For CDOs to be successful today, they need to think more like a product manager.

After all, product managers are responsible for every facet of their product. They determine which customer needs their products fulfill and define what success looks like for their product. And they’re ultimately responsible for reporting that performance to executive leadership.

Hopefully those responsibilities sound familiar to listeners of the CDO Matters Podcast — except that for them, their core product is data.

In our latest episode, Malcolm is interviewed by Rishabh Dhingra on the Inspired Podcast, where Malcolm shares his perspectives on the growing trend toward treating data as a product. CDOs who are considering the addition of the product management mindset into their business will find his perspectives refreshing — given the great value that he believes product managers, and treating data as a product, can bring to most data-driven organizations.

Malcolm shares details on his expertise in the field of product management, having been a Product Manager, a Product Director and, ultimately, a Chief Product Officer (CPO). Having managed teams of product managers in several software companies through the heyday of the internet boom, Malcolm has first-hand experience working in highly agile and fast-paced environments — where quickly adapting to changing needs was a daily struggle.

As Malcolm describes it, the core DNA of a good product manager is all about problem-solving — where professional product managers are trained specifically to determine the optimal combination of product attributes to address customer needs given known constraints on time, money or resources. Product managers also know how to build business cases to support investments in their products; otherwise, businesses wouldn’t invest in them.

One of the biggest benefits of implementing more product management into data management is that they will provide the skills necessary to build business cases for data and analytics products — being a standard operating procedure in the world of product development.

When it comes to data as a product, Malcolm believes many data leaders are often missing the mark by incorrectly focusing efforts on defining products rather than customer needs. He explains that it ultimately doesn’t matter if a data product is a field, an attribute or an entire table — but what matters is if a customer need is solved.

The need for data people to take a “bottoms-up” approach to data products — where the product is a function of the available “raw materials” — is a major flaw in data organizations that product managers could help a CDO avoid since product managers are inherently focused on solving customer needs.

Why should companies consider managing data as a product? According to Malcolm, companies that deeply integrate product management practices into the field of data management — and who deeply embrace all aspects of data as a product — will drive competitive differentiation.

The benefits of integrating product management practices into data management are many, but his highlights include better business cases, resource prioritization, cost management and many others as just a small subset of the universe of benefits with more focus on data as a product.

By the end of this episode, current or aspiring CDOs who have not already considered the integration of product management practices into their data organizations — both for products and the supporting organization — should have a roadmap for implementing these PM practices into their data organization.

Key Moments

[1:15] Transitioning from Product Management into Data and Analytics

[7:06] Resolving Customer Problems with Customer Data

[12:30] Malcolm’s Role at Profisee

[15:40] Confusing Data Migration and Warehousing with Data Management

[19:20] MDM Implementation: Successes and Fails

[27:30] Why Product Managers Make Great Business Leaders

[29:40] Defining Data as a Product (DaaP)

[37:20] Applying Data as a Product Within Your Organization

[47:30] The Future of Data and Analytics

Key Takeaways

Malcolm’s Role as a Thought Leader and MDM Evangelist (12:40)

“Primarily, I’m focused on evangelism…it is my job to raise the awareness in the market of the importance of data and analytics, the importance of master data management (MDM). How MDM can drive value for organizations and how it can be used as a foundational element for digital transformation.” — Malcolm Hawker

Data Warehousing vs. Data Management/Governance (15:40)

“So many companies that if you just put all of the data in one place that you have solved for data quality. That you’ve solved for having a single source of truth. That you have solved for having consistent data governance. That couldn’t be farther from the truth. All you’ve done is put your data into one bucket. You may have limited the number of queries that you have to make or the number of sources that you have to go into. You may have made it a little easier to centralize permissions and access to that data…but putting it into one place doesn’t solve for that issue…I am all for using data warehouses and I am all for using cloud-based solutions for housing data, but if you don’t address some data quality issues, you’re going to have a lot of problems.” — Malcolm Hawker

Limiting Your Scope (23:45)

“Most data and analytics leaders are not building business cases. That means they struggle with scope. But product managers know you’ve only got time, people and money. If one of those has to go, then you have to limit your scope…If you don’t have a business case, then it’s really hard for you to limit your scope. It’s really hard for you to prioritize. It’s really hard for you to understand where the biggest benefits are going to be…you can’t differentiate whether A or B or C is going to drive value for the business. It inevitably leads to scope creep. It inevitably leads to situations where data and analytics leaders can’t justify the things that they’re doing.” — Malcolm Hawker

Bringing Product Management to the Data Space (33:42)

“If we could apply more product management into data management, data management would be a much better place. I would argue it would be far more customer-centric, it would be far more effective, it would be far more productive, we would be able to quantify the business benefits that we were driving, we would be able to prioritize our efforts, we would be able to spend money more efficiently, but what we do instead is we get into these arguments about, ‘What is a data product?’ Is it a field? Is it an attribute? And it’s not helping, because it’s backward. Start from the need.” — Malcolm Hawker

Where are Data and Analytics Headed? (47:35)

“In terms of the future, there are some things that we know are here and will continue to be here and continue to expand [into] what I would have called when I was at Gartner, augmented data management. What that means are the application of AI and [machine learning] and cool new technologies…to provide added layers of automation in the world of data management. I would put the creation of management of data fabrics in the bucket as well…with limited numbers of people, we need more and more automation in the data space.” — Malcolm Hawker

About Rishabh Dhingra

Rishabh Dhingra is the host of the Inspired podcast and is currently a Solutions Consultant in Business Analytics at Google. Having graduated from the Thapar Institute of Engineering & Technology in 2011, he serves as a veteran in the field with more than 11 years of experience architecting, designing and developing enterprise-scale business intelligence and analytics solutions for insurance, legal, banking and other industries.

EPISODE LINKS & RESOURCES:

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Check out the Inspired podcast

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