Review of “The Decision Makers Handbook to Data Science”
A summary of Dr. Stylianos Kampakis’ book on Data Science, covers the history, now, with great insights on how to apply in your business, from data strategy to data management, from hiring to building the data culture.
We will be hosting Dr. Stylianos Kampakis on our Agile Data London meetup this month, and I wanted to give a brief summary on his book as I found it very useful for many practical reasons, join us if you would like to ask your questions live.
Building a data science team can be tricky, from the idea creation to setting up the team, and going to live, and Kampakis’ book can help you to start your journey with good questions. Rather than a summary of chapter by chapter, I will go through the top three topics on his Data Strategy Canvas, which is also available as a free tool on his website. It is different than Louis Dorard’s Machine Learning Canvas, and combining both can also result in a powerful tool…
Data Strategy describes a “set of choices and decisions that together, chart a high-level course of action to achieve high-level goals.”
- Challenge: What is the problem you are trying to solve? The trick here is to state the challenge as a question, so that everyone in your team can look at from the same lenses, and be encouraged to ask more question about the challenge.
- Data Sources: It is critical identifying the sources and usefulness of them, either quantitate or qualitative, experiments, surveys, observations. Kampakis highlighted several issues with data collection. First one is the data not being recorded properly, either human error or noisy sensors. Second one is that when data collection is not supervised, monitored, verified, it can result in noise and mistakes. Third issue is data not being clear, without a descriptive documentation about itself, which can create misunderstandings and miscommunication, especially for new entrants. Forth one is about the legal requirements, where data is not stored compliant with the legislations, like GDPR, which can result in huge fine. Fifth one is the ownership of the collection/storage of the data rather a centralised system. And lastly, the consolidation issues where multiple data sources stored in multiple locations can make it tricky to create a unified view of your data.
- Data Acquisition: Deciding which data we should acquire can be tricky too. Kampakis suggests four dimensions: appropriateness of the data, nature of the data, time requirement, cost of acquisition. They involve asking the questions like what the right dataset look like, how much bias and noise my dataset will have, how long time do we need to acquire the data to get ready to be consumed and how much does it cost for the data to be available when we want serve. The tradeoff should be all considered as part of the process.
- Methods: You can start asking which methods will be used to hire the right data scientist with the right skillset, or after the hire, to explore the options. Each business problem can be unique and may require simpler algorithms, so a thoroughly discussion can help you to stop you from boiling the ocean, when a simple unsupervised learning algorithm can save you time and cost from deep learning investment.
- Success criteria: What good looks like? A common understanding and acceptance of the goal by the whole team will empower them, and when it has milestones broken down, it will help the team to navigate through the trenches with confidence.
- People: There are so many titles, roles, suggested by data community, and Kampakis has another take on this. He does not put Software Engineer to his diagram, but a Hacker, as he believes the quality of the code that data scientists produce does not need to be production level. Data scientists try to solve a problem with just enough tooling. Do you agree?
I hope it created bulb light moment for your plans or will engage your team in good conversations to look at the problems from different angles. I am looking forward to your comments. What do you think about Kampakis Data Strategy Canvas? Which one was your favourite one? What do you think about the hackers?
