• Skip to main content
  • Skip to secondary menu
  • Skip to primary sidebar
  • Skip to footer
  • Articles
  • News
  • Events
  • Advertize
  • Jobs
  • Courses
  • Contact
  • (0)
  • LoginRegister
    • Facebook
    • LinkedIn
    • RSS
      Articles
      News
      Events
      Job Posts
    • Twitter
Datafloq

Datafloq

Data and Technology Insights

  • Categories
    • Big Data
    • Blockchain
    • Cloud
    • Internet Of Things
    • Metaverse
    • Robotics
    • Cybersecurity
    • Startups
    • Strategy
    • Technical
  • Big Data
  • Blockchain
  • Cloud
  • Metaverse
  • Internet Of Things
  • Robotics
  • Cybersecurity
  • Startups
  • Strategy
  • Technical

What Good Data Product Managers Do – And Why You Probably Need One

Barr Moses / 8 min read.
February 9, 2023
Datafloq AI Score
×

Datafloq AI Score: 59.33

Datafloq enables anyone to contribute articles, but we value high-quality content. This means that we do not accept SEO link building content, spammy articles, clickbait, articles written by bots and especially not misinformation. Therefore, we have developed an AI, built using multiple built open-source and proprietary tools to instantly define whether an article is written by a human or a bot and determine the level of bias, objectivity, whether it is fact-based or not, sentiment and overall quality.

Articles published on Datafloq need to have a minimum AI score of 60% and we provide this graph to give more detailed information on how we rate this article. Please note that this is a work in progress and if you have any suggestions, feel free to contact us.

floq.to/5u90w

Atul Gupte co-authored this post.

The companies we talk to are diligently building their data product or platform. This includes migrating to Snowflake, integrating with Databricks, moving towards a data mesh, or generally investing in their data stack.

Increasingly, we are seeing data departments modernize their team structure with data product managers at the helm of such projects.

The search volume for data project manager has increased over the last 10 years according to Google Trends data.

The search volume for data project manager has increased over the last 10 years according to Google Trends data.

These data product managers are responsible for identifying gaps in their internal user’s data experience and bridging them by working with the data and analytics team. They drive the prioritization of projects and overall vision to develop an organization’s internal capacity to effectively operationalize data.

It’s an essential role according to Wendy Turner-Williams, former Chief Data Officer at Tableau. A good product needs a good product manager.

“From my perspective, a data product manager is actually one of the first roles I would hire for. I like them to actually create the vision and then drive the engineers to that vision,” said Wendy during a recent conversation. “For me, it is a critical component as I tend to focus on data product managers who can create a story, engage with our internal customers or even our product team.”

In this article, we’ll walk through:

What is a data product manager? How did the role evolve?

In the early 2000s, companies like LinkedIn, Netflix, and Uber had a problem. Teams across the organization were working with data, and lots of it, at scale.

Data was powering their product roadmap, fueling executive-level decision making, and informing their paid marketing campaigns.

Internal and external data was flowing in and out of the company. There were regulations, guidelines, and restrictions for how this data could be used and by whom. But nobody was in charge of developing data solutions to make analytics operational, scalable, and accessible.

As a result, the product data manager role was created to answer questions like:

  • What data exists?
  • Who needs this data?
  • Where is this data flowing to/from? In other words what is the data lineage?
  • What purpose does this data serve?
  • Is there a way to make it easier to work with/access this data?
  • Is this data compliant and/or actionable?
  • How can we make data useful to more people at the company, faster?

What is a data product?

Of course, you can’t have a data product manager without a data product-or several for that matter.

But defining a data product is surprisingly difficult. The truth is, many things can be considered a data product, from a Looker dashboard or Tableau report, to an A/B testing platform or even a multi-layered data platform.

Eric Weber, the Head of Data Experimentation at Yelp, suggests, “talking about data products in a generic way can produce generic results. Data product is a useful idea, but to make it really create value, we have to get into the specifics….”

So let’s get specific. Regardless of what data the product visualizes / crunches / puts to work, there are specific outcomes it should deliver:

  • Increased data accessibility (surface data where people need it when they need it)
  • Increased data democratization (make it easier for people to manipulate the data)
  • Faster ROI on data (quicker insights)
  • Time savings for the data team / data consumers
  • More precise insights (i.e., experimentation platforms)

Similarly, there are important characteristics or qualities a data product should have.

  • Reliability and Observability. Acceptable downtime for a SaaS product is a discussion of “how many 9s?” As in 99.9% or 99.999% availability. Just as software engineers use products such as Datadog or New Relic to track SaaS product performance, data product managers need solutions to identify and solve data product performance issues in near real-time.
  • Scalability. The data product should scale elasticity as the organization and demand grows.
  • Extensibility. While the data product has likely been built from an integration of different solutions, it needs to maintain the ability to easily integrate with APIs and be versatile enough to be ingested in all the different ways end users like to consume data.
  • Usability. Great SaaS products focus on providing a great user experience. They are easy to learn, fun to use, and quick to get work done.
  • Security and Compliance. Data leaks are costly and painful, as are regulatory fines.
  • Release Discipline and Roadmap. SaaS products continually evolve and improve. Roadmaps are built at least a year into the future with a strong quality assurance process for updates.

What does a data product manager do? What skills do they need?

A data product manager is responsible for data democratization and increasing the time to value for the data itself. They design, build and manage the cross-functional development of a data platform, or a suite of specific data tools, to serve multiple customers.

For example, Atul defined the product strategy and direction for Uber’s data analytics, data knowledge, and data science platforms. In his role, he led a project to improve the organization’s data science workbench that was utilized by data scientists to make it easier to collaborate.

Data scientists were currently automating the process of validating and verifying worker documents that were required when applying to join the Uber platform. This was a great project for machine and deep learning, but the problem was data scientists would routinely hit limits of the available compute.

Whereas a traditional engineering project lead may have tried to add more virtual machines or extend the project timeline, Atul researched multiple solutions and identified virtual GPUs (then an emerging technology) as a possible solution.

While there was a high price tag, Atul justified the expenditure with leadership. The project was not only going to save the company millions, but supported a key competitive differentiator.

This proactive approach allowed Uber to start building the foundation they would need to leverage GPUs immediately upon availability. Time to value was greatly accelerated-a hallmark of a good data product manager.

What background do data product managers need? Who do they report to?

While you don’t need to write code, this is a difficult job to do without technical training. This is a role that requires understanding complex systems and working with very technical colleagues.


Interested in what the future will bring? Download our 2023 Technology Trends eBook for free.

Consent

It can also be helpful if the candidate has experience talking to customers. This can indicate they are skilled in translating requirements and telling stories to diverse audiences.

Common data product manager backgrounds include:

  • Back-end engineering (managers or strong engineers that want to set a vision)
  • Traditional B2B product management
  • Internal tooling product management
  • Data analysts

How much does a data product manager make

How much does a data product manager make? The average salary for a data product manager is $112,704 according to Glassdoor.

Some data product managers are beholden to data analysts and data scientists. Others work with operations teams, software engineers, or in the case of larger companies, executives.

However the reporting is structured, the data product manager makes it easier for data consumers to understand and democratize not so much the data itself, but the insights gleaned from that data.

Data product manager vs. product manager

Working with data involves a skill set unique to most forms of product management.

Data Product Manager and Other Data Personas

There are a variety of data personas you have to consider when you’re building a data platform for your company: engineers, data scientists, product managers, business function users, and general managers. (Image courtesy of Atul Gupte)

Instead of working with traditional customers, you’re working with data consumers. These are employees using products that make sense of your company’s data, whether that’s internally derived, third-party, or otherwise.

In other words, the data product manager is a product manager role solely dedicated to building internal data tooling or the data product that serves internal data consumers.

Data product manager vs. data scientist

The main difference between these two roles is data scientists are trying to glean insights within an existing product or solution. For example, “why is a user not signing up?”

On the other hand, a data product manager works to empower engineers, business stakeholders, and executive leadership by discovering, “what is the best outcome for this data and how do we get there?”

For example, Uber collects data every time a user takes a trip. The data scientist would be able to help predict price points for when a user might complain or jump to another rideshare app as well as reasons for why the price was so high.

The data project manager would be focused on what else can be done with the data, what other data it can be combined with, how to ensure the data is reliable, if the machine learning models are adequate, and more.

The future of the data product manager

Data teams are becoming increasingly decentralized and splintered – there are more roles emerging, from data governance managers to analytics engineers.

At the same time, the distance between data producers and data users is growing and demand is increasing exponentially. This is due in part to the growing reliance on data across all parts of an organization.

The future of the data product manager will very much resemble the traditional product manager: a conductor that spans silos and inspires teams to play in harmony.

Signs You Need A Data Product Manager

10 signs you need a data product manager.

They will be the critical connection point between data team members, data consumers, and product builders. They will bridge the divide between data product and data as a service. They will identify the needs of users, monitor developments, evangelize a vision, coordinate stakeholders, and prioritize projects.

As a result, the organization will move from a reactive posture of fighting data fires to a proactive stance of building internal data capabilities as a competitive advantage.

Progressive data product managers will critically examine the qualities of a good data product and set their own metrics (we have some suggestions for downtime and data quality).

Data product user satisfaction will be surveyed, downtime measured, and release processes documented. It will all be tied back to business value and evangelized across the company.

And that is an exciting future for any data professional indeed.

Categories: Big Data
Tags: Big Data, big data engineer, big data quality, Product Management
Credit: www.montecarlodata.com

About Barr Moses

CEO and Co-Founder of Monte Carlo Data. Lover of data observability and action movies. #datadowntime

Primary Sidebar

E-mail Newsletter

Sign up to receive email updates daily and to hear what's going on with us!

Publish
AN Article
Submit
a press release
List
AN Event
Create
A Job Post

Related Articles

How data and modern machine learning can help TSA keep us safe

March 20, 2023 By fahmidkabir737

Exploring the Legal Implications of Generative AI: Is it Fair Use?

March 20, 2023 By Bill Franks

Optimizing Traditional Agricultural Practices with AI

March 20, 2023 By Roger Brown

Related Jobs

  • Software Engineer | South Yorkshire, GB - February 07, 2023
  • Software Engineer with C# .net Investment House | London, GB - February 07, 2023
  • Senior Java Developer | London, GB - February 07, 2023
  • Software Engineer – Growing Digital Media Company | London, GB - February 07, 2023
  • LBG Returners – Senior Data Analyst | Chester Moor, GB - February 07, 2023
More Jobs

Tags

AI Amazon analysis analytics application applications Artificial Intelligence benefits BI Big Data business China Cloud Companies company costs crypto Data design development digital engineer environment experience future government Group health information learning machine learning mobile news public research security services share skills social social media software solutions strategy technology

Related Events

  • 6th Middle East Banking AI & Analytics Summit 2023 | Riyadh, Saudi Arabia - May 10, 2023
  • Data Science Salon NYC: AI & Machine Learning in Finance & Technology | The Theater Center - December 7, 2022
  • Big Data LDN 2023 | Olympia London - September 20, 2023
More events

Related Online Courses

  • Business Innovation and Digital Disruption
  • Velocity Data and Analytics Summit, UAE
  • Webinar: Large Language Models – Balancing Opportunities & Challenges
More courses

Footer


Datafloq is the one-stop source for big data, blockchain and artificial intelligence. We offer information, insights and opportunities to drive innovation with emerging technologies.

  • Facebook
  • LinkedIn
  • RSS
  • Twitter

Recent

  • How data and modern machine learning can help TSA keep us safe
  • Exploring the Legal Implications of Generative AI: Is it Fair Use?
  • How Data Analytics is Revolutionizing Talent Acquisition Leadership
  • Storing the World in a Sugar Cube: The DNA Data Revolution Unfolds
  • Optimizing Traditional Agricultural Practices with AI

Search

Tags

AI Amazon analysis analytics application applications Artificial Intelligence benefits BI Big Data business China Cloud Companies company costs crypto Data design development digital engineer environment experience future government Group health information learning machine learning mobile news public research security services share skills social social media software solutions strategy technology

Copyright © 2023 Datafloq
HTML Sitemap| Privacy| Terms| Cookies

  • Facebook
  • Twitter
  • LinkedIn
  • WhatsApp

In order to optimize the website and to continuously improve Datafloq, we use cookies. For more information click here.

settings

Dear visitor,
Thank you for visiting Datafloq. If you find our content interesting, please subscribe to our weekly newsletter:

Did you know that you can publish job posts for free on Datafloq? You can start immediately and find the best candidates for free! Click here to get started.

Not Now Subscribe

Thanks for visiting Datafloq
If you enjoyed our content on emerging technologies, why not subscribe to our weekly newsletter to receive the latest news straight into your mailbox?

Subscribe

No thanks

Privacy Overview

This website uses cookies so that we can provide you with the best user experience possible. Cookie information is stored in your browser and performs functions such as recognising you when you return to our website and helping our team to understand which sections of the website you find most interesting and useful.

Necessary Cookies

Strictly Necessary Cookie should be enabled at all times so that we can save your preferences for cookie settings.

If you disable this cookie, we will not be able to save your preferences. This means that every time you visit this website you will need to enable or disable cookies again.

Marketing cookies

This website uses Google Analytics to collect anonymous information such as the number of visitors to the site, and the most popular pages.

Keeping this cookie enabled helps us to improve our website.

Please enable Strictly Necessary Cookies first so that we can save your preferences!