Mark Zuckerberg’s congressional hearing last month provided the perfect opportunity to mock the technological gap between millennials and older generations. Jokes were abound comparing the Facebook CEO’s explanations to those many of us had with our parents about what the Internet is and how to program the VCR.
Despite the initial outrage about the Facebook user privacy crisis and the questions that remain, Facebook’s stock has rebounded. Investors seem confident about Facebook’s ability to leverage its network to generate revenue. Within two weeks of the data harvesting scandal, the company lost nearly 18% (~$95 Billion) in market capitalization. However, by April 25, following a positive earnings announcement, the stock price had rebounded to within 5% of its price on the March 16 disclosure date.
Even as news cycles kept the story in the news, Facebook’s value to advertisers remained obvious by providing the clearest understanding of individualized behaviors and preferences available on the market. That is not important just for advertisers. More than 70% of the supply chain community said in a recent survey that a clear understanding of what customers value and will pay for is both highly valuable and hard to get.
In demand driven supply chains, the presumption is that once this highly valuable data has been acquired, supply and demand signals can be synchronized resulting in a network that is optimized with the right supply quantity, in the right form, at the right location, allowing operations to be both agile and efficient. For example, a retail chain can use search analytics to predict locations and timing for seasonal illnesses (cold, flu, allergies), then send additional inventory to the closest points of distribution and adjust regional forecast splits.
This type of example is at an aggregate level, whereas the debate about Facebook data mining is much more personal. It is not a new debate, either. In 2012, as part of the then titled Chief Supply Chain Officer Study, we asked the supply chain community how they felt about different aspects of data mining on both personal and aggregated levels. The community was neutral about using geospatial data, and more uniformly leaned toward both mining transaction and webstore data.
Interestingly enough, on both aggregate and personal levels, the community felt that mining search and Facebook data was unfavorable. It was a slight margin then and, as evidenced by the publically available examples, it was not enough to dissuade companies from finding ways to use the data to their benefit.
The mainstream debate about data acquisition is wide ranging, but for the cost-sensitive supply chain community, it may be necessary to rephrase it as two diverging questions:
- What is the cost to acquire the data that we find valuable but hard to get?
To get closer to the customer requires significant changes in culture, process, and technology, or an external source that puts the data in a tidy package. Either way poses a significant investment, and privacy adds additional complexity that must be accounted for.
- What is the cost of not knowing what customers value and will pay for?
One Chief Supply Chain Officer told a group at a forum that in order for their supply chain to be most effective they have to know what’s on the shelf…not the store shelf, but the shelves inside the consumer’s home. Without that level of detail, the supply chain was spending countless hours, and dollars, chasing solutions to manage the variability of customer behaviour. Based on the reactions from others at the table, the situation is common to many, and what’s not being spent to acquire data is showing up in OpEx budget overages
Ideally, there’s a way to blend both questions into one cohesive data strategy, but as the decisions become increasingly granular, it will be difficult to keep the big picture in mind. It is at those moments where the value of data from sources like Facebook and Google, and its associated costs, will be evaluated. Is it part of the problem or part of the solution?