by MARK DANCER on Innovating B2B
Becoming a data intermediary
Data is the future of distribution. The incumbent value chain exists to move products from manufacturers to customers, with distributors adding value to help customers find, evaluate, buy, receive, and use the products they need to run their businesses. But a new value chain will emerge as customers, distributors, and manufacturers leverage data, analytics, automation tools, and artificial intelligence to optimize business results.
In the not-too-distant future, the value chain’s primary role will center on data, not products. And the role of distributors, as intermediaries, will be to facilitate the use of data for customer advantage. Distributors will collect and store data from manufacturers and customers, add it to their own, and create unimagined and unprecedented value for customers and every company participating in a connected, data-driven value chain.
But there’s a problem. Data is considered proprietary at every level in the existing value chain, from manufacturer to distributor to customer. Every leader I know believes that data defines the future of distribution, but no one wants to be the first to make it so. All can imagine a slow and steady evolution toward the future, but can’t say how it will happen. The threat of disruptive change enabled by outsiders is real because of the power of data to create exponential economic value. But still, there is a brick wall blocking progress.
Looking for a solution, I found a big idea—a tipping point for collaborating around shared data might not happen withinan industry’s value chain but instead by sharing data across industries. Cross-industry data collaboration is already happening, as explained by Eve Besant in an article hosted on Capgemini’s Data-powered Innovation Review | Wave 3.
Besant begins by explaining:
Innovation doesn’t happen in a vacuum, and the development of new products, services, and solutions involves input and information from a multitude of sources. Increasingly, many of these sources are beyond an organization’s borders and the organization’s industry.
Besant then argues that “organizations that have developed cross-industry data collaboration capabilities can more easily foster innovation, leading to a competitive advantage.” She offers several examples, shared here with emphasis added to highlight the leading industries:
In financial services, institutions that must prevent and detect fraud use cross-industry data sharing to better understand the profile of fraudsters and fraudulent transaction patterns.
In manufacturing, companies are using AI to manage supply-chain disruptions. Using data from external sources on weather, strikes, civil unrest, and other factors, they can acquire a full view of supply-chain issues to mitigate risks early.
In energy, smart meters in individual homes open new doors for data collaboration, transmitting information about energy consumption.
In education, school systems, local governments, businesses, and community organizations work together to improve educational outcomes for students.
In healthcare, during the COVID-19 pandemic, hospitals relied on information from health agencies and drug companies regarding the progression and transmission behavior of diseases. Governments followed data from scientists and healthcare professionals to create guidance for the public. Retailers heeded guidance from the public and healthcare sectors to create new in-store policies and shift much of their business online.
Not one of the cross-industry data collaborations above is led by distribution. That’s a problem, or maybe an opportunity. Distributors can follow precedents set by others, reinvent their role as a data intermediary, create new value for customers, and over time, entice other players in the incumbent value chain to join in. By doing so, distributors would spark a revolution, beating outside disruptors at their game and owning the future of distribution.
Foresight and footsteps
Besant offers another missing piece for ushering in cross-industry data collaboration. It turns out there are multiple models for sharing data. She provides seven, each with an actionable definition: a single, governed source for all data, simplified data sharing, secure data sharing, inexpensive data management, democratized data, and advanced analytics.
