¿Cómo muestra a las partes interesadas el valor de su marco de ciencia de datos?

Defining what the business wants to solve and how to track the success metric is essential. If we want a successful project, we need to know what we are trying to solve. Many data projects failed because the business goals are not specific enough or impossible to meet. That is why, discussing the requirement is very important at the beginning of the project.

To align a data science framework with business goals, it is important to understand the challenges and opportunities of the business. Once that is done, data science techniques such as predictive analytics, machine learning, and natural language processing can be used to identify patterns in data that can be used to inform decisions.

There are two levels of business goals: those at the organizational level and those at the business unit level, which are derived from the organization's goals. While business goals focus on achieving their primary metric, business units either have guardrails or supportive metrics to contribute to this primary metric. It is important to consider the value of your data science models in achieving these goals. However, there are no one-size-fits-all standards for achieving this. Each organization has its own culture and framework that data scientists must pay attention to.

At the center of a whiteboard: Write down the Core Business Goal. : Answer questions on data requirements, time required, external integrations and success measures. : For each answer, draw an extension. Group these extensions spatially in accordance with completeness. : Now, connect each extension with a specific answer:: How can I know if this extension helped? Ideally this will become your KPI. : You can add more levels of extensions till you reach the KPI level. This is where you get business onboard and clearly discuss on requirements to outcome flow.

"Imagine our data science framework as a gourmet recipe – we blend data ingredients, sprinkle predictive seasoning, and serve up insights so delicious, stakeholders will be asking for seconds!"

1. Use storytelling techniques to communicate your data science framework in a clear and engaging way. 2. Craft a narrative that connects the data, the analysis, and the insights to the business context and the stakeholder's needs. 3. Use visualizations, metaphors, analogies, and examples to illustrate your points and make them memorable. 4. Structure your story using a framework like STAR (Situation, Task, Action, Result) to highlight the value of your data science framework. 5. Use real-world examples or case studies to illustrate the effectiveness of your framework and help stakeholders envision its potential impact on their business challenges.

Drawing from my experiences, I've come to view the process of providing evidence and feedback as the heartbeat of any data science initiative. It's one thing to design and implement a framework, but its true value emerges when we can tangibly demonstrate its impact. Metrics and results are the language that translates our efforts into business value. Moreover, feedback is the compass that guides refinements, ensuring our approach remains agile and aligned with stakeholder needs. I often lean on methodologies like PDCA, not just as a systematic approach, but as a philosophy that champions adaptability and continuous growth in the ever-evolving landscape of data science.

It is very easy to develop a data science model whose output is simply more data. The hard (and transformative) part is to derive information that will actually inform decisions or create new constructs in the organization. The easiest way of understanding whether your framework is complete is by observing how it is being used and/or perceived by your stakeholders. This is the moment when you usually find out what to tweak in the model, what implementation was missing, or if you just need to better communicate the results. The closer you are to your stakeholders, the better your awareness of these matters.

I have come to believe - scaling and adaptability points do not make much sense in discussions with stakeholders that are users - to start with. These discussions come up when the analytical solution is performing and you have to kick-start discussions about scaling. This point usually is part of your solution framework but discussions on this come very later in the game.

In my journey through the data science landscape, I've come to realize that the backbone of any robust framework isn't just the algorithms or tools, but the adherence to best practices and standards. It's these guiding principles that ensure our work is not only effective but also ethical and trustworthy. Especially in today's data-driven world, where concerns about data privacy and security are paramount, it's imperative to have a framework that stands up to scrutiny. By aligning with standards like DMBOK, we're not just ticking boxes; we're sending a clear message about our commitment to excellence, integrity, and the responsible use of data. It's this commitment that builds trust and fosters lasting collaborations.

I second this. Helping stakeholders to ultimately transform from "users" to "decision makers" using data as a means to do so is crucial to harness the full power of data and its immediacy. In addition, provide comprehensive documentation and tutorials that guide users through the process of scaling and adapting the framework to their specific needs. Real-world examples are valuable.

Education to Empowering journey happens with 'iterative understanding' of concepts and their usefulness to stakeholders. Some might be ahead on the journey than others. Have seen this useful in dealing with multiple different teams involved in same project - like Analytics + Marketing + Sales Support. Having a simple visio or flow (high level) helps - which I keep ready at start of every meeting. This helps convert FYI users to informed users and then gradually pivot them to super users/evangelists.

1. Identifying data champions in the other departments so as to ensure buy-in from all departments. 2. Ensure you keep the conversations going so as to keep improving the created metrics, data stories and collect feedback on improvements to make and new hypothesis to test 3. Try to compare your generated insights/data use cases to your competitors and other companies who have had sucess stories from using same data science techniques. Your stakeholders will have a stronger sense of ownership when they see how it worked well for others.