Part one “Winning a battle but losing the war” in the absence of data strategy
A good data framework stems from an understanding of business logic
Data cannot go from being accumulated to being used without a good data framework, that is, a top-level design for data. However, if one does not understand the business logic, one can fall into a misconception: mythologising data. And if you want to knock data off the pedestal, you need to attend the following 2 points.
- Don’t use frequent-user data to describe potential-user data
There are good and bad data. In reality, we often mistake using only good data for our data strategy, seeing only good data and making a generalised data strategy. Some e-commerce platforms, for example, often use the data of their frequent buyer to describe the data of all users( including potential buyers) and ignore data to describe the future outlier, which is confusing others and confusing themselves.
2. From efficiency to outcome/consequence
We can think of today’s world as “a world shaped by data and algorithms” because an algorithm can realise almost everything around us. Because of the abrupt development of digital transformation, from management automation to a job setting, a specific formula can be applied by just entering a request then the result can be computed.
However, no one in reality questions this result: why can this management responsibility be automated? Why is there only one staff in a self-service convenience store? Is such an arrangement appropriate?
If you pay attention, you will find that companies that rely purely on algorithms to drive their business, although some will soon get big and capture huge market share, it is difficult to sustain in the long run. The business logic of using good data and algorithms should not exhaust one’s value but move from the pursuit of efficiency to focus on the outcome.
We can undoubtedly manage from a cost perspective, but that is only one dimension. In contrast, If we can think using the big picture of the outcome, efficiency is naturally achievable.
So if we don’t think about the framework of data in terms of business logic but use it for the sake of using it, it often leads to “winning the battle but losing the war”.