Data Management

Manage the exponential growth of data within an organization requires a consistent framework to manage it and avoid turning it into chaos. This involves planning a structure to manage information taking into account a lot of risk factors.

Data management solutions encompasses techniques centered in governance, like data quality, data integration, among others. It includes all the practices necessary to manage data as a critical enterprise asset. These solutions comprises the development and execution of architectures, policies, practices and procedures in order to manage the information lifecycle needs of an enterprise in an effective manner.



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Data Quality includes techniques for data cleansing, data standardization, verification, profiling, monitoring, matching, householding, and enrichment.

Why data quality is so important?  

- To manage services accurately 

- To manage service effectiveness

- To prioritise and ensure the best use of resources

- To ensure that decision taking is in line with business goals  

Data Integration acquires data from sources and transforms and cleanses it. ETL (extract, transform, and load) is the most common form; others include ELT, customer data integration (CDI), data federation, database replication, and data synchronization. 

Data integration may be analytic or operational, and its importance affects directly the organization efectiveness. Some key benefits are:

- Reduce data redundancy

- Helps to ensure data qualitty

- Reduce IT resources

Master Data Management (MDM) includes techniques for acquiring, improving, and sharing master data across multiple internal IT systems, and often to partners or customers. MDM is related to data governance, which aims to improve data’s quality, share it broadly, leverage it for competitive advantage, manage change, and comply with regulations and standards.

Why Should You Manage Master Data?

Because it is used by multiple applications, an error in master data can cause errors in all the applications that use it. For example, an incorrect address in the customer master might mean orders, bills, and marketing literature are all sent to the wrong address. Similarly, an incorrect price on an item master can be a marketing disaster, and an incorrect account number in an Account Master can lead to huge fines or even jail time for the CEO—a career-limiting move for the person who made the mistake.

Data Lifecycle Management (DLM) is a policy-based approach to managing the flow of an information system's data throughout its life cycle: from creation and initial storage to the time when it becomes obsolete and is deleted. The effective management of corporate data has grown in importance because an increasing number of compliance regulations.

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Data Masking And Subsetting

Data masking (or obfuscation) is the process of hiding original data with random characters or data. Generally is followed by data subsetting tasks that allow obtain a portion of the original volume of data. The main reason for applying masking to a data field is to protect data that is classified as personal identifiable data, personal sensitive data or commercially sensitive data, however the data must remain usable for the purposes of undertaking valid test cycles. Mostly, this reason is supported by regulations that the organizations must comply.

Database Synchronization

Database Synchronization is the process of establishing data consistency between two or more databases, automatically copying changes back and forth. Harmonization of the data over time should be performed continuously. Pulling out data from source (master) database to destination (slave) is the most trivial case.

Database Administration

Database Administration includes functions of managing and maintaining database management systems (DBMS). Mainstream DBMS software need ongoing management and it's critical to have an expert to monitor and execute maintenance task relative to allow the normal function or to achieve specific improvements or tunings of the database system. 

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