Computing - 9.4.1e issues related to transaction processing systems

9.4.1e issues related to TPSs

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9.4.1e issues related to transaction processing systems

changing nature of work and the effect on participants, including:

the automation of jobs once performed by clerks

shifting of workload from clerks to members of the public

the need for alternative procedures to deal with transactions when the TPS is not available

bias in data collection:

The result of bias during collection is inaccurate data leading to inaccurate outputs from the system.

Bias in data collection is an inclination or preference that inclines most aspects of the collection process; the result of bias during collection will lead to inaccurate data leading to inaccurate outputs from the system. Those involved in collecting data must aim to minimise the amount of biasness present.

For Example, a loan collection assumes that an individual’s income-based how constant their return repayments are, whereas business owners can easily adjust their income and therefore pay more.

Being less biased can result in better results and more accurate outputs from their collection of data.

when establishing the system and deciding what data to collect

when collecting data

The importance of data in transaction processing, including:

Data Security

Data can be secured through multiple strategies:

Passwords - Confirms the user is who they say they are to gain access rights
Backup copies - Different backup methods can be used to create copies
Physical barriers - Locking away machines with important data and information
Anti-virus software - FIles are scanned to look for viruses,
Firewalls - Protects from outside penetration by hackers. Monitors transfer of information to and from the network. The Barrier between LAN & internet.
Data encryption - Unreadable by those who don’t possess decryption code

Data Integrity

A measure of how correct and accurate data reflects its source. The quality of the data. The integrity of data is critical in all transaction processing systems. There are three techniques for improving data integrity which includes:

Data validation
Data verification
Referential integrity

Data Quality

Data quality is concerned about how reliable and effective the data is to its organisation and measures the conditions of data based on accuracy, reliability and consistency.

For example, responses from surveys may be imputed accurately into a system, however, the quality of that data will be poor if responders didn’t answer honestly - making the data unreliable and ineffective.