In addition, Things notifications have been improved, and now include variable snooze durations (10 min, 30 min, 1 hour). The new Things widgets can be configured in the Notification Center to show to-do lists and quickly glance at what you're doing Today, see what's Upcoming in your schedule, stay on top of your most urgent projects, view tag-filtered lists, and so on. However, the two big changes in this version revolve around the new unified Notification Center in macOS 11. “Begin with an end in mind”…not a bad idea if your organization is serious about leveraging data and analytics for business and operational value.First up, the Things interface has been tweaked throughout to align with the fresh new look of Apple's latest Mac operating system, and includes a remolded app icon to fit in your Dock. The mind map tool can help drive collaboration between the data science team and the business stakeholders to understand where and how AI and advanced analytics to derive and drive new sources of customer, product and operational value. Okay, I know that these images are hard to read, but I think you get the point how a simple mind mapping tool can be used when working with key stakeholders to tease out and validate the key business and operational requirements necessary to ensure a successful data science project. Then in Figure 4, I have walked through an exercise with my key business stakeholders – Store Management, Field Marketing and Store Operations – to understand and validate that I have captured the necessary details to support their “Increase Same Store Sales” business initiative. For example, in Figure 3, I have used the mind mapping software to recreate the business-related panels (in green background) from the Hypothesis Development Canvas in Figure 1. This is an interactive tool (I use iThoughtSX) which we can use in interactive sessions with our key stakeholders to identify and validate the key data science requirements. Now there this is another tool to help us build out our Hypothesis Development Canvas…a mind mapping tool. You can download the spreadsheet from my website (the download is at the bottom of the infographics page). In the blog “Wrestling with Data Science “Second Surgeries?” Try MVE!”, I shared a spreadsheet version of the Hypothesis Development Canvas courtesy of Jason Arnold. The Interactive Hypothesis Development Canvas Think of the Hypothesis Development Canvas as a miniature movie about the “day in the life” of your key business stakeholders in their use of data and analytics in support of their key use cases. Yes, we cheat in our data science process by doing all of this pre-work before we ever “put science to the data.”Īs I discuss in the blog “Does Your Hypothesis Development Canvas Tell a Story?”, a well-structured Hypothesis Development Canvas, like a well-structured storyboard or journey map, provides a concise yet thorough way to make a story – or use case – come to life by personalizing the experience for our business stakeholders (see Figure 2). The Hypothesis Development Canvas is a critical design tool that ensures that we have captured all the important information necessary to deliver material value to the business and operational stakeholders. The Hypothesis Development Canvas ensures that we thoroughly understand the problem our key stakeholders are trying to address, the metrics against which progress and success will be measured, the key decisions that the stakeholders need to make in support of the targeted business problem, the predictions that we’ll need to make to support those decisions and the data sources that might be better predictors of business and operational performance. The Hypothesis Development Canvas ensures alignment between the Data Science team and the business stakeholders in understanding where and how analytics can optimize the organization’s key business initiatives (see Figure 1). To combat the “jump to solution mode too quickly” challenge, our data science team makes extensive use of the Hypothesis Development Canvas. And that’s an approach that leads to customer frustration and lack of adoption. Our data science team tries to live by that simple phrase, but it’s difficult because we want to jump right into “solution mode” …we have seen so many similar problems, that we already know and understand how to solve that problem. A simple phrase that makes everyone’s life easier and more productive. “ Begin with an end in mind” – Stephen Covey “7 Habits of Highly Effective People”
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