Data integrity is a primary condition for reliance on automation systems for management decisions. In the hands of a good manager, a trustworthy system is a powerful management tool, yet systems allowing low quality data may cause more harm than good. Data quality results from three main factors:
Accuracy of measurement – the measuring device should accurately reflect the milk volume, composition, time or other measurements.
The rate of measurement – the system should capture as many of the animals visiting the parlor as possible. This depends on the quality of animal identification, mainly through identification rate.
Data assignment to animals – Data assignment is probably the most important of the three factors, as mistakes in this criterion can lead to wrong immediate actions as well as erroneous long-term decisions.
The IDeal system
Afimilk has held the reputation for presenting highly reliable data, since the company’s early days, when it developed the dairy industry’s first automation system. Having proven the integrity of its tools for managing dairy farms, Afimilk went on to create a highly dependable ID system, the IDeal.
The IDeal combines various elements for ensuring a high identification rate and an ultimate data assignment concept. The importance of identifying animals at the milking point is far greater for data integrity than just maximizing cow identification: it actually ensures data assignment to cows.
Stall vs. Entrance ID
Since milkers are too busy to correct wrong identification, In each of these two loads, half the cows (ten) are assigned the wrong data. This means 20 of 100 cows are assigned with wrong data, or in other words, the reliability of data is a poor 80%. With the IDeal stall ID, where data assignment is close to 100%, the same ID rate result is 98% data reliability.