Advancements in machine learning (ML) and artificial intelligence (AI) have placed a premium on data. As McKinsey reports, “as much as 70 percent of the effort involved in developing AI-based solutions can be attributed to wrangling and harmonizing data. Unless data is thoughtfully sorted and organized for easy consumption and reuse, scaling solutions can be a big challenge.”
In this context, it’s no wonder that Consumer Packaged Goods (CPG) and Food & Beverage (F&B) companies are struggling to clean and harmonize unstructured and siloed master data to take advantage of new AI capabilities. Traditional systems of record have fallen short, and can only provide insights based upon a historical data set. And while the past can help to inform the future, these legacy systems relegate companies to “driving through the rearview mirror” based upon yesterday’s data.
Platform-in-Use: A New Paradigm for CPG and F&B Enterprises
This is where the concept of "Platform-in-Use'' comes into play. Inspired by digitally native companies like Tesla and Netflix, which leverage information from consumers using their service to advance and tune their products for all consumers, the notion of Platform-in-Use can help consumer product companies amass consistent, structured data sets to move from a reactive state to a predictive state.
In his book, Fusion Strategy - How Real-Time Data and AI will Power the Industrial Future Dr. Venkat Venkatraman describes this challenge facing manufacturers as follows:
“Going forward, industrials will need to combine what they do best (create physical products) with what digitals do best (use AI to parse enormous, interconnected, product-in-use data sets) to make strategic connections that will otherwise be impossible… by systematically accumulating such data, companies can develop datagraphs, which capture the relationships, links, and interrelationships…datagraphs are derived from data network effects, which are triggered when a company’s product gets smarter as it gathers more data from users of the product.”
Consider a CRM platform, which provides the customer data master. And while your customer information is essential (contacts, emails, addresses, ect.), I would argue that the true insight lies in harvesting opportunity data, data about how a sales deal moves through various sales stages. This is an example of Platform In-Use. Most customers establish a multi-stage sales process, complete with stage gates. As opportunities advance, patterns emerge. You can see which sale stage is slowing down a process and investigate, as a first reactive step. But over time, as you move thousands and even millions of opportunities through a structured sales process, you can begin to leverage AI to begin to surface even greater insights and move to a more predictive sales state. This insight is essential for forecast accuracy and accurately guiding revenue outlook for investors and to the market.
The emergence of AI across the CPG and F&B industries presents an opportunity to redefine core value drivers. Platforms that span a multitude of business processes can create a corpus of structured “data graphs” that power real-time, actionable insights for companies.
This fusion of data and ML/AI is reshaping the enterprise technology landscape and reorienting how durable value is created. Real-time data, combined with AI-powered insights, establishes a live decision framework that shifts the orientation from reactive to predictive and ultimately to a prescriptive stance. Think of this as live streaming your manufacturing process.
The Power of Structured Data
“It's not just about having data - the intrinsic value of data is zero,” Alberto Prado, Vice President, Head of Digital R&D at Unilever reflected at the 2024 Veeva Consumer Products EU Summit. “It is what you do with it that creates value. Is this data available? Is it accessible? Is it the right quality to do the job? Is it timely?”
Structured data is the cornerstone of effective ML and AI implementation. Unlike unstructured data, which is often disparate and difficult to analyze, structured data is organized, easily accessible and searchable, making it ideal for training AI models.
For example, Tesla’s fleet learning model and Netflix’s recommendation engine are predicated on the continuous accumulation and analysis of structured data from consumers using their products. Similarly, CPG and F&B companies can unlock operational insights from structured data records, transforming their operations by leveraging data that is created during and throughout the manufacturing process. These “data graphs” create insight and allow manufacturing operations teams to begin to take a more prescriptive approach, anticipating and interceding when deviations are likely to occur.
Amassing consistent structured data sets over time enables the shift from reactive to prescriptive approaches.
Platform-in-Use: Unlocking Value and New Business Capabilities
The concept of Platform-in-Use can address these challenges by emphasizing the value of structured data graphs and AI. Here’s how:
- Real-Time Insights: Platforms, like Veeva’s Vault platform, can create a corpus of structured data graphs that power real-time, actionable insights for companies. This fusion of data and ML/AI is fundamentally transforming how value is created.
- Predictive and Prescriptive Analytics: By leveraging structured data, companies can shift from a reactive state to a predictive and even prescriptive state. For instance, non-conformance records (NCRs) can be analyzed over time to predict and prevent quality defects, ensuring higher efficiency, reduced errors and decreasing waste.
- Elevating Platform Value: The more expansive the use cases that a platform can address, the greater the benefits. This magnifies the value of the platform, providing a compounding value to customers with the additional increased operating leverage for information technology organizations.
By embracing the concept of Platform-in-Use, companies can ensure that they are not only keeping pace with technological advancements but also positioning themselves to lead in the era of AI and ML.
Want to dig deeper into this topic? I encourage you to read the recap of Alberto Prado and my recent Summit keynote, which also featured Matthieu Cassier, Chief Digital & Transformation Officer, Research & Innovation at L’Oreal, and Stefan Vos, Industry X Lead for Consumer Goods Europe at Accenture or watch the session on-demand.
