The Definition

Data Management is the comprehensive series of procedures to be followed and have developed and maintained the quality data, using the technology and available resources. It can also be defined that it is the execution of architectures under certain predefined policies and procedures to manage the full data life cycle of a company or organization. It is comprised of all the disciplines related to data management resources.

Following are the key stages or procedures or disciplines of data management:

1. Database Management system

2. Database Administration

3. Data warehousing

4. Data modeling

5. Data quality assurance

6. Data Security

7. Data movement

8. Data Architectures

9. Data analysis

10. Data Mining

1. Database Management system:

It is one of the computer software from various types and brands available these days. These software are designed for specifically for the purpose of data management. These are few of these; Ms Access, MsSQL, Oracle, My Sql, etc. The selection of any one of these depends upon the company policy, expertise and administration.

2. Database Administration:

Data administration is group of experts who are responsible for all aspects of data management. The roles and responsibilities of this team depends upon the company's over all policy towards the database management. They implement the systems using protocols of software and procedures, to maintain following properties:

a. Development and testing database,

b. Security of database,

c. Backups of database,

d. Integrity of database, and its software,

e. Performance of database,

f. Ensuring maximum availability of database

3. Data warehousing

Data warehousing, in other words is the system of organization of historical data, its storage capability etc. Actually this system contains the raw material for the management of query support systems. That raw material is such that the analysts can retrieve any type of historical data in any form, like trends, time stamped data, complex queries and analysis. These reports are essential for any company to review their investments, or business trends which in turn will be used for future planning.

The data warehousing are based on following terms:

a. The databases are organized so that all the data elements relating to the same events are linked together,

b. All changes to the databases are recorded, for future reports,

c. Any data in databases is not deleted or over written, the data is static, readable only,

d. The data is consistent and contains all organizational information.

4. Data modeling

Data modeling is the process of creating a data model by applying and model theory to create data model instance. The data modeling is actually, defining, structuring and organizing the data using predefined protocol. Then the theses structures are implemented in data management system. In addition, it also will impose certain limitation on the database with in the structure.

5. Data quality assurance

Data quality assurance is the procedure to be implemented in data management systems, to remove anomalies and inconsistencies in the databases. This also performs cleansing of databases to improve the quality of databases.

6. Data Security

It is also called as data protection, this is system or protocol which is implemented with in the system to ensuring that the databases are kept fully safe and no one can corrupt by access controlling. The data security, on other hand, also provides the privacy and protection to the personal data. Many companies and governments of the world have created law to protect the personal data.

7. Data movement

It is one term broadly related to the data warehousing that is ETL (Extract, Transform and Load). ETL is process involved in data warehousing and is very important as it is the way data is loaded into the warehouse.

8. Data Architectures

This is most important part of the data management system; it is the procedure of planning and defining the target states of the data. It is, realizing the target state, describing that how the data is processed, stored and utilized in any given system. It created criterion to processes the operation to make it possible to design data flows and controls the flow of data in any given system.

Basically, data architecture is responsible for defining the target states and alignment during the initial development and then maintained by implementations of minor follow-ups. During the defining of the states, data architecture breaks into minor sub levels and parts and then brought up to the desired form. Those levels can be created under the three traditional data architectural processes:

a. Conceptual, which represents all business entities

b. Logical means the how these business entities are related.

c. Physical, is the realization of the data mechanism for specific function of database.

From above statements, we can define that the data architecture includes complete analysis of the relationship between functions, data types and the technology.

9. Data analysis

Data analysis is the series of procedures which is used to extract required information and produce conclusion reports. Depending upon the type of the data and the query, this might include application of statistical methods, trending, selecting or discarding certain subsets of data based on specific criteria. Actually, data analysis is the verification or disapproval of an existing data model, or to the extract the necessary parameters to achieve theoretical model over realty.

10. Data Mining

Data mining is the procedure to extract unknown but useful parameters of data. It also can be defined that it is the series of procedures to extract the useful and desired information from large databases. Data mining is the principle of sorting the large through the large amount of data and selected the relevant and required information for any specific purposes.

Just about everyone relies on computers these days. A great deal of information is kept on a number of hard drives and servers in both home and business environments. Knowing that there is always the possibility of a system failure, many people make sure to back up essential data. Here are a few tips to help you manage an effective and responsible data storage management process.

The first rule of thumb in any data storage management process is to set up a regular schedule for backing up the data. This may involve a schedule that is anywhere from a weekly backup to one that occurs two or three times a day. There are several factors to consider in setting up the frequency.

First, how often is your data updated? Second, how essential is your data to the operation of your business? Third, how much effort and expense would be involved in re-entering data if the last backup was done a week ago, versus yesterday? Responsible management begins with setting up routine backups that will make the best use of your time and other resources.

Next, there is the need to decide on the type of backup device you will use. Some people prefer to save data on disk. In environments where servers are used, many businesses choose to have the data on a primary server backed up to a secondary server. Still others choose to store the data on a remote server, via an Internet connection. All three are examples of effective data storage, since it is possible to retrieve all data up to the point of the last backup within minutes.

Last, review your backed up data now and then. The reason for this is to make sure your backup process is working properly. If you find that your current structure of data storage makes it hard to remember to routinely save some obscure file that is rarely used but is absolutely essential, then you may need to tweak your process. Also, there is a chance some data in some fields is not carrying over for some reason. Unless you do a periodic check, chances are you won't know this until a system failure and you find data is missing from the last backup.

For an IT business to grow and make handsome profits, you need to make sure that it follows various processes and use high-end solutions for data management and its security. Read further to know how various database management solutions help to streamline your business in an enhanced way and help you to take your business to the next level of success.

• Data Centralisation: Data centralization software merges all data at a central location and thus provides a consistent, secure data storage location. Data management processes become streamlined. With all data and processes in place ROI increases. Data centralization software can allow and restrict data access of users as per the need. >> Read about CIMS data centralization software.

• Real-time automatic disaster recovery : Interruptions in business service cause huge loss in productivity and profitability. Various real-time automatic disaster recovery toolkits help in recovering the data by instantly replicating the data that existed before the disaster to a secondary storage without any loss of data and without affecting the business operations. Hence, a real-time automatic disaster recovery toolkit is indispensable to almost all businesses. >> Read about the RDM and CMDR real-time automatic disaster recovery toolkit that offers real time replication across any Internet location

• Database Marketing: Database effective marketing involves utilizing your customer information for targeted marketing that is usually done through emails. Database effective marketing usually focuses only on the customers who have shown some interest in the products and services being offered by a company in the past. Database effective marketing if done correctly can surely lead to positive results and increased ROI. >> Find about DEX pro data management software allows you to do database marketing easily and cost-effectively.

• Data Cleansing Software: Today data is stored at different sources to reduce load on main server and for fast accessibility. With huge business databases, multi-source data cleansing or deduplication has become a necessity to provide correct information to its users. UK data cleansing software products available in the market can be uses to merge and purge your significant data. >> Read about multi-source data cleansing software Data De-Duplicator which has merge and purge facilities.

• De-duplication: The problem of duplicate data records is faced by almost all businesses. It results in difficulties in data management and efficiency of business operations. Data de-duplication software removes identical and redundant information resulting in neat seamless database records. >> Find about data de-duplicator software.

Working across Sap heterogeneous forums systems at multiple places, SAP Master Data Management leverages accessible IT assets in business-critical data, delivering greatly reduced data repairs charges and very useful for sap business jobs. Moreover, by ensuring cross-system data consistency, SAP Master Data Management speed ups the implementation of business processes for jobs. SAP MDM is a key enabler of SAP Enterprise Service-Oriented Architecture forums.

SAP is at present on its second iteration of MDM software. Facing restricted acceptance of its primary release, SAP changed path and in 2004 purchased a small vendor in the PIM space known as A2i. This code has happen to the basis for the presently shipping SAP MDM 5.5, and for itself, most analysts believe SAP MDM to be more of a PIM than a broad MDM product at this time.

The components & tools of SAP NetWeaver master data management integrates business courses across the comprehensive value chain, delivering features and functions to help: Master data consolidation, Synchronization and distribution of master data , Centralized management of master data, Administration of master data, Management of internal content, Catalog search, Print catalog customization , Multichannel syndication of product catalog content, Business process support and Business analytics and reporting.

There are five normal execution scenarios:

Content Consolidation, Central Master Data Management, Master Data Harmonization, Rich Product Content and Global Data Synchronization With the SAP (MDM), you can:

1. Control customer relationships efficiently through streamlined visibility across various systems 2. Simply allocate master data to assigned systems through automated distribute and subscribe models 3. Lessen the number of part masters maintained worldwide by removing duplicates 4. Analyze and statement on spending by part, supplier, or other master data 5. Negotiate superior sourcing contracts based on analytical insights 6. Lessen supply chain charges by ensuring exact exchange of data involving manufacturers and dispensers or dealers. SAP Master Data Management is the basis for harmonized, reliable information that can be offered to client applications across the enterprise. It offers you a great way to attain information steadiness across your business or jobs and IT landscape. It enables improved decision-making, translating chance charges into gains, and reducing the charge of IT maintenance. SAP Master Data Management allows you to go with information across myriad applications and topographies -- whether that details resides in SAP, non-SAP, or legacy applications. Therefore, you can lessen costs, develop decision-making, and attain business goals on jobs. The sap news says that SAP (MDM) increases the sap jobs search and by training this sap certified course education module, it supports and gives more vacancies for permanent sap jobs for all developers or trainers worldwide.

Mining data is one of the keys to running an effective business. Here’s a primer on effectively managing your business data to maximize the efficiency of your business.

Effective data management plays an essential role for any growing business. Information technology has generated advanced tools for analyzing and managing data. Use of these tools can improve the performance of almost any operation. Steps made in capturing mass data electronically have developed the need for effective management strategies. Getting more and more data and transforming it into usable information is a major concern of today’s services and industries.

New technologies require new expertise, internal procedures and decision-making methods. Earlier companies were creating electronic databases, which were non-relational and difficult to use. Now with the use of highly sophisticated software and high-speed computers, businesses are reaping huge benefits from the computer/information revolution. Businesses are continuously making steps in managing data by using various tools to optimize information for sorting, searching and presentation in meaningful formats.

Many software programs and database applications are available on the market that enable companies to manipulate data in real time, capture knowledge for future use, ease the progress of operations to save time and costs and also to coordinate operations with partners.

The amount of data storage necessary and the duration it is kept online is growing swiftly, yet resources to manage data are limited. Data storage is a test to those companies wishing to maximize the value of their available data and also a huge task for storage professionals to manage and protect this data. Enterprises are struggling to bring together highly reliable platforms that can recognize where data is located in a company and whether it is utilized efficiently. Data management solutions must track, monitor and be vigilant of the conditions of your company data. It should also manage and distribute data efficiently. It should unify and simplify the administration of storage infrastructure.

Data is growing exponentially. Companies need maximum scalability, performance and production for data rigorous applications. They also need an easy to use, backup tool that provides transparency to where and how data and storage is utilized. Before choosing such and important process as data management, be sure to research your options and go with a solution that is flexible and scalable.

Data security and data management are two of the most popular buzzwords in today’s world, where computerized maintenance management rules. Computerized maintenance management systems, or CMMS, are those systems, which are used to storage and maintenance of data. For instance, SAP and ERP are two such important systems.

SAP and ERP help people manage a database of information about an organization’s data maintenance and management operations. These software packages can also be used for taking work orders, managing the assets of an organization, controlling the inventory, and for preventive maintenance or keeping track of the various jobs and work orders.

Now, there is new software called facility manager. What has facility manager got to do with computerized maintenance management? Facility manager is an important paperless computerized maintenance management system, and a solution to control other maintenance management systems. It is used in various industries, including call center businesses, and also on helpdesks for maximizing productivity, increasing efficiency in the work and simplifying the tasks at hand.

Computerized maintenance management systems also help you to determine your liabilities and costs, and can also be used as an accounting system. Some of the computerized maintenance management systems come with the usual features, whereas others come with more sophisticated and advanced features, for a price.

You can get computerized maintenance management systems at your local retail software stores, or stores that stock computers. You need to consider the main product and its features before selecting the system. It must be preloaded with mechanical, bio-medical procedures and compatible with Microsoft Windows. Finally, buy the system from a trusted vendor.

What is Data Quality Management?
Data quality management is the process of tracking and analyzing the data in customer and business accounts, ensuring it's accurate and up-to-date. This includes periodic updates and cleaning, pruning data for old and outdated information, analyzing data fields, and ensuring all personnel have reliable data they can count on for lead management, integration, and much more. Data quality management typically follows the structure outlined below:

Planning a Successful Strategy
The primary step in implementing true quality data management is through implementing and planning a successful strategy for migrating and managing the data. This depends directly on the integrity of existing data and how it is consolidated and organized.

Unfortunately, many company's data systems are messy, with information and files spread across a number of different data field, often complete with duplicate or incomplete records. Thus a carefully planned, unifying data management strategy is a must; if your company is organized, all processes will run smoother and quicker.

Implementing Data Migration for CRM
Consolidating data into one source is one of the most important steps a company can take in managing the quality if the data that is fed into its system. Many companies struggle with multiple data sources, and this can waste valuable time and resources.

If an in-house sales team has different data than the team in the field, things can get sticky and spiral out of control. Thus, the correct CRM application can help a company migrate its data effectively, quickly, and efficiently, allowing for more success and a higher quality of data.

Cleansing and De-duplicating Data
Cleansed and de-duplicated data is data that has been purged of annoyances like duplicate records or content and user problems due to integrity issues. This aspect of a CRM strategy's quality data management is meant to guide a company through the process of cleaning data before or after it has migrated from one source to another. This results in a more efficient, less cluttered system that is optimal for users and ultimately increases productivity.

How does Data Quality Management affect CRM?
Data quality management is integral to the success of a strong CRM strategy because all the system's users, from the sales force personnel to the executives and marketing teams, have to access the same quality of data. A central source of qualified data ensures everyone is on the same page and knows what’s going on. This leads to a more effective sales team, better customer service, efficient lead management, and an overall more solid, targeted CRM program.

What happens if Data Quality Management isn't figured into your CRM strategy?
If data quality management isn't figured into your CRM strategy, it means that your company may be working with faulty, inaccurate information; this can lead to all sorts of unmitigated disasters, from cold, lost leads to unsatisfied customers and a confused sales force. Data quality management is essential to the way a company or organization does business; thus, a CRM strategy without data quality management is too weak to be truly effective.