Data Democratization Manifesto
Data democratization is as a matter of fact on its way, but after talking to quite a few practitioners or practitioners-to-be in different forums, it turned out that many questions remain open and although some common basis is there, the scope and magnitude is still unclear.
Especially with the advent of Internet of Things (IoT), where machine generated data is constantly increasing its share, democratization policies shall allow for maximization of the value generated out of the data. While the need for data democratization is universally accepted and seen as a must have and as a real business drivers, a written and published declaration of motives, views and intentions (a.k.a. a manifesto) shall provide common understanding. That’s what made us emulate other similar movements, such as Agile Software Development, Agile Data Science or User Data Manifesto 2.0, and come up with a Data Democratization Manifesto.
That is, while there is value in the items on the right and might be justified back in the time of a central Business Intelligence setup, we value the items on the left more.
Principles behind this data democratization manifesto
We follow these principles:
- The highest priority is to maximize the value of the data to drive business results.
- Business people and data scientists must mesh, working together on a daily basis all through projects and ensuring that insights are transformed into actions.
- While the duty of the business side is providing the maximum context and expert knowledge for data scientists to guide their models; the duty of data side is incorporating the maximum relevant knowledge from business experts into the models.
- Business and Data approaches must share the same targets. Data science shall be measured by the business value obtained from it.
- Every decision encompass all relevant knowledge available in the company and outside the company –as long as data protection policies are not compromised-. If there is a data source that might be relevant for the decision, it must be immediately made available. It’s up to the business data scientists to decide on the relevance of a data source.
- If there is a business need, new data sources need to be made available. The cost of acquiring these data sources should be weighted to the monetary impact of the decisions supported by these sources.
- Data has an opportunity window: the sooner it gets to where decisions are made, the higher the value.
- Democratizing data does not mean creating new silos. If new data is available on one end of the company, it must be made available for the entire company (always respecting data protection policies).
- Data Business and Technical Catalogue and Data Lineage practices are essential elements implementing a data democratisation strategy.
- Data-driven decision making is an infinite loop, where knowledge is to be shared via proper documentation and communication.
Final comments and disclaimer
To complement this post, I recommend reading the Royal Statistical Society Data Manifesto, which is also handling the data democratization topic but from the government perspective. While our purpose is to focus on data democratization for enterprises and businesses, it is encouraging to see how both manifestos are aligned.
As a final note, I’d like to say that this is just a proposal, a beta in working state that encapsulates all aspects we understand under Data Democratization. If you want to contribute to it, please feel free to email us or participate in the forum discussion. Likewise, you can provide your comments right here.