The food-and-beverage production sector relies on data analytics to keep costs and wastage down: their data strategy centers on quality data.

Today, how effectively food production businesses harness their data can make the difference between getting their produce sold or go to waste.

Take the case of perishable produce such as kiwifruit. With many varieties only able to stay ripe for five to seven days, precise and timely data across the value chain is crucial if fresh kiwifruit is to be offered to consumers. Any missteps in data about the fruit can adversely impact quality, distribution, prices, and even the environment.

For example, if the data used for yield prediction is flawed or outdated, it may result in unrealistic estimations of crop yields, creating a ripple effect of consequences. A surplus produce from over-estimation can impact an organization’s sustainability efforts and lead to waste, whilst under-estimation can lead to supply shortage and price impact on consumers.

In another instance, gaps in data pertaining to quality control and assurance of produce may result in contaminated or unsafe products reaching our grocery stores — risking the health of consumers. 

It is therefore imperative for food producers to have a proactive approach to ensuring data quality. By putting data first, and investing in reliable data management, validation, and governance processes, they can ensure that data is right from the start, to make the right decisions in a timely manner. 

Gary Chua, Managing Director, Asia Pacific & Japan, Syniti

Quality data in, quality results harvested 

Embracing a data-first approach and the developing the ability to proactively capture, sanitize and use it, not only helps to avert severe consequences, but drives efficiencies and competitiveness.

Organizations such as Zespri, are already seeing the fruits of a proactive approach to data management. The firm replaced manual tracking and inputting of data into spreadsheets, with an automated data platform with strong governance processes. Beyond driving efficiencies in data management, this ultimately helped to instill trust in their data among employees, who previously had to re-run and validate their data repetitively to make decisions with greater certainty.

With greater visibility into their data quality, their employees are now strongly positioned to make better-informed decisions to drive business improvements.

Learning points

Digital transformation to make optimal use of data is important, but ensuring data quality is used is equally important. So:

    • Start the data work early and focus on the importance of high-quality data so that it will translate directly into the bottom-line.
    • With many organizations in the food and beverage industry turning to big data analytics, the critical question remains whether the data itself is accurate and relevant to provide reliable insights for effective decision-making.
    • Choose the right data management platform that can ensure that only quality data is fed to the system, before it delivers improved outcomes.

In another example, a global leading food and drink manufacturer implemented a data management platform across all business processes. With in-built, active governance and validation processes in place, the firm saw its “first-time-right data” (data that is correct without the need for human remediation), jump from 24.3% to 97.7%. Previously, new product development and launches, which used to take three months, now takes just weeks, owing to enhanced governance and automated validation of data.

The organization is also able to avoid incorrect deliveries and missed invoices, providing both reputational and cash flow benefits.