Sophisicated datagraphs are the separator between the top tech companies and their competitors, but the tech isn’t solely for apps with billions of users.
A common framework found in the top echelon of successful technology companies is the collection of relevant, real-time data on users, fed into highly sophisticated algorithmic datagraphs, which provides useful insights and continuously optimizes the product.
At the very top of the ladder, these graphs are famous for their sophistication. Google’s Knowledge Graph pulls data from 4.3 billion active users, from almost every country in the world, and is responsible for making the search experience deeply personalized and accurate.
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As Google does for search, Amazon does for commerce; and Facebook does for social. The collection of customer engagement data, combined with continuous improvement of the underlying algorithms, has allowed them to surpass all competition in customer experience and product insights.
Even though it may seem like the accumulation of massive amounts of data has allowed these platforms to build the datagraph, it is actually the opposite way around. Through an early focus on extracting insights and optimizations from usage, these platforms were able to beat the competition even when there wasn’t a gap between them and the rest of the pack.
“Datagraphs are not static; they do not reflect information at a snapshot in time,” said Harvard Business Review author, Vijay Govindarajan. “They are dynamic, reflecting what data scientists refer to as data in motion. That’s partly why it is impossible to manually draw a datagraph. Technology is needed to gather and interpret in real time the data on the millions of units of a company’s products that consumers worldwide may be engaging with at any given moment.”
So, even though it may be difficult to reach the same complexity as Google’s Knowledge graph or Netflix’s recommendation graph, in each business sector there are key bits of data that organizations should aim to acquire, if they want to gain a competitive advantage.
Alongside the acquisition of key behavioral user data, organizations that aim to implement a datagraph strategy need to be focused on moving data into AI flows, instead of having it buried in data silos. One of the reasons for the success of Google and Amazon is taking almost every interaction with their platform and using it to inform and personalize the next experience for the user.
In that way, the user feels more compelled to come back each time, as they are fed recommendations that become more accurate. TikTok is perhaps the newest platform to truly understand the benefits of building a sophisticated recommendation engine, which starts from day one narrowing the field of content viewed, until it has a well rounded understanding of what the user likes and dislikes.
For an organization to attain the levels of competitive advantage made available through datagraphs, Govindarajan offers the following steps to catch up:
- Develop a datagraph strategy: Pair industry experts with data scientists to properly develop the datagraph, taking into consideration future trajectory and business implications.
- Develop proprietary algorithms: To rapidly analyze the data, an algorithm needs to be developed which can tell the organization: What happened?, What could happen?, What should happen?, and Why did it happen?
- Engender trust: Some algorithms are loved (Netflix recommendations), some are hated (Facebook social), and a lot of this comes down to communication between the platform and the user. If a user doesn’t understand what value the algorithm is providing, for instance that Facebook is providing a cleaner and more compelling user experience, they will naturally be wary of it, which may lead to less usage.
- Monetize the datagraph: Once constructed, the datagraph is not static. It can be used in any number of ways, to launch new products, to better commercialize certain aspects of the business, or to protect against competitors.
