Outcomes are purely data-driven when data is the only signal required to make a decision.
In contrast, in data-informed decisions, data is a strong input but not the only input.
Generally speaking, Product Analysts are data-informed and algorithm developers are data-driven.
EVOLUTION OF DATA SCIENCEImagine a world in which a machine knows everything about you and can shop for you without even explicitly asking for it; knows the food you like and cooks for you; knows your choices and can make decisions for you and knows what is good for you and plans your life.
This world is distant into the future and requires Artificial Intelligence to take over much of our lives.
For us to make progress toward this dream, we need to become even more data-driven.
In a perfect world with perfect information and a complete understanding of all the drivers of your system and how they interact with each other, the two approaches would converge.
In order to build a perfect model, the phenomenon under study needs to be completely understood; the relationship between the data and the phenomenon can be described by a perfect model (and associated rich feature set).
In order to evolve to this level of perfection and also make progress in the interim, the world will need to continue to make progress on data-informed decision-making.
i.
e.
, we need to continue augmenting our decision making by other subjective measures that cannot easily be fully quantified yet.
As we begin to have a deeper understanding of relationships between objects, more and more processes will get automated away and the future will be more data-driven than data-informed.
However, data-informed decision making will continue to be extremely important for the next few decades and data-driven decision making will only improve with advancements from people who are data-informed.
It is most illustrative to understand the differences between data-informed decision making and data-driven decision making by means of examples.
Setting goals.
Good goals are measurable and quantifiable.
Being able to identify and track goals will become increasingly data-driven.
For example, Facebook’s tracking of its active users maybe completely automated.
However, setting the right quarterly and annual goals for active users and revenue maybe only partly automated and will continue to be data-informed.
Defining a roadmap and strategy.
Establishing a roadmap and strategy is not quantitative and hence requires data-informed approaches.
For example, by using data, a roadmap can be developed for increasing daily active usage by focusing on SMS notification.
A good roadmap considers the relevant goals, the drivers of these goals, the levers that the product team has, and all of the courses of action that can be taken.
Much of this is qualitative, so the process of building a roadmap and defining strategy is primarily data-informed.
Forecasting outcomes.
Forecasting outcomes is mostly data-driven.
For example, figuring out whether or not to show a story to a user would require understanding multiple factors, including the probability of a user clicking or reading that story.
Companies typically develop models, which are iterated on continuously, to forecast this specific outcome.
Powering production systems.
For companies like PayPal to identify fraudulent activity of any transaction, it is prohibitively expensive to do this manually for all transactions.
As a result, they largely rely on machine learning to power their production systems and automate the calculation of the probability of a transaction being bad.
Much of the decision-making that follows the evaluation of the probability is also automated.
However, in areas where there are lower levels of confidence in the probability evaluations, the decision process could be data-informed.
TAKEAWAYSImproving products and monetizing through data has become a competitive advantage in recent years.
A strong and well-organized data organization is a strong differentiator.
Data scientists are driving key product decisions across companies and building next-generation algorithms to improve decision making.
The world will continue to become increasingly data-driven, but data-informed decision making will remain relevant.
This work is a product of Sequoia Capital’s Data Science team.
Chandra Narayanan, Hem Wadhar and Ahry Jeon wrote this post.
See the full data science series here.
Please email data-science@sequoiacap.
com with questions, comments and other feedback.
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