I've previously mentioned down-to-earth tips on how to boost data literacy in your company. Now, I want to discuss why it matters by revisiting some high-level concepts: data and knowledge.
Data, as defined by the Cambridge Dictionary, is "information, especially facts or numbers, collected to be examined and considered and used to help decision-making. In its electronic form, it can be stored and used by computers."
Knowledge, still defined by the Cambridge Dictionary, is "understanding of or information about a subject that you get by experience or study, either known by one person or by people generally."
How does this apply to your (data) team? Read on.
Interestingly enough, the term "information" can be found in both data and knowledge definitions. In fact, I would actually define "information" as an intermediate state and the link between "data" and "knowledge".
Data refers to raw facts or numbers collected by studying or running experiences about a subject. It is the most basic unit and often lacks context or meaning on its own. Data can be both quantitative or qualitative.
e.g.: "We collected data points for all our most recent ad campaigns."
Information is created when data is processed, organized, structured, or presented in a useful way. Information allows for a beginning of understanding and interpretation and provides the context needed to answer specific questions.
e.g.: "Our CPC ranges between $4.25 and $12.3 depending on the ads and audience."
Knowledge, however, is the result of analyzing, interpreting, and deeply understanding information. It involves the application of experience, expertise, or various cognitive abilities to ultimately form conclusions, insights, or predictions.
e.g.: "We iterated on the various marketing efforts and found that we actually need these ads to boost the conversion of other inbound efforts. Publishing content and ads on similar topics in the same month boosts our conversion rate by +4%, creating an additional $300k in revenue."
Knowledge allows individuals or organizations to make informed decisions and take appropriate actions based on context. It enables to recognize specific situations that happened before.
By quickly identifying a new situation, one is able to compare previous actions and their impacts in order to make the best possible decision.
Sharing knowledge then becomes critical to stay a high-performing organization. This is what data literacy is actually about: sharing the knowledge extracted from the data.
While knowledge is hard to create, it's a bit easier to spread. You should make sure that when you and your team create additional knowledge pieces (thanks to ad-hoc analyses, for example), you share them as broadly as possible. Not only the data points but the entire reasoning and conclusions, so someone else can build their own knowledge on top of it.
Make knowledge something that can be shared, retrieved, improved, and reused by you and your teammates.
If this brings questions to you? Feel free to reach out! As always, I will be pleased to discuss the matter with you 😃
P.S.: Why do I love the matter so much?
Husprey is on a mission to ease knowledge creation from raw data. We are convinced that in the next 5 years Notion (or GDocs) and Tableau (or Looker) will need to get very close from each other to continue to bring value in companies. And in fact, they will look a lot like... notebooks (👋👋) used by both analysts and business users (👀 👀).