The life cycle of data varies with the needs of a particular enterprise: For instance the analysis of a flight data recorder’s life cycle ends with the one flight. But if a comparative analysis needs to be completed among several flights over a period of time the life cycle becomes flexible. In general, data life cycle management (DLM) is a policy-based for a particular enterprise as it manages the flow of an information system goes through its life cycle starting with recording the data points, classification, analysis and storage for its usefulness until time dictates the data has become obsolete and is deleted.
How is data integrated into the IT value chain is again particular to each enterprise’s needs. In general it can be defined as a series of activities that an enterprise performs in order to deliver its product or service. Either a product or a service must move through a chain of events before is delivered adding value at each step of the process. The value chain framework is designed for each activity in particular but in general is divided in two main categories:
1- Primary actions for production or delivery of goods or services for a business to be and function in a socio-economic environment
2- Supporting activities like logistics or financial needs which assist in providing efficiency of the primary activities as the they move through the value chain.
Quality control over data is increasingly important for organizations that make data driven decisions. However, several measures are essential for these activities as the expansion and management of data flow become challenging.
Presently, many organizations have an increasing demand for high quality data as the bar rises for analysis techniques and the availability of quality data, is demanded in order to comply with new regulations and legislation. However, this demand for quality data also implies quality sourcing not limited to the data residing in the organization’s IT system
High data quality is also demanded for the improvement of organizational performance, logistics support, growth, competitive advantage and compliance with the growing need of data collection regulations.
Various sectors of the economy are subject to stricter regulations like medical devices, financial services, telecommunication, pharmaceutical, consumer markets and others that collect personal information that are the subject of privacy legislation.
Data complexity and growth is also a challenge for an organization where it is unclear the understanding of data quality and that data management is an IT department responsibility rather than a business side responsibility.
Unfamiliarity with collection methods within the organization such as robotic operated processes, especially if data is transported and transformed as it moves through the chain. Particularly when transformations are complex, it can require an IT specialist to determine which data elements belong to one another.
The inherited complexity of tracking data increases in companies with multipolar IT environments caused by many legacy systems that need improvement or replacement from its existing reporting flow. A known factor is that the more computing is required within a flow, the more complicated it is to capture and interpret its meaning
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