How to Think with Data and get insight
Wahyu Ikbal
@wahyudesu
In today's digital world, data is key in making business decisions. However, many companies use a flawed method by starting with data, then choosing tools and techniques they like. This often leads to predictable but not very impactful results. What if we changed this method? What if we could find really valuable insights from our data? The solution is to change how we analyze data.
First why, then how
Most people approach data the wrong way: They start with a data set, then use their favorite tools and techniques on it. This produces a narrow set of unsurprising results.
When we want to gain knowledge from the data, we should first do some thinking. Before we can answer how we first need to ask why. But this can be surprisingly challenging. The answer is to have a structure to think through all the aspects of a problem.
Context (Co)
Every project has a context, which is the broader situation that sets it apart from the specific issues it aims to address. This context involves understanding who is interested in the project's outcomes, their general goals, and the overall purpose of the project.
An example : A news organization produces stories and editorials for a wide audience. It gets money from advertising and through premium subscriptions to its content. The final decision-maker is the head of online business.
Understanding the context helps us grasp the project's larger goals and guides our decisions. It includes relevant details like deadlines for prioritizing tasks. Contexts can evolve with new partners, employers, or shifts in an organization's mission. For freelancers, it's crucial to understand the context of each project, especially when aiding others. While satisfying personal curiosity is acceptable in moderation, focusing solely on personal interests can lead to missed opportunities to provide value to others. Contexts provide a project with a broader purpose and help maintain focus.
We find context when we know who we are working with and why they're doing what they are doing. We learn the context when we talk to them about their long-term goals. The context provides a project with larger goals and helps to keep us on track.
Needs (N)
In any data project, the first step is to clearly define the specific problems that data can solve, not just the tools or models needed. This involves being open, asking questions, and understanding the problem well.
Data science is about finding useful insights, not just making pretty charts or predictions. For example, if a company wants a dashboard, it might actually need to understand how to improve performance. It's important to focus on the real needs and how data can fill those gaps for real change. The main goal is to use data to make smart decisions and actions. It's important to look past just asking for certain tools and understand the real problems that need solving. This way, data experts can make sure their work really helps organizations reach their bigger goals.
Vision (V)
Before we start collecting data, transforming it, and testing ideas, we need a clear vision of our goal. This vision helps us understand what the end result should look like and how we plan to achieve it. It could be a detailed description of the results we expect, a basic outline of our plan, or specific questions that guide our efforts.
Vision is when we take a glimpse of what it will look like to meet the need with data. The vision consist of the following:
- A mockup explaining the intended results: It could take the form of reporting the outcome of the analysis in a few sentences, a graph, or a user interface sketch that shows how people might use a tool.
- A sketch of the argument we're going to make: It is a loose outline of what we need to do to be convincing.
Example 1
- Vision: The nonprofit that is trying to measure its successes will get an email of key performance indicators on a regular basis. The email will consist of graphs and automatically generated text.
- Mockup: After making a change to our marketing, we hit an enrollment 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 poorly) 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.
Outcome (O)
The data scientist wants to know how the data and/or insights will be used. How will it be integrated into the organization? Who will use the data, and why?
An example of outcome: The marketing team needs to be trained in using the model (or software) to guide their decisions, and the success of the model needs to be gauged in its effect on the sales.
Telling a great project story and a great project scope is challenging. We often don't know the best metric or tools at the start. Focusing too much on the technical details, like math or software, without considering the project's purpose, vision, and expected results can lead to wasted time and effort.
What's next
With a basic understanding of the four areas of a project scope (context, needs, vision, and outcome), we turn our attention to filling in the details of the project. By thinking deeply before digging into the data, we maximize our chances of doing something useful as opposed to simply the first things that come to mind. Working with data can be immersive, creating a balance between exploring quickly and planning thoroughly. something useful as opposed to simply the first things that come to mind. Working with data can be immersive, creating a balance between exploring quickly and planning thoroughly.
Exploring data quickly is tempting, but planning carefully has long-term advantages. This planning includes talking with important people, defining key terms, and organizing ideas and steps. The process isn't straightforward and can change for even simple projects. Bigger projects often start with less clear information, requiring a more organized approach.. It's essential to take detailed notes throughout the project, as these notes will be vital for final documentation and help catch any mistakes.
It's essential to take detailed notes throughout the project, as these notes will be vital for final documentation and help catch any mistakes.
Reference:
https://www.oreilly.com/library/view/thinking-with-data/9781491949757/