Data < Information < Knowledge < Wisdom
I’ve had the opportunity to work on several products in my career that productize knowledge. In 2016, I collaborated with the founders of Wisdo to create a productization of wisdom. We signal tested hundreds of different productizations and eventually landed on what is the foundation of their now scale product which has helped millions of people and won a ton of awards.
I learned a lot of very valuable things from working on these products and we developed a mental model that I use almost every day for unpacking whitespace, product analysis and building new products. That mental model is called Data < Information < Knowledge < Wisdom. Here’s a good explainer for the model:
Data
Data can be defined as a) facts and statistics that are collected together for reference or analysis or b) things known or assumed as facts and are used as the basis for reasoning or calculation. I think of data as being loose bits and pieces of stuff that exist around us.
Information
Information can be defined as Data that has been aggregated, processed and organized for the purposes of adding clarity and enabling analysis.
Knowledge
Knowledge can be defined as Information that has been analyzed to gain insights, perspective and belief.
Wisdom
Wisdom can be defined as Knowledge that has been combined with practical experience.
Here are some practical examples of Data, Information, Knowledge and Wisdom products that millions of people use every day:
Data
RSS feeds
Accounting Software
Dashboard
User Data
Data generated from use of products
APIs
Information
Google Search
Calendars
News
Knowledge
Grammarly
Wisdom
The first phase of the Internet was largely focused on Data as being the basis of products. The Internet allowed sharing of Data between parties in a connected form and most initial productizations centered around this value transaction.
As Google became the dominant search engine, their entire value proposition was enabling easy discovery of Data. Google Search is an Information product. It doesn’t provide analysis or add experience to its search results and it is squarely oriented towards discovery of what exists online.
As the Internet became littered with anything and everything, Data and Information became commoditized and the market bifurcated to Private and Public Data and Information. Think paywalled news sites, reports for sale and for fee content for Private.
The next phase of development occurred when creators started adding analysis to Information to create Knowledge. The last 20 years of product development for the Internet has largely been unlocking and productizing Knowledge across most product categories. Think Bloomberg, Tableau and Strava Metro.
I believe that one of the next phases of the Internet will be to productize Wisdom. Said differently, product creators will add the practical experiences of their users and customers to their knowledge products to create products that are wise. A critical flaw of Knowledge products is that their value is based upon analysis from humans or machines based upon inference, reasoning and deduction not real practical experience of what has occurred in the past. This flaw is magnified when you consider that inference, reasoning and deduction is fallible and solely based upon how a human or machine thinks about a specific topic. Wisdom products are based upon practical experience which provides real validation for the set of possibilities for what could occur and it enables adding weighted probabilities to the likelihood of that things occurring. There is a reason why wisdom is viewed as being of the highest order of value because it’s reflective of validation and truth.
As important as Wisdom is, it’s a lagging indicator and indicative of what happened before. It’s not indicative of what will happen next or to be used as the basis for predictions. But it provides a strong baseline that can be used to assess current state situations, opportunities, problems and decisions.
I use this Data < Information < Knowledge < Wisdom mental model every day. Many products generate Data as a consequence of users engaging with them and we’re at a moment in time when many many things can be measured which also creates even more Data.
Given this proliferation of Data, it begs the question of what to do with it. Some product creators package it in the form of reports and sell it as Information or use it as the basis of analytics and reporting as Knowledge products. Fewer creators aggregate experience and make Wisdom products.
I see a lot of products that generate Data and they deliver dashboards and light trend views to their users as a form of Information. Think Apple Watch, Fitbit, scales, thermometers, calendars etc. These products are missing a big opportunity to go a step further and create Knowledge Graphs or Wisdom Graphs. If your Apple watch could tell you when exactly to rest based upon your past experiences, it’d be a much more compelling pitch than just telling you that you’ve been sitting down too long.
A practical example is Cloverleaf AI, a video-based research tool that extracts signal (Knowledge) from millions of hours of government meetings (data). Cloverleaf is used by hundreds of companies who are impacted by changes in legislation, regulations and public consultations. They notify customers of signals and they also syndicate past signals and decisions to provide a view into how comparable situations were handled in the past (Wisdom). This combination of features/functionality provides a compelling product to customers that is super sticky and has a built in moat. Bonus points would be if they evolved generationally to becoming Predictive using past signals and decisions to infer the highest probability outcomes for current state to future state signals.
I encourage product creators to audit their product, category, competitors and comparables using the Data < Information < Knowledge < Wisdom mental model. By codifying those products based upon what the product delivers (Data, Information, Knowledge or Wisdom), it can shine a light on opportunities to compete by adding higher order of value aspects to their product.
If your category is populated with Data products, organizing to create Information or adding analysis to create Knowledge are viable options for establishing a strong market position for your product.
If your category is populated with Knowledge oriented products, a clear Differentiation Strategy would be to specialize by adding experience and delivering a Wisdom product.
When you combine this mental model with the Generational Product framework from my book The Future Is Invisible, you get a super strong strategic set of whitespace options for products that are unique, ownable and can endure generationally.
If you have any questions on the Data < Information < Knowledge < Wisdom mental model, please reach out.
