It’s oh so easy to get immersed in analytics or big data sets without a clear idea of the questions one wants answered through data. The book Thinking with Data – How to Turn Information into Insights by Max Shron talks about how to get the most of data and how to go about looking for the right data. Max Shron is the founder of Polynumeral, a New York based applied data strategy consultancy. The title of the first chapter of “Thinking with Data” is aptly titled “Scoping: Why Before How” and it covers the main concept behind this book: “CoNVO”. CoNVO stands for context, need, vision, outcome:
- Context (Co) – Context emerges from understanding who we are working with and why they’re doing what they are doing. Who are the people with an interest in the results of the project? What are they trying to achieve and why? Shron offers some good examples of context (see Fig. 1 below). The context provides a project with larger goals and helps to keep us on track when working with data. Contexts include larger relevant details, like deadlines and business objectives, which help to prioritise.
- Needs (N) – It’s useful to see how Shron looks at “needs” from a data perspective; “what are the specific needs that could be fixed by intelligently using data? If our method will be to build a model, the need is not to build a model. The need is to solve the problem that having the model will solve.” Shron goes on to explain that “when we correctly explain a need, we are clearly laying out what it is that could be improved by better knowledge.” I’ve included some good examples of needs in Fig. 2 below.
- Vision (V) – Shron describes the vision as “a glimpse of what it will look like to meet the need with data”. The vision could consist of a mockup describing the intended results, or a sketch of the argument that we’re going to make, or some particular questions that narrowly focus our aims (see Fig. 3 below).
- Outcome (O) – For a data scientist, the “outcome” is all about understanding how the work will actually make it back to the rest of the business and what will happen once it’s there. How will the data and/or insights be used? How will it be integrated into the organisation? Who will use it and why? Shron stresses that the outcome is distinct from the vision; the vision is focused on what form the work will take at the end, while the outcome is focused on what will happen when the work is done (see Fig. 4 below).
Main learning point: Even though I got the sense that “Thinking with Data” is more aimed at data scientists and analysts, I found the book very useful for me as a ‘non-data professional’. Despite it being a very short book, Shron gets his main “CoNVO” concept across very effectively. A good use of data starts with properly scoping the problem that you want to solve. An unstructured scope will make it hard to gather the right insights and to use large data sets intelligently. Using Shron’s CoNVO model will help to gather and analyse data in very targeted and efficient kind of way.
Fig. 1 – Examples of Context – Taken from Max Shron – “Thinking with Data”, p. 3
- This department in a large company handles marketing for a shoe manufacturer with a large online presence. The department’s goal is to convince new customers to try its shoes and to convince existing customers to return again. The final decision maker is the VP of Marketing.
- This news organisation produces stories and editorials for a wide audience. It makes money through advertising and through premium subscriptions to its content. The main decision maker for this project is the head of online business.
Fig. 2 – Examples of Needs – Taken from Max Shron – “Thinking with Data”, p. 5
- Our customers leave our website too quickly, often after reading only one article. We don’t understand who they are, where they are from, or when they leave, and we have no framework for experimenting with new ideas to retain them.
- Is this email campaign effective at raising revenue?
- We want to place our ads in a smart way. What should we be optimising? What is the best choice, given those criteria?
- We want to sell more goods to pregnant women. How do we identify them from their shopping habits?
Fig. 3 – Examples of mockups and argument sketches – Taken from Max Shron – “Thinking with Data”, pp. 9 – 13
Mockups can take the form of a few sentences reporting the outcome of an analysis, a simplified graph that illustrates a relationship between variables, or a user interface sketch that captures how people might use a tool.
Example of a sentence mockup:
The probability that a female employee asks for a flexible schedule is roughly the same as the probability that a male employee asks for a flexible schedule. There are 10,000 users who shopped with service X. Of those 10,000, 2,000 also shopped with service Y. The ones who shopped with service Y skew older, but they also buy more.
A mockup shows what we should expect to take away from a project. In contrast, an argument sketch tells us roughly what we need to do to be convincing at all. It is a loose outline of the statements that will make our work relevant and correct. While they are both collections of sentences, mockups and argument sketches serve very different purposes. Mockups give a flavour of the finished product, while argument sketches give us a sense of the logic behind the solution.
Example of the differences between a mockup and an argument sketch:
Mockup – After making a change to our marketing, we hit an enrolment goal this week that we’ve never hit before, but it isn’t being reflected in the success measures. Argument sketch – The nonprofit is doing well (or poor) because it has high (or low) values for key performance indicators. After seeing the key performance indicators, the reader will have a good sense of the state of the nonprofit’s activities and will be able to adjust accordingly.
Summary of the differences between a mockup and an argument sketch:
In mocking up the outcomes and laying out the argument, we are able to understand what success could look like. The most useful part of making mockups or fragment of arguments is that they let us work backward to fill in what we actually need to do.
Fig. 4 – Examples of an outcome – Taken from Max Shron – “Thinking with Data”, pp. 14 – 16
- The metrics email for the nonprofit needs to be set up, verified, and tweaked. Sysadmins at the nonprofit need to be briefed on how to keep the the email system running. The CTO and CEO need to be trained on how to read the metrics emails, which will consist of a document written to explain it.
- The marketing team needs to be trained in using the model (or software) in order to have it guide their decisions, and the success of the model needs to be gauged in its effect on the sales.