information is well on its way. Information today is driving society. Finding the solution to successfully managing it, however, is the gateway to unlimited potential.




SOURCE:
http://www.helium.com/items/637629-the-importance-of-managing-information

Advantages of Database Management Systems

Database is a software program, used to store, delete, update and retrieve data. A database can be limited to a single desktop computer or can be stored in large server machines, like the IBM Mainframe. There are various database management systems available in the market. Some of them are Sybase, Microsoft SQL Server, Oracle RDBMS, PostgreSQL, MySQL, etc.

The advantages of the database management systems can be enumerated as under:

Warehouse of Information
The database management systems are warehouses of information, where large amount of data can be stored. The common examples in commercial applications are inventory data, personnel data, etc. It often happens that a common man uses a database management system, without even realizing, that it is being used. The best examples for the same, would be the address book of a cell phone, digital diaries, etc. Both these equipments store data in their internal database.

Defining Attributes
The unique data field in a table is assigned a primary key. The primary key helps in the identification of data. It also checks for duplicates within the same table, thereby reducing data redundancy. There are tables, which have a secondary key in addition to the primary key. The secondary key is also called 'foreign key'. The secondary key refers to the primary key of another table, thus establishing a relationship between the two tables.

Systematic Storage
The data is stored in the form of tables. The tables consists of rows and columns. The primary and secondary key help to eliminate data redundancy, enabling systematic storage of data.

Changes to Schema
The table schema can be changed and it is not platform dependent. Therefore, the tables in the system can be edited to add new columns and rows without hampering the applications, that depend on that particular database.

No Language Dependence
The database management systems are not language dependent. Therefore, they can be used with various languages and on various platforms.

Table Joins
The data in two or more tables can be integrated into a single table. This enables to reduce the size of the database and also helps in easy retrieval of data.

Multiple Simultaneous Usage
The database can be used simultaneously by a number of users. Various users can retrieve the same data simultaneously. The data in the database can also be modified, based on the privileges assigned to users.

Data Security
Data is the most important asset. Therefore, there is a need for data security. Database management systems help to keep the data secured.

Privileges
Different privileges can be given to different users. For example, some users can edit the database, but are not allowed to delete the contents of the database.

Abstract View of Data and Easy Retrieval
DBMS enables easy and convenient retrieval of data. A database user can view only the abstract form of data; the complexities of the internal structure of the database are hidden from him. The data fetched is in user friendly format.

Data Consistency
Data consistency ensures a consistent view of data to every user. It includes the accuracy, validity and integrity of related data. The data in the database must satisfy certain consistency constraints, for example, the age of a candidate appearing for an exam should be of number datatype and in the range of 20-25. When the database is updated, these constraints are checked by the database systems.

The commonly used database management system is called relational database management system (RDBMS). The most important advantage of database management systems is the systemetic storage of data, by maintaining the relationship between the data members. The data is stored as tuples in a RDBMS.

The advent of object oriented programming gave rise to the concept of object oriented database management systems. These systems combine properties like inheritance, encapsulation, polymorphism, abstraction with atomicity, consistency, isolation and durability, also called ACID properties of DBMS.

Database management systems have brought about systematization in data storage, along with data security.

By Bhakti Satalkar



SOURCE:

http://www.buzzle.com/articles/advantages-of-database-management-systems.html

Data Management Companies


Data management covers the entire system associated with organizing data as a helpful resource. Mainly, data management is the consolidation of information in such a way that data is easily maintained and capable of being retrieved when required.

The competitive environment requires businesses to capture, process, and study huge volumes of data. It may not always be possible for businesses to handle all the data that forms a part of their functioning. Apart from structured data, businesses are also required to deal with unstructured data in the form of e-mail, and images. These are to be stored for different strategic, trade and regulatory needs. Hence, businesses employ the services of data managers to handle their resources. Data management companies handle data with a comprehensive plan, which encompasses human as well as technological aspects. These aspects combine to achieve the basic aim of maintaining data without any fuss, and facilitating easy retrieval of data whenever required.

Data management companies assist businesses in developing policies and systems desirable to identify and exploit market opportunities. They help businesses in satisfying the changing demands of customers. These companies have professional specialists who have years of expertise in this field. They aid businesses in obtaining, replicating, transforming, and managing data to provide it to executives for the purpose of decision-making.

Data management companies also deal with data mining. Data mining utilizes computing power and highly developed analytical techniques to determine useful pattern relationships from large databases of the customers.

Data management companies can mine the data of their clients, or the data that they collected from their clients. Most importantly, they can show their clients how to understand data and use it to their best advantage. The data mining services offered by companies combine leading tools and statistical analysis techniques to create strategic intelligence from corporate data. This involves examining historical detail transactions to identify trends and establish and disclose hidden relationships for future predictability.

Data Management provides detailed information on Data Management, Data Processing And Management Services, Data Management Software, Clinical Data Management and more. Data Management is affiliated with Data Recovery Services.




SOURCE:

http://ezinearticles.com/?Data-Management-Companies&id=428878

Flexible Customer Data Integration Solution Adapts to Your Business Needs

Flexible Customer Data Integration Solution Adapts to Your Business Needs

Customer data integration (CDI) has become one of the buzzwords within the master data management (MDM) industry. Although the concept of creating a single organizational view of the customer is noble and desirable, its value should also be justified by organizations. To implement a customer data hub that only creates a centralized view of an organization's customer-related data does not affect a company's bottom line, unless business units have bought into the initiative and tie it to the organization's strategy. Customer turnover, collections, call centers, and marketing initiatives can be monitored, consolidated, and improved through CDI. However, to ensure successful CDI implementations, solutions should be driven and managed by the business units to ensure buy-in, and to increase the value associated with customer-related data.

In addition to the collaboration and buy-in needed to ensure a successful project, the type of CDI initiative and the architectural style chosen to implement it play important roles in the use and view of customer data. CDI hubs are used differently depending on the way they deliver information to users. It becomes important to choose a style compatible with the organization's current business needs, with the knowledge that these needs will change over time, and that as a result, the CDI architecture may change as well.

An Overview of Siperian's Product Offerings

Siperian is a leading San Mateo, California (US)-based CDI vendor for the health and life sciences industry. The vendor's solutions allow customers to create, consolidate, and present a single view of customer-related data based on their organizations' needs and maturity within their CDI or MDM environments. Siperian's product offerings reflect the business needs of organizations, and provide businesses with the ability to reduce operational costs and improve compliance when implemented in alignment with the organizations' business processes. This occurs through the management of customer-related data by creating a singular view of the customer across the organization, and by providing the appropriate views of that data to business units across the organization, based on their needs.

CDI hubs enable organizations to develop centralized customer data management structures, and to contribute to the ongoing data quality activities required to ensure successful CDI initiatives. Different hub styles, coupled with vendor product offerings, provide organizations with the ability to build and structure their customer information to enhance the customer experience, and to supply employees with the right information when they need it.

Siperian offers three products with differing architectural styles, namely Master Identity, Master Data Management, and Operational Views, to meet the varied requirements of an organization's customers based on the maturity of its CDI environment. These styles provide organizations with different benefits based on the way these organizations choose to apply CDI. Organizations may want a total approach to CDI immediately; that is, to manage their organization-wide CDI and MDM initiatives from the start. However, the implementation of a CDI initiative in stages provides organizations with stronger frameworks to develop and maintain their CDI environments and data quality initiatives over time.

Siperian's Master Identity offers organizations a master reference to link customer-related data across the organization. The Customer-Centric Master Data Management style creates a cross-reference to provide one version of customer data within the organization. This includes the cleansing of data to provide one version of the customer addresses, as well as other information that requires reconciliation across various systems. Customer-Centric Operational Views creates a virtual view of consolidated customer records based on customer transactions.

All three styles provide reliable master data to operational systems such as enterprise resource planning (ERP) and customer relationship management (CRM). Siperian also supports a "consolidation style," which relies on the centralized repository of reliable master data. This centralized repository supports downstream analytical environments, including reporting, analytics, and business intelligence (BI) systems.

Master Identity, through its Master Reference Manager (MRM), identifies entities including customer, product, supplier, etc. Master Identity uses a "registry-style" approach to match and link records from different systems across the organization to create a "golden record." This record creates references based on attributes such as customer number, a combination of phone number and name, and other unique identifiers to link the various records across the organization, creating a central reference area to pool data.

The registry stores data that can be cross-referenced back to the source systems. This hub style may be accessed in real time, and provides read-only access to data as needed. This means that operational data stores are not affected, and that data can be accessed instantaneously across multiple business units, helping users within customer service and marketing departments access the required information. This type of architecture does not allow organizations to add or change data, as the data acts only as a reference point. Therefore, it is not advantageous for point-of-contact users, who are required to update operational systems.

Master Data Management (MDM), through Hierarchy Manager (HM), manages and visualizes the relationships between these master data entities within the centralized customer repository. Based on this, a single, consolidated view of customer-related data can be presented, centralized, and managed from across multiple applications and lines of business. Corporate acquisitions provide a good example of how this architecture style can be applied to organizations. Hierarchy Manager identifies and consolidates the data from across multiple corporate entities to create a singular view. This architectural style allows individual or single organizations to manage their data quality activities across the organization, harmonizing the customer view across operational systems, and maintaining greater consistency across the organization. This provides the foundational building blocks to create a single view of the customer.

The third style, Operational Views, through Activity Manager (AM), accesses the reliable master data in the centralized repository, and then aggregates and identifies the associated transactions and interactions that take place with the customer. A federated view of the data combines with the master data to reference and deliver a full view of the customer within the business context required, and writes back data to the operational systems to maintain a single view of data across the organization. Data is integrated and systems are built to leverage the data views without worrying about integration with existing systems. Large organizations with high transaction volumes use this style to manage customer-related transactions. Many organizations adopt this style as a natural outgrowth of the MDM approach, as they see the benefits of expanding their usage of CDI to include daily operations.

Product Strengths

Siperian's strengths are in its ability to match its offerings to an organization's business needs. In addition to offering three distinct hub styles, Siperian centers its offerings on the integration of an organization's CDI initiative with its overall data management strategy. This means that although Siperian focuses on providing solutions for a specific set of data, the vendor places importance on an organization's overall business processes and how its CDI requirements will grow.

Siperian's service-oriented architecture (SOA)-based platform solutions enable organizations to implement the vendor's product offerings more easily than those of vendors that have not adopted SOA standards. Siperian's SOA-framework solutions provide organizations with the ability to integrate multiple applications onto a common platform. Siperian's hubs use additional adapters to leverage SOA and to integrate out of the box with information systems and platforms such as SAP, Oracle, and Siebel. Because many organizations build their information structures based on these three platforms, the natural integration of Siperian's offerings with these products ensures seamless integration.

Siperian delivers out-of-the-box, industry-specific models within the following industries: health and life sciences, financial services, high-tech, manufacturing, and communications and media. Companies in these industries can take advantage of these models, as these models meet many of their requirements out of the box, eliminating the need for excess customization. Siperian's expertise within these industries translates into additional value for the customer, including enhanced features and functionality, additional services, and superior support.

Challenges

Although Siperian provides enhanced CDI offerings to its customers, in 2005, the vendor expanded into vertical markets outside of the health and life sciences industry, such as the financial services and manufacturing sectors. This means that organizations in these vertical markets that are considering implementing a Siperian CDI hub should evaluate and compare the vendor's offerings to other vendors that are more mature within these markets. In addition to market penetration presenting a challenge to Siperian, data hubs built based on industry-specific needs may not be as advanced as those of Siperian's competitors, and therefore might require more customization within user organizations.

The focus on an organization's CDI solutions within an MDM framework poses a potential risk, as MDM connotes the organization's overall commitment to managing data and its relationships. Realistically, in many organizations this is not the case, as organizations cannot even agree on the definition of a customer. Although integration of an organization's processes with technology increases the likelihood of a project's success, actual stewardship of that process may have to be defined on a smaller level. This creates the inability in many organizations to implement an organization-wide CDI initiative, which lessens the likelihood of increased adoption of CDI hubs within the organization. Hence, organizations implementing CDI for the first time will need to obtain management buy-in from the business units involved. Without this buy-in, the actual software solutions may be useless if they can't be managed properly.

Conclusion

By having a single view of the customer, organizations can improve customer turnover, collections, call center activities, and marketing initiatives, thereby enhancing the bottom line. However, a single view of the customer within the organization requires discipline, buy-in from management and users, and alignment of the organization's technical architecture with its business strategy.

Before selecting from the CDI hubs available, an organization should evaluate the maturity of its CDI within the organization, and its current and future customer data requirements and architectural requirements. Organizations with immature CDI strategies, or those lacking CDI experience, should consider Siperian because of the vendor's expertise in and commitment to helping organizations align their CDI initiatives with organizational processes. Additionally, organizations facing rapidly evolving customer, business, or architectural environments and requirements should consider Siperian because of the compatibility and flexibility of the vendor's three CDI hub architectures. Lastly, organizations in the health and life sciences industry should consider Siperian because of the vendor's vertical expertise in this area.

About the Author

Lyndsay Wise is a research analyst for business intelligence (BI) and performance management. She has over seven years of IT experience in business systems analysis, software selection, and implementation of enterprise applications, globally. Wise has been featured in numerous publications covering topics such as BI, data integration, enterprise performance management (EPM), and customer data integration. In addition, she has written a number of articles covering major vendors in the BI industry. Wise can be reached here.











SOURCE:
http://www.technologyevaluation.com/research/articles/flexible-customer-data-integration-solution-adapts-to-your-business-needs-18931/

Business Intelligence Basics

Business Intelligence is an integral part of successful business management. Unlike most IT projects, the goal of BI is not just functionality of an application, but the data itself must be useful as a tool. This end result of BI should be higher net income, increased growth and efficient decision making.

Most businesses have very similar data needs for effective business management. Some of these could be

* Strategic direction and management of employees, goals, and objectives
* Software capabilities for at-a-glance decision making
* Basic but effective and efficient decision support capabilities, including training, customer support and project management

Some companies have a difficult time keeping up with these three items. Not only is it difficult to keep up to date with latest technology innovations, but also with user requirements and customer demands. It must be understood that no technology stands alone or becomes successful on its own. There are key attributes that help contribute to success of this business intelligence arena.

Successful Business Intelligence depends upon several different attributes. Certainly, proven IT development methods, such as effective software development life cycle programs drive success. These methods start with documentation of the business user needs and how information will drive specific actions.

Two attributes make BI a little different. BI projects must be driven by how information will be used. A good business intelligence architect will have to become a knowledge expert on business processes that drive data entry. The IT group building out a business intelligence system must be focused on the business as a whole with the system development knowledge.

The second attribute that is unique to any business intelligence system is the linkage of day-to-day activities to the company’s strategic goals and visions. In the BI arena, some users need to know what happened yesterday and others need to know what business trends have been over the years. No matter what, though, the information delivered must be actionable, accurate and timely.
Read on
Linux OS and Linux Applications as Part of a Data Center Solution
The availability of technical support contracts and applications has made Linux a very popular and good choice for those running servers in the data center.

To implement a great business intelligence system, the back-end work is critical. Planning and documentation are critical to success. Not only do these activities ensure the right data is delivered to the right person in the right format, the actual development time can be shortened. The planning phase should include source-to-target mappings, business rule definition, data metrics definition and rules for data usage. Once the backend is built out correctly, front end data extraction tools could be added on top for standardized and adhoc reporting needs.

Good business intelligence programs require not only excellent IT resources and knowledge, but must keep focused on how information is used to meet business needs. BI programs need to keep strategic goals and organizational missions in mind when recommending solutions, identifying opportunities and implementing new tools





SOURCE:
http://businessmanagement.suite101.com/article.cfm/business_intelligence_basics

Plant Intelligence as Glue for Dispersed Data?

Plant Intelligence as Glue

A series of TEC tutorials have looked at manufacturing plant-level systems addressing both the importance of plant level systems (see The Importance of Plant-Level Systems) and the difficulties in developing systems that integrate the needs of the shop floor and the back-office (see The Challenges of Integrating ERPs and MESs). One approach to addressing these challenges is to use plant or manufacturing intelligence systems.

Given the problematized communication between manufacturing execution systems (MES), plant automation, and enterprise applications, a new breed of applications are coming from the likes of former Lighthammer (now part of SAP), Kinaxis (formerly Webplan), Activplant, and Informance. They offer middleware analytical applications called manufacturing intelligence or plant intelligence that target other applications used to generate corporate-wide visibility of key performance indicators (KPI). These plant portal applications consolidate data taken from a wide range of computing sources—from plant floors, enterprise systems, databases, and elsewhere—and organize these data into meaningful, roles-based information, aggregating the data from disparate sources for analysis and reporting. Connections can through extensible markup language (XML), or open database connectivity (ODBC) standards, with communications managed by a protocol layer in the portal's Web server architecture.

Near real time visibility and transactional exchanges have to be created between enterprise applications and the plant floor with appropriate drill-downs to contextualize and understand the impact of specific manufacturing events. These products are applied to critical plant processes, and monitor production and provide the input required for calculating key metrics, such as overall equipment effectiveness (OEE). In order to increase OEE, data generated by equipment in a production line is acquired and aggregated (preferably in automatically, see The Why of Data Collection).

Information is contextualized using business rules and user roles to create and maintain consistent functional and operational relationships between data elements from these disparate sources. For example, these products can demonstrate the relationship between allowed process variables and ranges of time series-based quality and yield data. It can also analyze information by using business rules to transform raw process data into meaningful KPIs. Data can also be filtered for any noise/outliers; visualized with a context-based navigation and drill-down capabilities; and presented or propagated to determine the factors and root cause disturbances that slow production or impact quality. Ultimately plant-level systems allow decisions to be made that will speed up throughput and increase first-run production.

How It Works

Configuring data sources for integration can be done through templates, which is analogous to selecting a printer for an office application. The real trick, however, is in having sound plant-level models, which are frameworks that portray accurate plant-level context and data management, within the application sets. In manufacturing, even small changes to a master plan can create a so-called "reality gap" and these are historically addressed by last-minute panicking and scrambling, all the while, the business protagonists are not always (if ever) conscious of the impact or even the validity of their "educated guess" decisions.

Thus, these new software applications make it possible to model the cascading consequences of anything users do in response to an unplanned event (like a customer doubling an order or a machine breaking down), which in turn, makes it possible to understand how the other, intertwined parts of the user organization and supply chain will be impacted by a change (see Bridging the Reality Gap Between Planning and Execution).

When users have information about unplanned events and how their responses will impact the company, they should have manufacturing intelligence that can guide them through the forking paths of exception-based, decision-making. The value of the plant-level information indeed changes when enterprises use it to support higher-level, strategic, and tactical business processes. For example, data generated for a department supervisor or for management purposes has one value, and the same data used for Sarbanes Oxley Act (SOX) compliance has another (see Attributes of Sarbanes-Oxley Tool Sets). Moreover, the value of quality assurance (QA) information increases substantially when used to support enterprise-wide warranty issues.

Inventory information takes on a different look when viewed across an entire supply chain with synchronized schedules that are based on real demand. This greater value comes from changing from the data-centric view of separate manufacturing applications, to a process-centric view of integrated systems that can support company processes that have a higher impact. Manufacturing intelligence cannot work without its backbone enterprise resource planning (ERP) and MES system where all the transactional information resides. But ultimately, the actual mechanism for pushing changes through this backbone is manufacturing intelligence. It is thereby necessary to have a way to address both the planned and unplanned components of manufacturing in the same extended system.

User Recommendations

Examples of the potential benefits from the intrinsic integration of ERP with the plant floor and of achieving near real time information are multiple, and can be seen in The Importance of Plant-Level Systems and The Challenges of Integrating ERPs and MESs.

Common issues pertaining to interfaces in a best-of-breed environment might be mitigated (if not completely eliminated) with applications such as SAP's composite application SAP xApp Manufacturing Integration and Intelligence (SAP xMII, formerly Ligthammer CMS [Collaborative Manufacturing Suite]) or Oracle's innovative approach seen in Oracle Discoverer and Daily Business Intelligence. In Oracle's case, its application can seamlessly integrate performance metrics with transactional data, thereby incrementally updating performance metrics as transactions occur. Namely, when the data is stored in the same database, there is no need for the creation and management of ungainly interfaces, because there is only one master application. Data visibility becomes inherent, since by using the proper links, data can be gathered and disseminated in multiple ways, without delay.

Yet, in most cases, multiple databases on the shop floor, such as quality management data, production and warehousing real time transactions, plant maintenance data, ERP master data, etc. are rarely in sync, making timely decision-making difficult and often inaccurate. This holds true any time information is kept in more than one location, because without a highly advanced method of synchronization, the chances of having accurate data stored in more than one location are small indeed.

If data is only synchronized on a batch mode basis daily, or even by shift, managers have a difficult time making timely, accurate decisions, and this impacts all functions, such as production planning, shipping, inventory control, and purchasing. It also handicaps customer service representatives as they attempt to serve customer requests about the status of their orders. In the worst cases, some data is never synchronized to the master ERP system, which creates a serious communication void and promotesthe dreaded "islands of automation".

Users should, however, understand that the current generation of plant intelligence software mainly brings visibility, sometimes through composite applications, or the vendor will natively provide all the functional pieces. Its biggest asset is that it features real time event detection, workflow management, and interoperability. Nonetheless, it does not yet produce true intelligence required for trustworthy corporate performance management (CPM). These solutions often still lack business context and sophisticated production scenario analysis, which are featured by out-of-the-box data models for certain industries, for example, how to unlock constrained capacity, improve quality, reduce the cost of more frequent and lengthy changeovers, and improve profit margins. They may not have data life cycle management tools either, which allows for data reconciliation, data modeling, data cashing, yield accounting applications, etc.Analytics that add intelligence to the near real time data that enterprise systems can access through the portal toolsets may also be absent.

Thus, while the likes of SAP xMII can connect to and use real time data, many companies with intricate manufacturing plant-level processes will still, at least for the foreseeable future, need to leverage multi-vendor, best-of-breed solutions to create their own data management discipline and plant-level intelligence. They will also likely have to invest a lot of their own skills, experience, and intellectual property in constructing their manufacturing performance management systems.

When selecting these applications, in addition to using customary checklists of Web-enablement and platform support, users should ensure that the products use industry-standard frameworks, interfaces, and terminology, such as business to manufacturing markup language (B2MML), XML/Web services, ISA-95 and ISA-88 for batch process industries, and Microsoft Common Object Model (COM) and open connectivity (OPC) for application interfacing. Also, the candidate products should feature documented, "out-of-the-box" interfaces for popular ERP and plant automation applications (such as SAP, Oracle, Siemens, ABB, Rockwell, etc.), and to other control systems like distributed control systems (DCS) and programmable logic controllers (PLC). Given the dominant mantra of "intelligence", these products should logically include a strong and intuitive reporting function (ideally based on an "open" or pervasive products such as Business Objects' Crystal) and provide an integrated workflow management engine to model the production process.







SOURCE:
http://www.technologyevaluation.com/research/articles/plant-intelligence-as-glue-for-dispersed-data-18318/

Managerial issues: How organizations can promote data quality

Quality data is important to any organization. Businesses, agencies and other kinds of organizational structures rely on data to be up-to-date, accurate and relevant to the mission of the organization.

Organizations that do not maintain strong quality in their data transactions typically learn that many problems can arise due to poor maintenance of data quality.

Issues such as:

*Bad decision making
*Lack of uniformed decision making
*Error prone records
*Costly errors

As a solution to poor quality data, organizations should strive to develop good database design and data models. These should contain strong security methods built into them and network resources. While these are great first steps, they are not enough to ensure good data quality.

One of the best ways organizations can promote good data quality is to carefully plan integration of the information systems right from the beginning. As a part of this development, decision makers should consider including all user levels to be involved in the process.

The benefit to this is it allows both management and system developers get a full picture of the organization's business requirements and needs. The more complete the development process is, the better off data quality is over the long term. In addition, development should also consider potential expansion and future business growth.

As the organization's members begin the development of an information system(s), these are some of the points that should be considered in the development process:

*Accuracy of taxonomy
*Precisely defined records
*Precision in how data is recorded and the methodology used
*Precise documentation, including updates, changes, additions or deletions
*A well-defined method of transmitting data

After the system is established, the organization should perform data quality audits on a regular basis. In addition, data cleansing should be conducted periodically to allow for better data consistency. Engaging in these activities will increase data integrity and overall quality.

Additionally, employees should be trained to eye over data before final submission into database, and not get careless. Since typos can be easily made, getting into the habit of proofing data entry will only add to better data quality.

Poor data quality, which includes bad or incorrect data will result in errors. An information system database plagued with inaccuracies has a much higher probability poor business decisions being made. Managers and employees who base their decisions on the information in error-laden databases are going to find themselves making decisions on wrong or inaccurate data.

Ultimately poor decisions can result in major financial loss for the organization, including but not limited to a recall of products. Striving hard to create good data quality in an organization is an important goal to work towards. Poor quality can result in problematic issues for the organization, stakeholders, consumers and the general public. It is in everyone's best interests when an organization pays attention to data qualit




SOURCE:
http://www.helium.com/items/1711157-managerial-issues-how-organizations-can-promote-data-quality

Empower Your Business With Efficient Data Management and Backup

This article deals with data management and data backup - two of the most critical Information Technology practices in today’s business enterprises.
Today’s business world is highly dynamic. The business environment can change color like a chameleon and can make a large business conglomerate topsy-turvy within months. The strength of a business house lies in its data, and the quality information that can be derived from the data. Therefore management and protection of data are the two highly critical activities of the IT department of a company. In this article, we will briefly explore Data Management and Data Backup, two of the most important functions that charter the success or failure of an enterprise in the long run.

Data Management

Data management involves the smooth and efficient execution of common file movement functions like copy, move and deletion. Data management aims at achieving a system where you can retrieve the files easily and quickly. It also ensures that you have up-to-date copies of important files so that you can access them in the event of a data loss.

Today’s volume of business data is growing in geometric proportions. Advancements in Information Technology have armed IT practitioners with effective tools for data analysis and data management. Today’s databases (such as relational databases, object oriented databases) are highly efficient in optimizing the data for efficient search, sort, and presentation operations.

Secured Data storage is an important aspect of Data Management. Data management solutions are designed to track, monitor, and be on the guard to monitor usage of company data. Data should be accessible to all those who need them at the right place and at the right time. When demanded, sensitive data should not be shown to a person who does not have proper authentication.

Some of the important considerations for today’s data management practitioners are as follows:
- Scalability - Business houses of all sizes need to consider the scalability aspect of data management. Data storage, data retrieval, data backup (more about that later), and data security should be scalable enough to support the company’s expansion.
- Keeping pace with technology - Technology is changing very rapidly and one new technology tends to outsmart the other. Data management practitioners should keep themselves abreast with latest developments and upgrade their data management tools when needed.
- Efficient resource usage and audit - Data management practitioners should fish out those technology resources that are under-utilized. In today’s age of cost cutting, one cannot afford under utilization of a resource. Before going for a fresh spending on technology, the usage pattern of all resources should be mapped. A continual audit system helps to attain this objective.
- Access Control - Access to sensitive data must be controlled under a carefully devised policy. Access should only be granted to those people who are eligible to view the data. Each access attempt should be accompanied by an electronic audit trail for future analysis.
- Data Destruction - A company should also have a data (read electronic document) destruction policy in place. Imagine a period twenty years hence. Lot of data might become outdated and might need to be eliminated to free up storage place. However, before deciding upon a data destruction policy, the company should consider with legal counsel to learn about the probable time period after which the government may not summon those information any more. Data classification (such as critical, confidential, and public) is an important precedent activity before chalking out a data destruction policy.

Data Rescue: Things that you need to know about data backup and rescue.

By Sandy Taylor
Published: 5/14/2010




SOURCE:
http://www.buzzle.com/articles/empower-your-business-with-efficient-data-management-and-backup.html

Separation of Warehouse Data

One example of this characteristic is that a terabyte of data may have 50GB that are actively used and 950GB that are accessed perhaps only once a month or once a quarter. The organization pays the same for the data regardless of how frequently it is used. The data warehouse administrator can either archive the inactive data or place it in near-line storage. Accessing the inactive data, moving it to near-line storage, then deleting the data from the data warehouse defines the separation.

While it is true that all data warehouses face separation, the degree of separation varies among warehouses, based on these factors:

* Size of the warehouse
* Type of business the warehouse supports
* Who uses the warehouse
* What kind of processing is being done
* Level of sophistication of end-user analysts

Critical Success Factors

There are three critical success factors that each company needs to identify before moving forward with the issue of data quality:
* Commitment by senior management to the quality of corporate data
* Definition of data quality
* Quality assurance of data.

The senior management commitment to maintaining the quality of corporate data can be achieved by instituting a data administration department that oversees the management of corporate data. The role of this department will be to establish data management standards, policies, procedures, and guidelines pertaining to data and data quality.
Data Quality

In addition to referring to the usefulness of the data, data quality has to be defined as data that meets the following five criteria:

1. Complete
2. Timely
3. Accurate
4. Valid
5. Consistent

The definition of data quality must include the definition of the degree of quality that is required for each element being loaded into the data warehouse. If, for example, customer addresses are stored, it might be acceptable that the four-digit extension to the zip code, or the three-digit extension to a postal code, is missing. However, the street address, city, and state or province are of much higher importance. This parameter must be identified by each individual company and for each item that is used in the data warehouse.
Read on
Managing Customer Information
Many organizations in the public and private sectors collect personal data for marketing purposes, to evaluate their products services and to enhance profitability.

A third factor that needs to be considered is the quality assurance of data. Since data is moved from transactional/legacy systems to the data warehouse, the accuracy of this data needs to be verified and corrected if necessary, and this will often involve cleansing of existing data. Since no company is able to rectify all of its unclean data, procedures have to be put in place to ensure data quality at the source.
Modify Business Processes

This task can only be achieved by modifying business processes and designing data quality into the system. In identifying every data item and its usefulness to the ultimate users of this data, data quality requirements can be established. One might argue that this is too costly, but is has to be kept in mind that increasing the quality of data as an after-the-fact task is five to ten times more costly than capturing it correctly at the source.

If companies want to use a data warehouse for competitive advantage and reap its benefits, the issue of data quality is extremely important. Only when data quality is recognized as a corporate asset by every member of the organization will the benefits of data warehousing and CRM initiatives be realized



SOURCE:

http://customer-relations.suite101.com/article.cfm/separation_of_warehouse_data

The Role of PIM and PLM in the Product Information Supply Chain: Where is Your Link?

Introduction

By now, most of us understand the concept of the supply chain. This is the conceptual representation of product movement from raw material through manufacturing and distribution to the consumer. It's a problem that we've been working on for years and it is critical that we get it right.

Just as important, but not as evolved, is the product information supply chain where information moves from raw product data through value-adding edits and context setting, to retailers where it can be used to optimize the selling process. The current state of this information supply chain is abysmal. As one executive from a large consumer packaged goods (CPG) manufacturer recently said, "requests for product information scare me because many times we don't know where it exists in our organization, and if we can find it, it is most likely out-of-date".

The solution to the problem is product information management (PIM). A PIM solution would include the ability to organize a company's product information, regardless of location, into a consolidated system of records, and be able to synchronize or distribute that information to any business partners that require it.

Lately, diverse groups have been discussing PIM from the perspective of data synchronization and syndication, product lifecycle management (PLM), and enterprise publishing. Each of these product categories includes the management of product information, but each uses product information for a different operational role.

The term PIM has appeared more frequently lately in the discussion of data synchronization and syndication because of a number of market initiatives that act as catalysts for change. For example, many large retailers, including Wal-Mart, Office Depot, The Home Depot, Albertsons, and Safeway, asked their suppliers to synchronize product data via EAN/UCCnet registry and data synchronization services.

Other catalysts include the Sunrise 2005 initiative that seeks to standardize on a format for global product identification via a new 14-digit code, and the radio frequency identification (RFID) initiatives in place to bring about the rapid adoption of new radio frequency tags on all products, so that they may be more easily tracked through manufacturing and retail environments.

Because of these same initiatives, PLM vendors are also being increasingly asked by their customers to include more commercially-oriented product information in their PLM systems. A few high profile case studies (Procter & Gamble and Heinz are examples) have shown how the data in a PLM system (MatrixOne and Prodika, respectively) can serve as the source of valid, consistent and up-to-date product information for synchronization and syndication to supply chain partners. However, most PLM systems lack a PIM system's ability for secure, trusted synchronization of information to data pools like UCCnet.

Lastly, enterprise publishing's goal is to reduce costs to create and speed deployment of all the product-related information, including user manuals, sales collateral, and web sites, that make up the complete product offering. Enterprise publishing is a fascinating topic, but let's save it as a topic for future articles.

Origins and Evolution

PIM and PLM have different origins and were designed for different purposes. PIM originated from the need to optimize distribution channels by supplying them with the most current information on products and promotions. PLM, on the other hand, grew from the need to make better products through better management of engineering and design information around the manufacturing process.

Like most software solutions, PIM has evolved along a path on which the most pressing needs are met first. For CPG companies, the highest-priority need is data synchronization. When faced with mandates from their largest retail partners to synchronize product information via EAN/UCCnet, many CPG companies quickly implemented solutions to upload information to the registry.

However, they soon discovered that they did not have all the requested information, or a process for keeping the registry information up-to-date. A long-term PIM strategy requires integration with other systems, workflow, an information repository, and the ability to synchronize and syndicate information to a variety of destinations in multiple formats.

PLM's origin was the need to manage bills of material (BOM) or recipes, manage complex CAD drawings, and consolidate product design information in a centralized repository. To extend that capability, PLM adopted collaborative processes to share design information across the enterprise and with supply chain partners. PLM also added tools to help manage the new product introduction process, including project management to better track project progress through stages and portfolio management, to help target the most profitable products mix available. For more information on the history of PLM and the different aspects of a PLM system, see PLM Coming of Age: ERP Vendors Take Notice and The Many Faces of PLM.

There are a number of best-of-breed PIM players on the market, including FullTilt Solutions, GXS, IBM, SAP, Sterling Commerce, and Velosel. Each has a number of customers using their solutions, but the PIM market is just now heating up and the lion's share of the market is still up for grabs. Over the coming months Technology Evaluation Centers, Inc. (TEC) will create a PIM Evaluation Center to help companies determine which PIM solutions best fit their needs. In addition, TEC will continue to cover PLM solutions in the PLM Evaluation Center www.plmevaluation.com.




SOURCE:
http://www.technologyevaluation.com/research/articles/the-role-of-pim-and-plm-in-the-product-information-supply-chain-where-is-your-link-17863/

Belwo Clinical Trial Data Management

he processing, handling and retrieval of clinical data, is at the heart of a successful clinical trial. Managing data in every sphere of business is highly essential. Data management enables how an entire organization flows in a methodical, systematic and processed way. However, above and beyond any other field, data management is critical in dealing with clinical trial research material.

BelWo Data Management System:
Once the data is acquired, the management of the data is essential to the success of the trial. The collected data is located on case report forms (CRF) and stored in the clinical data management system
. Each case report form is named to avoid any mistakes. The data is checked for any typographical errors as well as any logical errors. Data coding is also done by the management system. Coding is done mainly to introduce medical terms into the attained data. In the end, the data is analyzed by regulatory authorities to approve or disprove.
BelWo provides a range of Clinical Data Management services to fit your specific clinical trials needs. The BelWo clinical trial data management team handles individual stand-alone services for smaller research trials to more complex data management for larger clinical trial studies.
BelWo’s objective is to convert the data into a reliable, accurate and meaningful trial output that meets international quality standards and regulatory guidelines. BelWo’s clinical data trial management services have defined systems that enable faster decision-making.

BelWo Services:

* Case report form (CRF) design
* Database design and study setup
* Data entry and verification
* Data validation and query resolution

BelWo Benefits:

* Increase quality of data captured during clinical research process
* Reduced burden placed on healthcare professionals conducting clinical trials.
* Improved patient care
* Improved product and patient safety

Our clinical trial data management has provided a positive effect on speed, efficiency and the results of clinical trial studies since 2002. By utilizing technology, our data management services and clinical trial expertise we help you get the most from your data. BelWo’s clinical trial data management provides accuracy, security and more control at a cost effective rate.
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SOURCE:

http://www.articlesbase.com/health-articles/belwo-clinical-trial-data-management-822228.html

Storage Capacity Reporting and Its Place in the Bigger Data-Management Picture


Storage capacity reporting itself is central to a wide range of key enterprise activities that revolve around capacity management. This range of services involves the creation, implementation and revision of plans as needed in order to allow users to properly handle all activities related to their storage needs.

The first activity is the production of a capacity management plan concerning where to store the data, its capacity, when it is to be upgraded and other key decisions. Top management crafts this plan in coordination with the recommendations of companies' IT groups and based on such factors as the number of employees, company activities, the state of the industry itself and so on. This plan is put to the test once the storage capacity systems are used. Through the use of capacity reporting solutions, IT personnel can monitor the service levels of the IT solutions employed over specified periods of time.

Depending on their findings - based on whether they find current solutions to be inadequate or excessive - a capacity manager may recommend tuning or changes to storage infrastructure in order to allow for better utilization of a company's capacity management solutions and resources. To this end, stored data capacity enhancements may be advocated. For example, the adoption of larger-capacity solutions that may allow for faster or less complicated storage or retrieval may be recommended to replace smaller-capacity devices that may also be slower to respond, thus sapping productivity. Since the needs of company divisions may themselves change as division workloads increase or decrease over time, those in charge of storage capacity reporting often input allowances in their recommendations should sudden mid-course corrections become necessary.

Resource Box:

APTARE is at the forefront of the storage capacity reporting industry. The company's acclaimed range of products includes the StorageConsole Capacity Manager, which provides a comprehensive view of storage allocation and consumption across the storage environment as a whole. Visit the company homepage at APTARE.com or call 866-9-APTARE to learn more




SOURCE:
http://ezinearticles.com/?Storage-Capacity-Reporting-and-Its-Place-in-the-Bigger-Data-Management-Picture&id=4714278

Where is Oracle in the Product Lifecycle Management Software Market?

Background

Over the past ten years or so, most enterprise resource planning (ERP) vendors have invested heavily in their product solution suites in order to extend their product portfolio beyond traditional integrated ERP components like financial accounting, human resources (HR), manufacturing resource planning, order management, inventory management, etc. Common application extension segments, generally built on the same technology platform as the ERP modules, include customer relationship management (CRM), supplier relationship management (SRM), supply chain management (SCM), and product lifecycle management (PLM).

At the ERP tier one level, which clearly includes SAP and Oracle, major efforts have been made to ward off the attempts of best-of-breed vendors to exploit revenue opportunities resulting from the gaps left by ERP. However, considerably different approaches have been taken by the two companies. SAP has taken the organic growth through internal development approach to application extensions, which can be time consuming and often results in a late-to-market outcry by clients in need. On the other hand, it often results in a product with the same look-touch-feel and process controls as the traditional ERP components and has better integration touch points. Oracle has taken a somewhat more risky and expensive "acquisition" approach for certain functional areas, as demonstrated by the acquisitions of Siebel for CRM, G-Log for multimodal transportation planning and execution, and Retek for unique retail applications and expertise. PLM, however, seems to be a more difficult puzzle for Oracle to solve. Internal development and commitment to the PLM solution set continues in earnest, while any speculation that their game plan may include a PLM acquisition alternative is just that, speculation.

Oracle PLM 11i.10

Oracle appears to have most of the key elements of a PLM suite. They provide the means for centralized product information, collaboration within and across the extended enterprise for product development, product configuration, and sourcing, as well as visibility and control of product portfolio management (PPM). They also offer a best-in-class Product Information Management (PIM) Data Hub product, and tout its PLM applications as providing the most advanced set of tools based on a single global product set repository. The current Oracle PLM Suite includes the following modules.

* Product Lifecycle Management is the core of the applications suite, with centralized product and component information within a single global catalog for product data management (PDM; items, structures, revisions, issues, change requests, roles, and project issues), as well as intellectual capital. Product change management and issue resolution is also addressed within this core module.

* Oracle CADView-3D provides visualization and mark-up capabilities for two dimensional (2D) and three dimensional (3D) modeling. Documents can have various formats, such as word-processing files, image files, computer-aided design (CAD) 2D or 3D drawing files, and other unstructured data formats.

* Project Management provides project planning, tracking, budgeting, and forecasting capabilities, as well as Project Collaboration software for reporting the project status to all stakeholders. Oracle Projects is integrated with the other relevant Oracle modules to manage projects like financials, HR, CRM, and procurement. It is geared toward new product development introduction (NPDI), with lifecycle phase gate product management. It also has built-in application programming interfaces (API) to Microsoft Project, Primavera, and Artemis, supporting a two-way interchange with desktop scheduling systems.

* Product Portfolio Management enables what-if analysis on product portfolio simulations, priority rankings based on investment, alternative cost and benefit analyses, and idea management.

* Oracle Sourcing integrates negotiation of supplier contracts with effective on-line management of the negotiation process as part of the PLM activities.

* Oracle Configurator manages customer needs with configuration rules for complex products and services as part of the sales cycle.

* Daily Business Intelligence manages the product data repository for analytics.

* Product Data Synchronization provides for Global Data Synchronization Network (GDSN) or Uniform Code Council (UCC)net services.

The Oracle PLM suite is built on a single e-business suite data model and technology platform, providing for seamless process integration and business intelligence, which are two of Oracle's strengths among its general business applications. Oracle's PIM Data Hub extends the Oracle PLM solution to synchronize product data spread across diverse business entities. Oracle PLM also integrates with Oracle Content Services to provide for application-specific document and content management. Finally, Oracle PLM addresses most of the needs and nuances of NPDI and addresses all of the salient compliance issues for both discrete and process industries.

While the PLM suite outlined above seems relatively comprehensive, it has been brought together over time as a conglomeration of existing modules that were not necessarily developed with PLM in mind, although there was also considerable product development that did have a PLM focus. Given Oracle's legacy in database management, it is no surprise that the product data management, business intelligence, and decision support capabilities are the strongest parts of the PLM suite. These strengths, coupled with acquisitions for guided selling (Concentra in 1998) and CAD viewing technology (Assentive in 2001), and a heavy dose of integration work and internal development have helped bring Oracle PLM to the level where it is a viable solution for its installed base. In fact, the suite has several key high-technology clients, and really has gained momentum in other key Oracle verticals.

During its Oracle E-Enterprise Suite 11i.9 rollout, Oracle espoused a renewed interest and heavy investment in SCM and PLM. This version has essentially the same components as those listed above, but with more depth and tighter controls for product collaboration. The version 11i.9 release also offers a slew of new features aimed at a number of selected focus industries, such as the aerospace and defense (A&D), automotive, telecommunications, consumer packaged goods (CPG), government, high-technology, healthcare, and life sciences industries. However, the product is not as deep in certain verticals as the products of best-of-breed vendors focused on specific verticals.

Does an Acquisition Make Sense?

A look at the PLM software vendor landscape reveals a paucity of choices. If Oracle were to acquire a pure-play PLM vendor, they would want to acquire a significant player with considerable depth and brand recognition in PLM, as well as a large and well-established installed base that could be farmed for years to come. The two largest PLM vendors by revenue, namely IBM Dassault Systmes and UGS, have revenues of over the billion dollar (US) mark. They are both growing and profitable. Selling out to Oracle is highly unlikely. PTC has an aggressive business strategy that could put them at the $1 billion (US) revenue mark by 2008, and they are performing well toward this goal. With such companies, the issue is not whether Oracle can afford one of the larger PLM vendors, but rather if an exit strategy of going to Oracle makes sense for them.

Agile and MatrixOne, perceived as the pure-play collaborative product development vendors in PLM, are both in the $100 million (US) range with moderate growth. Agile has a significant installed based of over 200 clients that use Oracle for the backbone ERP system, and they are an active Oracle partner on the infrastructure side, integrating Oracle's application server into its product offerings. Agile has been less willing, however, to get too close to Oracle on the applications front. MatrixOne is in a quandary relative to their financial reporting, and might be a more willing target for acquisition.

Given Oracle's rather low-key presence in PLM, an acquisition would certainly put them on the front page, and garner support for their PLM efforts going forward. In fact, some would argue that Oracle needs a significant and positive "booster" initiative on the PLM front, and that an intelligent acquisition could be the boost they need in order to be taken seriously outside of their immediate applications installed based. However, the current path of internal development with a focus on a PLM product enrichment roadmap may be the most logical course of action.

Summary

Fusion is the result of a major development effort to bring all of the diverse applications recently acquired neatly under one common data model and service-oriented architecture (SOA) platform, selecting the best elements of each and consolidating with the best of Oracle e-Business Suite. Using Fusion Architecture tools, Oracle hopes to have the consolidation complete by the end of 2008. With its focus on Fusion, Oracle has put a lot of effort into one course of action. This strategy may be paying off, as in early January Oracle announced its successful delivery on considerable Oracle Fusion commitments in 2005. In fact, since their introduction in January 2005, the combined Oracle-PeopleSoft organizations have defined the Oracle Fusion Architecture, seen broad industry adoption of Oracle Fusion Middleware, certified PeopleSoft and JD Edwards applications on Oracle Fusion Middleware, and released pre-Fusion applications.

Will PLM be another piece of the Fusion Applications puzzle? Oracle certainly intends for its PLM product set to keep pace with its other segments, like CRM and SRM. Getting to a position of strength in the PLM market will require continued and even heightened commitment; achieving the desired state through internal product development may be difficult in a short time frame. Acquisition of a best-of-breed PLM vendor would be a consistent strategy for Oracle, albeit a costly and unlikely one. In the December 2005 issue of Managing Automation, they referred to Oracle as "practically a PLM non-player". Not very good press for proud Oracle, which is usually considered a leader in most of the application segments in which it participates. Nor is it a fair critique. Oracle has garnered recent respect among its installed base with its PLM commitment, and is estimated to have grown its PLM revenues to over $100 million (US), which would place them in the middle of the PLM vendor pack. Oracle is most likely to stay the course and grow its PLM business through commitment and technology advancement along with the rest of the Fusion fold. Given this approach, Oracle installed base clients with a need for PLM capabilities would be wise to have Oracle on their list of potential vendor solutions.




SOURCE:
http://www.technologyevaluation.com/research/articles/where-is-oracle-in-the-product-lifecycle-management-software-market-18393/