Product Data Management (PDM) is the process of collecting, organizing, storing, and sharing data within an organization. You might also have heard that it comes under the umbrella of Product Lifecycle Management (PLM) and is sometimes referred to in software engineering as version control. A Data Product Manager is like a Product Manager, but who focuses more heavily on Product Data Management.
That might sound relatively simple, but there’s much, much more to it than that!
‘Without data, you’re just another person with an opinion’– W. Edwards Deming
There’s just no way around it anymore! If you want to work in product, you need to be comfortable with data. There’s barely a modern industry out there that doesn’t rely on it. It’s especially important for Product Managers. Your ability to understand, manage, and takeaway actionable insights from data will greatly impact your job hunt and your ability to progress in your career.
In this guide, we’ll go over:
- What is a Data Product Manager?
- How do Product Managers Use Data?
- How Top Tech Companies Use Data
- Choosing the Right Product Data Management Software
What is a Data Product Manager?
Data is an integral part of Product Management, as it is with all aspects of product. You may have seen the job title ‘Data Product Manager’ and thought ‘well, aren’t all Product Managers…Data Product Managers?’
It’s a common misconception, but being a data driven Product Manager doesn’t necessarily make you a Data Product Manager. Let’s look at the differences.
A Product Manager sits at the intersection between technology, design, and business. Rather than having any official authority over people, they act as a leader by guiding teams towards a shared end goal. They liaise with the teams, set the OKRs and KPIs, own the roadmap, and manage stakeholder relationships, among many other tasks. To do this well and ensure the success of a product, they need to be data-driven. This is what makes a data-driven Product Manager, and the title is self-proclaimed rather than their official job role.
The Data Product Manager role is responsible for all the same things as a Product Manager, but are more skilled in areas like machine learning and UX/UI. While most PMs will only need a working knowledge of these technical abilities, a Data Product Manager must know them inside and out, and be able to use them in product development. So while they do all the things a Product Manager does, they also have to deep-dive into data on a day to day basis. It’s more than just being data-driven, it’s actively building ways to collect and manage data, and using it to build and perfect a product. You should also know how to avoid common data pitfalls.
The benefit of having a Data Product Manager is that it takes data management out of the hands of individual contributors, and keeps your data sets and insights centralized. This closes the gaps between teams. It also takes pressure off of individual contributors who might not be able to handle data at scale focus on their key duties.
The added responsibilities of a Data Product Manager include choosing and maintaining your product’s Product Data Management Software.
How do Product Managers Use Data?
So do you need to be a complete Data Scientist to be a Product Manager? No. But you still have to build data-driven products.
1. A/B Testing
A/B Testing is a very important and commonly used way of gathering data for a new feature. Product Managers usually have good instincts, but the best way to tell if a feature will work one way or another is to perform A/B tests.
There are many common pitfalls here, and most of them involve the mishandling of data. You need to know what questions you’re asking, what data you want to gather, and what you’ll do with it. It could be as simple as “did more people click the button when it was blue, or when it was red?” Or “if we layout our homepage this way will more users finish the onboarding process?” Depending on your product, it could be infinitely more complex.
2. OKRs and KPIs
As a Product Manager, you’ll also be in charge of setting the Key Performance Indicators (KPIs) and Objectives and Key Results (OKRs) for your product. They must be in line with your company’s business goals, and an understanding of data is absolutely essential for translating these goals into your own.
For example, your company’s main business goal for Q1 might be to expand into a developing market. What does that mean for your product? You need to decide which metrics you’ll use to measure your product’s impact in that new market.
3. Stakeholder communication
Stakeholder Management is the activity of interacting and addressing all stakeholders involved in product management. Numbers are a universal language, and there’s no easier way to communicate with stakeholders than by using numbers. As we mentioned before, without data you’re just a person with another opinion, and in a stakeholder meeting there might be a lot of different opinions. If you can use numbers to back up your position, you’ll have more authority. When a stakeholder requests a feature, you can use objective facts to prove why it won’t work, etc.
4. Usage tracking
You need to know how people are using your product. A good data-driven Product Manager should be able to tell how many features are using feature X compared to feature Y, how many people complete the full onboarding process, etc. Having this kind of information helps you to find areas where users aren’t necessarily doing what you want them to do. This way, you can make improvements and keep your churn rates low.
How Top Tech Companies Use Data
When talking about it in theory, it’s true that data can seem like a dry topic. So when you’re learning about it, it’s important to focus on what you’ll be able to do with it in the future. All the coolest things done by your favorite companies are possible because of good product data management.
One of the companies which is most famous in the product world for managing and leveraging a massive amount of data is Netflix. After all, data comes from users and Netflix has 167 million of them!
Netflix uses information collected from its millions of users to personalize the user experience, in various ways. Information on which shows person A enjoyed can be cross referenced with information on which shows person B enjoyed, to make recommendations. If Sally liked House of Cards and The Wire, then it’s likely that Jim, who also enjoyed House of Cards, will like The Wire too.
Netflix constantly tests the user interface right down to which cover art a user is more likely to prefer. If a user consistently clicks on content showing dramatic close-ups of characters faces, Netflix will show them more of these. If a user prefers content featuring female characters, Netflix will change the cover of The Avengers, for example, from a closeup of Thor to one of Black Widow. All of this is done using data management.
The problem with using basic tools like Google Analytics, is that the booking flow at Airbnb is complex. Users can browse properties whether they’re logged in or not, making it hard to track their progress. They can browse from their mobile device while logged out, and then come home to a desktop, log in, and make the booking immediately. According to Google Analytics, all they did was log in and book the first property they saw within minutes. This gives the product team a skewed vision of the customer journey. This is why it’s important to use more comprehensive PDM tools which allow for the tracking of multiple flows.
Airbnb is famously in love with experimenting, and they built their own A/B testing framework to do so. Jan Overgoor, a former data scientist at Airbnb, wrote about how experiments at Airbnb improved the product.
As a Product Manager, you don’t need to be able to design these experiments yourself. But you have to be able to understand which experiments need to be run, what metrics you’re looking to measure, and what actionable steps you’ll take once the experiment is done. You’ll work closely with Data Scientists and Product Data Analysts, and communicating with them is crucial.
Apple is another company which runs on Big Data. As an industry leader in data-driven products, Apple collects, manages, and leverages a massive amount of data. One of the newer ways of gathering data is through wearable technology. With 43 million Apple Watch users, Apple and IBM created a partnership to make the most out of big data analytics in digital health information.
Sensors are collecting information on how much sleep users get, how active they are, their calorie intake, along with many other factors. This information can measure the general health of a population easily and in huge quantities. It’s a potential that has the product world buzzing with excitement!
If you want to continue building your career at Apple, check out How to Get a Product Management Job at Apple.
Another company which relies heavily on data analytics is Facebook, who’s ads platform is one of the most popular forms of paid social advertising out there. Its ads platform is one of the most sophisticated for marketers, because it knows exactly what its 1.62 billion daily users like and where they go on the web. Not only does this make the ads manager an appealing data product for marketers (making Facebook an absolute fortune in revenue) but it personalizes the experience for users. It’s this endless cycle of good content for users and good click rates for advertisers which has helped Facebook grow at scale.
However, we couldn’t talk about Facebook and Big Data without addressing the elephant in the room…the scandals. While data science for product development is exciting, it’s the responsibility of a Product Manager to ask “we can do this, but should we?” Taking the ethical implications of data usage into account will pay off in the long run, and could save you a lot of trouble in the future, especially if you’re working with data products.
Choosing the Right Product Data Management Software
As a Product Manager, part of your responsibility may be choosing a PDM tool.
Some software will be more suited to giant corporations that need many people to have access to the same data sets, others will be better for a smaller startup which only needs a handful of people to work with the data. So choose based on your situation.
However, there are some basic requirements that you should look for when choosing your PDM tool. It should:
- Facilitate collaboration easily between teams
- Reduce development errors
- Fit your budget/usage
- Improve productivity
- Help you find the data you need as quickly as possible
- Maintain your company’s security standards
Here are some examples of commonly used PDM software;
- Sales Layer: A PIM tool which offers PDM functionality. Centralizes all product information including technical sheets and SKUs, with automated information updates.
- Siemens PDM: The most common and most recognized PDM software, and the best to get familiar with if you are applying for Data Product Manager roles. Designed for both CAD and non-CAD users, it’s accessible to everyone in the business to access data from multiple applications.
Product Data Management in a Nutshell
If that felt like a lot of information to take in, we can’t blame you! But after all, taking in a lot of information and understanding it is what being a data-driven product manager is all about!
Let’s go over what we talked about, and condense it down to the main points
- Data is an integral part of PM, but some use it more than others.
- All Data PMs are PMs, but not all PMs are Data PMs.
- All successful products are driven by data.
- The most successful products are the ones using massive amounts of data in new and innovative ways.
- Make data more exciting by thinking about the possibilities, not the theory.
- Use the right DPM software to increase productivity and give all team members access to all data.
If you liked this article, and want to keep learning, check out more from our ‘What Is?’ series, and build your product knowledge base, starting with, What is Product Marketing Management?