Project Plan Presentation; Explain & Convince

One of the key business skillsets in data science is doing a great project plan presentation. Now this is true for almost every job, but in particular for data scientists. Why? It has to do with the nature of data science projects. Specifically the innovative aspects of data science projects.

As data scientists we are at the forefront of innovation, changing how people and companies do work. Hence we not only convince people the project is worth doing. We also have to convince people to change their historic way of working. People will be skeptic, part of convincing them is based on your technical expertise. There is a comfort in numbers and projections. But there is also a softer side to your storyline. Today I want to focus on some pointers on how to share your ideas with maximum impact.

In this series we earlier discussed how to write a great project plan, you can check that out here.

Craft a positive outlook

If my project succeeds, this will happen… The way we work will improve, get more cost efficient or we make more sales. All projects strive for something, what is so important is to start off at this final product and build this better future in the minds of your audience. It is however even more important to not build this outlook from a negative starting point. Let’s look at an example:

“Think of a new machine learning algorithm you want to design for an app of the company’s home inspection team, those guys that go to homes and check on the state of window frames for example. There is a reasonable chance you started this project because currently the home inspection team misses a lot of cases, or they call in maintenance crews far to often and find to many faults. In any case there is a problem in the status quo and room for potential improvement.”

It is now up to you to help paint them a picture of a new and better future, this algorithm will make their lives easier. However it is also up to you to realize that their first perspective can’t be that their current lives are bad, difficult, or any other negative connotation. Compare these 2 sentences:

“When this app is successful, we will be able to together work on new services and expand the business.”

“This machine learning algorithm will help our maintenance crews decrease their extreme workloads.  “

Which sentence inspires to move forward? Which sentence implicitly states the status quo is full of problems? I have seen a lot of presentations that explain the project benefits by going into detail on how big the current problems are. The thing is everyone is either aware of these problems or perhaps does not even recognize them as a problem. This loses your audience. Focus on the future, and invite your audience to travel with you.

The gift of responsibility

So we are traveling together, you are probably looking for a project team. What you should try and focus on is to give people their own responsibility as much as possible. Craft clear roles and communicate them. Do this the right way and you will have hit a major milestone in the project’s success. Let me show you 2 sentences again:

“To design the app perfectly for your daily jobs, I need your help to tell me what works for you.”

“You will have to tell me how to design the app, if you can test how to work with it on a daily basis the launch will be successful.”

What I am looking for here is to give a clear purpose and responsibility to your coworkers in the project, make them part of the success or failure as much as possible. Compare sentence 1 and sentence 2, which one conveys this more directly? Communicating this properly helps you in multiple ways, both by allowing you to focus on what Is in your own control and by sharing responsibility for the projects outcome. Put all this together and you are more likely to receive the commitment you need during the project.

Tell a story of your end results

Now that you have discussed where you want to go and who you will need to do what along the way, its time to ingrain your end goal into the minds of your audience. The key here is to tease your end result enough, by showing some examples and mockups, but avoid filling in all the details. Instead let your audience fill out the details and leave some room for questions.

See you know where your going, but what you will do precisely when you get? You may have an idea of how the new algorithm will fit into the daily work of your home inspectors, but  keep an open mind, if someone else has a great idea then leave space for that.

The story you should tell is focused on the practical day to day activities, don’t compare the current workday to the future workday, describe only the future. People know what they do on a daily basis better then you so you run the risk of coming across as someone who doesn’t know their facts. People don’t know the new future you will develop together in the coming months, so share some little bits. Take a look at this:

“When you enter the building, you will be able to take your ipad and take pictures of the current state of the building as usual. From this point on, the app will be there for you whenever help is needed. Unsure of the level of degradation? Unclear if you really saw every part of the window frame? For every question you will either automatically be guided by the algorithm or easily consult a coworker.”

Wrapping up Project Plan Presentation

These 3 pointers should help you convince more effectively that your projects are, worth doing. Craft a positive framework, delegate roles and share responsibility will help you build your future. What tips do you have to tag people along for your projects? Let me know below.

See you in a click!


Writing a Data Science Project Plan; 5 Components explained

Writing a great Data Science project plan is a special skill. It does not come naturally to many people, me included. Over the past years I have written many project plans. In my own experience it’s easy to get lost on what formats to use and what content to include. Also how do you balance explaining the process of the project to the actual content of the project. There are many tutorials and templates about how to run your project. But in this blog I will give you some tips & tricks for writing it down.

This skill is very important, it’s your project plans that communicate your ideas. These documents are shared extensively with your coworkers and bosses. Being able to write a great plan gives you freedom at work. It allows you to work on your own vision of what is most important to the company. From a data science perspective this is extra important. We are busy with new technology and fresh ideas that need extra convincing. If you fail to develop this skill you are in danger of spending your working life focusing on plans from other people. Always remember that your project plan is meant to inspire, explain and convince. Here are 5 project plan components that helped me along in the past.

Introduce the bigger scope

We all start somewhere, and your data science project plan begins here. I find great benefit in starting with the bigger company strategy your project contributes towards. This gives your readers a clear initial scope and they should be able to relate to your ideas immediately. It is so crucially important to start writing from the bigger scope & vision of your company or client. Explain time and again where you want to go, and how your project aids to this vision. If you get people to buy into the bigger picture off your project plan, they will follow your specifics later.

An example of this concept might be helpful.

Lets say your companies goal is to help speed up the transition to clean energy. You are doing this by selling solar panels. You have a dataset on lifecycle management & repairs of the solar panels. The project plan describes how you want to more accurately predict when repairs might be needed. Now that we have introduced the project within the scope of the company it almost feels unnatural, but lets continue.

The big nono is to start by explaining the current issues with repairs and breakdown of solar panels. Instead, take the time and effort to link your project to the larger vision and how it contributes to that. First explain your macro vision, then get specific.

Visualize & story tell your specifics.

In your first paragraph you’ve reminded everyone why we are doing what we are. People now know how this project fits this framework. Your next goal is to get specific, what a good data science project plan needs is a good and precise technical goal. Link this to a current problem.

is there a statistic or graph that displays your current issues? This is especially very important when your project is focused on analyzing a dataset. Try to visualize your data and problem, and visualize your goal of improving this.

Jumping back to our solar panel company, lets say current lifetime of solar panels is 20 years. Repairs are common in a specific timeframe. Perhaps we show people a survival curve, and we can visualize how this curve can change after the project.

If your project is more focused on working with people, this section is great to feature your end users. Ideally they will explain the problem. If this is not feasible, apply some storytelling of what this person runs into on a daily basis. From that background you can introduce your dashboard or app that will improve their decision making.

The goal here is to immediately ingrain, through visualization or storytelling,  in your readers what the main problem is your focusing on and how this problem will disappear in the future.

Explain your projects process

Now is the time to convince people. Think of it like this, so far your plan is easy to follow and believe in, we have accurately shown the project fits the broader scope and explained the problem and specific technical goal. But how are you going to achieve this? This is where you can lose your readers or have the commit to your idea. As mentioned there are many tutorials on different project processes, use this to your advantage and choose an appropriate one for your analysis.

The most important thing I’ve learned is that it’s always better to apply your process model to the project directly. How are you going to work in this specific case? Your readers are looking for realism, if you expect that the modelling phase of your project is going to be the most important step for success, write it down. This has two benefits, one is that your reader doesn’t have to get lost in how projects are run in theory, and second if you do this step correctly you can reference the project plan later when your actually working.

Deep-dive on specifics, specifically

So far the project plan has been non-technical, if you’ve done it properly then you have not yet explained the specifics of your machine learning algorithm or the nitty gritty of the new to be developed data warehouse architecture. Now is the time to explain in more detail these elements, look back at your problem scope and intended technical goal. What methodology are you going to use to solve this issue? How is your dashboard going to look? This section is, most crucially of all the steps in your project plan, dependent on your target readers.

Think of it like this, remember the purpose of your project plan is to inspire, explain and convince. This section is where you convince people, but everyone gets convinced differently. We have a collective habit to get technical and want to let everybody know our solution, so that they start believing too. This is known as the ‘what’ of your solution. But this step only hits home to your technical colleagues, if they don’t have to read your project plan, don’t write this paragraph.

If you need to convince your boss, then you will have to do more work here, not to further explain and teach, but to simplify and generalize your methodology. If your going to build a dashboard, provide a mockup here and explain the intended use in more detail. Focus on answering why this specific solution fixes the problem scope you made earlier. Stay clear of explaining the ‘what’ too much.

Plan for problems

Planning is the final leg of your project plan. This section also dependent more than others on your company’s culture and the projects specifics. Is it important to be precise? Are there external deadlines that need to be met? Or can you discuss during the project how much priority its getting? These are all questions I can’t answer for you, but they do determine how to write your planning and how much time and effort you need to put in this paragraph. In our company we run many internal projects at once, often juggling them for prioritization. This means that planning and deadlines are often flexible.

What I have learned over the years is that it is crucially important to describe your project planning risks here, where do you expect a high probability of a roadblock? What would that look like and how does it affect the timeline you initially had in mind? This is very important information for your readers too and it shows you have thought off not only how to make things work, but also how things can go wrong. Then when things do go wrong, you can refer to your project plan and discuss with your team what the consequences are.

Writing a data science project plan; wrapup

The 5 components I outlined above are important elements in every data science project plan. I’ve found them to be invaluable for keeping structure and focusing on the main points.

If you learn to do this correctly it will ultimately allow you to work on the project you love, instead of having to learn to love other people’s projects.