
A broad introduction to the course, going through the reasons why it was created and the outline of the curriculum
Understand what has boosted the need for data product managers and how data products provide competitive advantage to businesses today.
Overview on the different types , formats and sources of data
What is a KPI and how do we create a holistic KPI framework?
What do we mean by experimentation and hypothesis testing? What is AB test and what a multivariate test?
Do we blindly trust only data or do we add some intuition from experience as well in the mix? What is the difference of the 2 approaches
Recap what we have learnt
Section Introduction and outline
What is the data as a product approach, how it was developed and how it is adding value
What is a data platform product and why is it important?
The star of the course! Data products , their value, types and characteristics
The most common and strategic type of data products; what is an analytical data product , why is it important and what are its key features
Another type of data products; the operational ones. Dive deeper on what they do and what value they add.
The reason many of you are here! What are AI and ML products and what are their key features?
Recap on what we have learnt.
Section intro and outline
The key on crafting an effective data strategy is to first understand the broader strategy and objectives of the organisation. Here we will dive deeper on what, why and how we do this.
Understand what we mean about data assets and what are the sources they come from.
What is a data producer and what a data consumer? What is the origin of a data pipeline and what is its destination? Dive deeper into one of the most crucial parts of data products and infrastructure.
The blueprint of data strategy is how to organise and record your data. Here we will learn all about normalisation and events management.
You have probably heard about relational databases. Although they are indeed the most common, they are just one of the many types of databases out there. Learn about columnar , key value pairs and more databases, their pros and cons and how you would select the right one for your organisation.
Would you opt for an ETL or an ELT processing sequence? What is a flat file and what are its pros against APIs? What is a CRM and what an ERP? Learn more about your processing options.
What is a data warehouse and what is a data lake? What are the pros of each and how do you choose the right approach for your organisation? Can you combine them?
How do you model your data to fit the purpose of your data products? What approaches are out there and how you make sure you chose the right one?
What is a centralised data platform and what is a data mesh? Learn more about these "trendy" concepts here
How will users interact with your data products? What is the objective of data visualisation, what are the best practices and most important pitfalls to avoid?
Would you opt for a cloud data platform solution or do you want to keep things within your premises? Learn more about these important decisions and what criteria you should choose.
There are more important decisions to consider. Do you build a data platform or would you buy one? And if so, would you go for commercial or out of the box solutions? What are the cost but also skill requirements? And who will do this, your in house team or an outsourced one?
How do you build an effective data culture across the organisation to foster your data strategy objectives?
Recap on what we have learnt
Section Introduction
What is data governance and why is it important?
Learn more about the data governance principles, the pillars based on which your strategy is built.
One of the most important aspects of data governance is data quality and integrity. Lets learn why
What are some popular data governance frameworks, which are their objectives, components and pros & cons?
How do you built trust in data and why is this important?
What kind of tools are out there within the data governance space, what are their objectives and why should you care?
Recap on what we have learnt
Section Introduction
The start of every product! How do you identify the right opportunities and how do you start the discovery process
After identifying many opportunities you need to prioritise which ones to tackle. Here we ll talk about this process and go through some key frameworks and examples of prioritisation.
What is a minimum viable product and how does it differ from a prototype? Why are both important?
Service level agreements and data contracts are bespoke parts of the lifecycle , here we will dive deeper on why
Dive deeper into the intricacies of developing , testing and deploying data products
Understand why we need to monitor and evaluate our products so we can optimise them and ensure they still cover the needs of stakeholders.
Data products don't come with their fair amount of challenges! Here we will dive deeper in these so you are better prepared.
Recap on what we have learnt
Section introduction and outline
Understand the key technical skills you need to master, from SQL to machine learning algorithms
A data PM cannot survive in this landscape without a true understanding of the business and industry he operates in! Learn more about how these competencies will help you.
A data PM's life is all about identifying and solving problems. See how these skills can make your life easier.
A product manager needs to lead his team under a common purpose and goal. See how leadership skills can help you in that journey.
Nowadays all product development teams are working under agile methodologies, be it Scrum, Kanban or ML Ops. Learn more about these and be better prepared .
Understanding who your stakeholders are and what are their needs is one of the most crucial parts of the job. Learn some tips on how to do this effectively.
If only you had enough time and resources to pursue all opportunities. Prioritisation is essential to make sure you are allocating your resources in the most value adding and feasible objectives.
Here is where you shine! Roadmaps are effective communication tools for both your stakeholders and your internal development team. Learn how to craft them effectively.
Communication is perhaps the most important skill in a data PM's arsenal. Learn why and how you can improve it.
You can never make everyone happy. But you can keep them from getting upset. Here we will dive deeper in some of the techniques for effective expectations management
Data PM's do not live in silos. Your data team is your best ally. Learn how you can work with them most efficiently.
Collaborating with external and cross functional is not a nice to have; it is a guarantee for success for your products. Learn why.
Recap on what we have learnt.
Section introduction and outline
Understand the differences of the various data product roles and how to find the right one for your skills and needs
Learn how to ace your interview!
You don't get a second chance for a first impression! Make your first 90 days matter!
Your learning journey does not end here. Make sure you keep yourself posted on changes and new concepts
Welcome to the "Fundamentals of Data Product Management and Strategy," an essential course tailored for those who aspire to tap into the immense power of data for strategic advantage. If you're considering a career in data product management or if you're in search of a holistic understanding of how data can steer your business objectives, this course is your perfect companion.
In the world that is increasingly centred around data, its value cannot be overstated. Data products, in particular, have become vital tools for organisations, harnessing raw data and transforming it into meaningful insights that guide decision-making and strategic planning. The advent of Machine Learning and Artificial Intelligence products has only further accentuated this trend, promising unprecedented opportunities for those ready to seize them.
However, there's a notable void in the learning marketplace - while many courses focus on data-driven product management, few truly address data product management. Our course intends to bridge this gap, providing an in-depth exploration of the crucial elements of data product management without overwhelming you with excessive technical details.
We offer an immersive learning experience into the world of data strategy, a pivotal aspect often overlooked in conventional courses. Understanding how to strategise and operationalise data can significantly boost your organisation's business success, making it an indispensable part of your learning journey.
But our course doesn't stop at that. It delves deeper, exploring the complete lifecycle of data products - from identification and design to development, testing, and deployment. We also equip you with the vital skills you'll need as a data product manager, including mastering roadmaps, managing expectations, collaboration tactics with internal and external teams, and leveraging agile methodologies.
In the "Fundamentals of Data Product Management and Strategy," we aim to demystify the complexities of data product management and present it as a strategic differentiator in today's data-centric world. Join us on this enlightening journey today and step confidently into the future of data.