In my last post I discussed the dramatic differences in business models between social networks and business networks. Social networks derive revenue primarily from monetizing the content generated by their users with third parties. Business networks, however, charge end-users directly for the services they consume.
The CEOs of many business networks have talked about creating analytics and reporting services for the past 10 years, but there are few examples of success. Is the data on business networks simply not of interest (or worth analyzing)?
Let’s start by understanding the types of data that passes through business networks. The most popular transactions include
- Purchase orders
- Shipment notifications
- Commercial invoices
- Delivery confirmations
- Payment instructions
- Bank statements
- Inventory positions
- Product catalogs
- Point-of-Sale
Many people review this list and think of it as mundane data. What could we learn from reviewing a company’s delivery confirmations or inventory reports? How boring? It is far more fun and interesting to focus on performing sentiment analysis of Facebook posts to guess who will win the next US Presidential election. And far more rewarding to make stock picks based upon which new high tech gadgets are getting the most buzz on Twitter.
But I would argue that people thought about maps as boring 15 years ago. What could be more boring than street maps? Fifteen years ago most people owned a set of maps for their car. And the roads didn’t change much so there was relatively little repeat purchasing. So you might ask – why would anyone focus their time investing in reinventing the map? But Google, Apple and Yahoo clearly did not agree. All three have been battling to become the de facto source of mapping and driving instructions data for over a decade – first on the browser and now mobile apps. And now these vendors are best positioned to exploit the multi-billion dollar opportunity for location-based services and mobile advertising.
Out-of-print books offer another example. Google has spent the last 10 years cataloguing and digitizing 30 million different books – many of which are out of print. They have fought legal battles over copyright violations and suffered negative publicity in the press. Who cares about out-of-print books? If the books were worth reading they would still be on the printing presses, right?
But Google had a broader vision. Google wanted to democratize knowledge to everyone by continuing to improve its search engine. And it also saw an opportunity to build a real-time language translator. By comparing copies of the same book published in multiple languages Google could build an algorithm that could automatically translate text from French to English, German to Dutch, Italian to Portuguese. Think “Translate This Page.” Ten years later, who is best positioned to build a mobile app that can listen to human speech and translate it in real-time? Google is one of the top contenders.
How do maps and books relate to purchase orders and invoices? They all appear on the surface to be uninteresting for analysis purposes, but are sources of unlocked potential. When it comes to data beauty is in the eye of the beholder. Many types of information that would seem fundamentally uninteresting or irrelevant to the general public have immeasurable value in the eyes of a visionary entrepreneur or ambitious data scientist.
By analyzing the data on business networks companies could determine – What is their perfect order fill rate? What shipments are at risk of being delayed? How does their on-time delivery performance compare to your competitors?
