Intrinsic value of food chain data

Abstract

Food Science and TechnologyVolume 35, Issue 3 p. 44-47 FeaturesFree Access Intrinsic value of food chain data First published: 16 September 2021 https://doi.org/10.1002/fsat.3503_11.xAboutSectionsPDF ToolsExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Raymond Obayi of the University of Manchester explains how to assess the intrinsic value of food chain data to inform the adoption of smart technologies. Data [not technology] is the new value frontier The agri-food industry is experiencing a digital revolution with recent advances in precision agriculture and smart manufacturing. It is estimated that digital technologies could potentially unlock an additional $500bn in agri-food GDP contribution by 20301. To benefit from the digital revolution, food businesses need to assess the potential value of new data assets produced through technology investments in (1) digitising analog processes, (2) digitalising existing data assets for predictive analytics, and (3) transforming existing business models with advanced connectivity and network technologies2. Corporate assets are assessed using economic or market indicators of value. In contrast, data and information are pretty challenging to appraise because the intrinsic value of data is embedded in context and derived from its use3. This article presents a straightforward two-step approach to appraising the inherent value of food chain data to underpin investment decisions in digital technologies. Step 1: Appraising the value of food chain data to support technology investments Determine the intrinsic value of food chain data Data is perhaps the most valuable asset in the digital economy. Today's most successful companies like Amazon, Google and Facebook have invested in developing advanced capabilities for harnessing and utilising data to inform virtually all business processes and decisions. However, data is quite challenging to appraise because it is considered an intangible asset and is generally viewed as a public good with no stipulated market value. Data is also a non-rival asset, which means that the value does not diminish based on the number of users4. Consequently, it is quite challenging to ascribe a fixed value to data, as different users would value the same data asset differently depending on the intended application. Despite these challenges, actors have to reflect on the potential benefits of data to justify investing in advanced technologies5. The assumption that technologies are an end and not a means is flawed because the value of any technology is linked to the data and information generation and processing capability it affords6. There are several approaches prescribed in the literature for assessing the value of data. Cost-based and income-based approaches ascribe value to data based on the cost and future cash flows of data production, respectively. Market-based approaches value data by estimating the market price that users would be willing to pay for data access. Benefit monetisation and impact-based methods evaluate data value by assessing the benefits of particular data assets or the impact of data availability on economic, social or regulatory outcomes. The benefit monetisation and impact-based approaches are helpful because they assume that data would have varying implications for different actors7. Thus, stakeholders must collaboratively assess the value ascribed to data with all key owners, processors and users8. Data plays a crucial role in the food processing industry because it underpins performance indicators like safety, quality and provenance and supports regulatory and compliance requirements. While several new technologies are promising, food businesses must assess whether or not investing in a given technology will generate sufficient data assets to justify the investment to stakeholders9. In determining the potential value of new data assets generated from digital technologies, less emphasis should be placed on how much data is available or required. Instead, actors should collaboratively identify data assets with the potential to unlock the most value for stakeholders10. Understanding the value of data assets before exploring the required technologies is helpful to avoid the pitfall of chasing technology solutions in search of problems. It is, therefore, not so much about how advanced a given technology is. Value is really about the advantages or benefits of data and information quality that a given technology affords. Discussions around the importance of data rather than the value of technologies can help food chain actors to arrive at a consensus regarding data needs, priorities and perceived value before selecting the right technologies to deliver such value. Table 1 provides a series of questions to guide focus group discussion among food chain actors on the nature of benefits that they require from data assets. Actors can reflect on improving specific performance outcomes and understand how different data requirements form a data value chain around particular performance measures11. Table 1. Questions to guide food chain actors regarding the benefits of data assets Potential benefits Questions on data requirements Food safety12 What data is required to monitor, analyse and report on critical food production and processing controls to minimise risks associated with safety levels? What data assets do we need to inform record-keeping on safety parameters and procedures in our food chain? What data categories would enable us to run comparative hazard exposure assessments in line with surveillance database requirements, e.g. EU Rapid Alert System for Food and Feed (RASFF), the US Import Refusal Report (IRR), Inspection Classification Database (ICD) and China State Administration for Market Regulation (SAMR)? Food provenance 13 Can categories of available data be used to improve compliance monitoring in line with HACCP food safety management requirements? What categories of data are required in real-time to produce compliance records that are transferrable in machine-readable formats? Is it possible to store and share compliance reports on provenance records from critical control points and what data categories can be stored? What provenance data do customers and business operators require to run a robust food safety monitoring system? Food fraud prevention14 What are the types of fraud (adulteration, tampering, product overrun, theft, diversion, simulation and counterfeiting fraud) and what product, market and consumer data can be used to inform potential fraud identification? What are the critical data assets required to inform authenticity assessment of key food chain partners and how is such data captured and stored? What data is required to inform the Food Fraud Vulnerability Assessment of fraud susceptible operations across our food chain? Food Quality (nutrition, sensory, convenience, functional, ethical and aesthetical)15 What data categories can inform us of our key stakeholders’ priorities across the multiple dimensions of food quality? What data categories would allow us to demonstrate and communicate valuable quality parameters in the food chain to key stakeholders? What data could we use to link the subjective perceptual measures of food quality to the objective specification of quality outputs at critical nodes in our food chain? Evaluate actors’ digital readiness to inform technology deployment Once the benefits of data assets are identified and prioritised, the next step is to assess the digital readiness of actors to benefit from data generated using advanced technologies16. Different users embrace technologies at different paces due to varying levels of preparedness and different levels of data usage in production and processing operations. These differences can sometimes lead to uneven adoption and utility, which could diminish the overall benefits of digital technologies and the data assets generated17. Digital readiness is the ease of transitioning from manual to digitised workflows and adopting new technologies and data assets into existing models to derive value in the form of returns on investment18. It is an aggregate measure of a firm's digital potential and proficiency in terms of the level of behavioural and technical competencies to facilitate meaningful technology adoption19. For many upstream actors in food chains, the priority in terms of digital potential is to digitise existing analog data using smart sensors (RFID, barcodes and QR codes) to improve data capture and transmission quality. They tend to focus on digitising analog data to enhance accuracy, conciseness, integrity, availability, detail, timeliness, urgency, relevance, applicability to business processes, plausibility, clarity, objectivity and uniqueness20. Other actors invest in digitalising existing data assets by deploying advanced analytics and data processing systems for predictive analytics21. Value is also derived from transforming business models using connectivity, data storage and network technologies like distributed ledgers, smart contracts and cloud computing. Digital transformation is an enterprise-wide endeavour22. Actors could collaboratively assess their digital readiness for appropriating data generated from advanced technologies (not the readiness for adopting the technologies themselves) by: 1 Assessing digital readiness of all parties against dimensions like digital strategy, operational process maturity, employee digital capabilities, data security, interoperability. 2 Assessing the quality of data in each node of the chain. 3 Identifying gaps in the chain of data required to improve a given performance measure. 4 Highlighting the regulatory, security and propriety issues that may hinder data strategy. 5 Developing use cases for possible applications of data generated from digitisation, digitalisation or digital transformation investments. As shown in Figure 1, agri-food businesses can capture value from digitisation, digitalisation and digital transformation data. As such, technology adoption decisions should be based on appraising the potential value of this data rather than trends or advances in digital technologies. Figure 1Open in figure viewerPowerPoint Value streams from different levels of digital readiness Step 2: Appraising the potential value of data assets from advanced technologies Digitisation, digitalisation and digital transformation technologies provide food chain actors with different forms of value from data as a utility. While digitisation value takes the form of new digital data assets, digitalisation and transformation provide process improvement value and digital innovation value. Technology investments are expensive, so it is helpful to begin by ascribing intrinsic value to data assets from capture, analytics and digital transformation technologies23. Laney's article entitled ‘how to monetize, manage and measure information as an asset’24 proposed some data valuation approaches for adopting technology. To justify investing in digitisation, actors can evaluate the Intrinsic Value of Information (IVI) (Figure 2) generated from deploying such technologies in terms of the impact of its completeness, accuracy, availability, utility and access, and scarcity on specific aspects of performance (safety, quality, fraud prevention). Figure 2Open in figure viewerPowerPoint :Intrinsic Value of Information (IVI) Formula After determining the intrinsic value of data from different stakeholders’ perspectives, the next step is to assess and rank existing processes to identify areas for performance improvements. The Business Value of Information (BVI) (Figure 3) approach can ascribe value to data assets for predictive analytics capabilities and justify investments in digitalisation technologies. Figure 3Open in figure viewerPowerPoint Business Value of Information (BVI) Formula Transforming existing business models requires a comprehensive assessment of the potential impact of extensive technology deployment on key performance indicators (KPI) of changed food chain processes (e.g. inventory management, safety reporting, production optimisation, etc.). The Performance Value of Information (PVI) (Figure 4) assesses whether digital transformation would yield valuable outcomes for stakeholders (e.g. scenario analysis using digital twins of existing processes)25. The IVI and BVI provide leading indicators of the potential value of data from digitisation and digitalisation technology deployment, while the PVI is a lagging indicator26. Figure 4Open in figure viewerPowerPoint Performance Value of Information (PVI) Formula The KPI ratio of existing performance and modelled performance post-digital transformation indicates the potential value accruable (increase in a given KPI) after a digital transformation is introduced. The time ratio in the formula indicates the possible increase in value attributable to performance improvements due to digital transformation investments. Conclusions This article highlights the steps in evaluating data assets derived from technology investments and the digital readiness of actors to use new data assets to achieve expected ends or utilities. With several technological disruptions on the horizon, food businesses require a data value mindset, which views advanced technologies as tools for harnessing the value in data. To benefit from the current digital revolution, food businesses should focus on the importance of data rather than the splendour of new technologies. 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