The Great Data Companies of Future. The Culture
Every company is a data company but not many think of data as a culture. The data collection in modern companies (and modern world) is not institutionalised enough. In the episode “How to Love Criticism” of the “WorkLife” podcast one of the heroes to a question why he likes transparent criticism, answers:
“It’s just data. It’s just data, objective data about what I’m like. I would rather know how bad the bad is and how good the good is so I can do something with it”.
This is a great demonstration of a culture that systematically collects data — in different ways. Criticism is one type of data and there are many others generated but not collected or treated as data. This short story is about a culture that justifies and enables relentless data collection in companies.
Traditional companies view data as a “mechanism” to store and retrieve the product state. Hence data storages have undergone an evolution (they still do) to accommodate these needs. Therefore we know about relational, key-value and other types of Databases. The traditional companies see data as a way to store and retrieve the product state.
Modern companies, in contrast, are motivated to store disproportionately larger sized data from many sources. This has motivated to build all kinds of other databases such as NoSQL, distributed data stores and file systems. However the perception of data is still traditional but with much wider definition of what products are. Therefore modern companies maintain the same culture of data collection — store the product state. The motivation to collect more data comes from the need to build new-world features — smart, analytical etc.
The future looking companies look at data as a tool and a way of culture. Data is collected to support the product but also it’s part of the culture, the productivity. It helps with cost efficient ops and serves as a general tool (similar to Slack, Zoom etc which also collect data on their own BTW). Google has a great case of reducing its data centre cooling electricity usage by 40% by using Neural Networks trained over temperature and other sensor data they had collected over time. Bridgewater uses open (hierarchy-less) criticism (radical transparency) as a core component in their culture and that’s a source of data. Employees collect objective data about themselves in their brains and other devices via variety of channels, analyse and improve further in what they do. They also record their meetings to run it back and learn from. What other ways we can collect data? What would be the right storages for that data? How to use that data for the best? These are the key questions that motivate the existence of this story and its sequels.
Data Sources 1.0: User personal form-filled data, other product core data stored in tables
Data Sources 2.0: Data from IoT devices and sensors, user actions/impressions data, server logs data, Twitter streams, image data sets, criticism, recorded meetings.
All companies utilise data from Data Sources 1.0 to store their product state.
Modern companies also utilise Data Sources 2.0 to build significantly better products and user experiences.
Future looking companies utilise Data Sources 2.0 as well BUT with a different culture at place. Their mindset is focused to build not only great products but also great environments for the overall process — the big picture. Data is a key tool of the future.
Every company is a data company but at the best companies data is a culture. The future looking companies think of data as a necessary component to build great products, great product/user relationships, great teams and productive work environments.
Thanks to Adam Bittlingmayer, Lusine Margaryan and Hambardzum Kaghketsyan for reading drafts of this.
