Data collection is done in many organizations and is commonly thought of as keeping records. The record is then normally kept for future references to accomplish a bigger goal, for example, making better business choices which will make your business catapult to new heights. If the data quality is high, it is more viable at driving bigger organizational achievement on account of the dependence on fact-based choices, rather than inclination and human instinct.

There are few factors that determine the data quality, and when each of these factors is appropriately fulfilled, the outcome is great quality data:

Data Completeness

Data completeness actually means if there is no gap between actual data that was supposed to be Collected and the data that was really collected. This is extremely complicated with paper as it is likely to have human error. Paperless collection of data, however, uses tablets and smartphones instead of paper. Data completeness can be effortlessly accomplished with a function known as mandatory fields.

Data Consistency

Data consistency means if the collected data aligns with the anticipated version of the data that was supposed to be collected. To make sure that all the data is constantly collected in the estimated format, drop down menu should be used in a data collection application. This guarantees that all the data is collected regularly and enables accurate asset history, event, and full search results.

Data Accuracy

Data accuracy actually means if the collected data is accurate, and precisely signifies what it actually should. To make sure all the collected data is correct few extra yet quick steps (for example, image capture, time stamp and GPS location) can be added to completely eliminate or minimize inaccuracies.

Data Validity

Data validity can be somewhat more complex than the earlier points, and invalid data often implies that there is an issue with a Procedure instead of an outcome. Data validity is determined by, whether or not the data measure what it was supposed to measure.

Paper-based procedures make problems of invalid data trickier to change as changing forms can be expensive, inefficient and the bigger the company is, the tougher the change. With paperless data collection, there are no physical bits of paper since all information is gathered on a tablet or a smartphone.

Data Timeliness

Data timeliness means the expected time when data was supposed to be received so that the data could be used efficiently. The alignment of expectation and reality sometimes does not happen, causing futile data usage and an absence of information-driven choices.

Anything slower than real-time information is turning into an inexorably insufficient source of data in many enterprises. By using real-time information and analytics, organizations can make more powerful choices. This totally disposes of the time slack, causing educated and timely choices.

The Bottom Line

High quality data is determined by all these above factors. By following the best procedures for guaranteeing high quality data, companies can enhance their operational procedures and organizational visibility through educated, data-driven choices.

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