The significance of data quality has been recognized across diverse sectors such as medical technology, financial services, and consumer products. More sectors open to rising consumer preferences and tighter rules on the usage and accuracy of data. Together with more regulation, more companies are evolving into data-driven entities.
High data quality becomes considerably more effective depending on the scale and nature of an industry. In the food and beverage industry, the supply chain is very dynamic, flexible, and consumer-driven. The food industry is also an intensely competitive arena. Therefore, good data quality is a prerequisite to enable process reliability and improved customer loyalty. In reality, this means the food data faces the following common obstacles for industries concerned with food products.
Resources & Time
Poor data quality contributes explicitly to wasted resources and time when food industries are trying to overcome the problem they are facing. Depending on the amount of data in question, you will need to spend time verifying previous data and resolving anomalies, which might take a long period.
If the data was inaccurate and the brand reverberates with a different targeted customer, you would have misconstrued all the approach and promotion, branding, and communications to the wrong group of customers for the wrong messages.
Loss of trustworthiness
Executive management must have absolute confidence in the data given to them. Poor quality of data weakens that confidence, hampers decision-making, and causes management to lose interest in its data quality.
The trust issue will definitely persist suppose there is poor data quality at any given circumstances, though you’ve tried your best to understand and resolve it: but all to no avail. In the future, they won’t believe it or make choices based on it, even if you got it right.
Undisclosed Financial Expenses
One of the greatest costs of poor data is the financial aspect, and the difficulty of damages associated with data quality is that they are impossible to measure. Data is used in all aspects of food and beverage industries, so working with poor data quality affects many individuals and group. A choice made a year or earlier could have consequences for today’s activities.
Not all data are helpful, and some even affect food and beverage industries. Poor quality of data cause loss of time, money, and above all, income! Good data (such as oil) should go through different purification mechanisms to be considered useful.
Data cleaning can look like a big challenge for the food and beverage industries. However, because of poor data quality, both industries lose money. Establishment operation with high capital-intensive such as the food and beverage industries should not depend totally on data that has not been validated. Doing this would guarantee that they never become optimistic about using resources based on bad data.
Knowledge of high quality is challenging to acquire and maintain. Though, the part of using data for running an organization is too important to neglect. So, consider investing in reliable data processes. We believe it will be worthwhile!
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