What is the CDO’s Role in a Product-Led Economy?

What is the CDO’s Role in a Product-Led Economy?

“Every company will be a software company.”

You’ve probably heard this famous quote countless times —  the vision of companies running on software, powered by technology, providing value to their customers via technology-based products; all things digital.

Well, this vision has come to life. Every company is now a software company (some are native, born into a products world; some are immigrants). The product-led economy is not limited to cool tech giants such as Spotify and Uber. An increasing number of traditional enterprises are in various phases of transitioning into becoming product-led companies. They are adopting a “product way” of thinking, updating their operating model while adopting product-style methodologies and measuring the impact and the value they generate for the organizations. Inspired by how tech giants operate, these organizations strive to encapsulate their business models and competitive differentiators in the form of products.

Where does this shift toward a product-centric organization position the data and analytics function? What is data’s role in this new products-centered world? What does this mean to the CDO’s place in the organization?

I believe CDOs now face a critical crossroads that will dramatically affect their position in the organization in the coming years. They have an important choice to make and should make it now while the road still needs to be paved.

Scenario 1: Data & Analytics becomes an enabling and supporting function for other product teams.

In this scenario, Data & Analytics provides product teams with the data and analytics features required for their technology products. Data & Analytics empowers product teams to make more data-driven decisions concerning their product roadmap and prioritization, such as understanding customer behaviors, discovering underlying problems they can address (aka evidence-based discovery), measuring product progress, and proving data-based evidence of whether product ideas work. While this is of great value to product teams, this scenario also positions the Data & Analytics function as a “follower,” an enabler, and a service center for product requirements. In this scenario, CDOs are not the product owner but are empowering other product owners. CDOs that will not make an active decision will find themselves on this path by default.

This scenario is disappointing for the CDOs I talk to — especially those who have made a difference in how their organizations rely on data. CDOs have struggled to shift their data units from reactively responding to data requests to proactively looking for problems that data can solve and then coming up with the best ways to solve them. With this push toward a products-centered organization, are data departments doomed to be reduced back to a “service center,” basically taking orders from other product teams?

There is another possible scenario, depending on the path Data & Analytics leaders choose.

Scenario #2: Data IS the product.

If every company is a software company, will every company eventually be a data science company? Will algorithms become the core of the product (aka business model)?

In my opinion, it is a possibility that holds a significant opportunity for profound value creation by Data & Analytics in the coming years. This can put data and analytics at the heart of the business and become a true differentiator.

Some might say that in this scenario, Data & Analytics serves as an economic moat, protecting the organizations’ competitive differentiators and holding off the competition. Sounds a lot better than passively catering to service requests, right?

What are data products?

There are two primary products in the Data & Analytic world: Data Products (usually referred to as DaaP: Data as a Product) and Analytic Products.

Data Products or DaaP (Data as a Product): High-quality, easy-to-use domain-specific data set that can be applied to various business challenges. The data product’s clients include data scientists, engineers, analysts, or other data consumers in the organization.

A prevalent example for a data product —  a 360-degree view of customers (a “customer data product”) at one large bank has 60 use cases, and those applications generate $60 million in incremental revenue and eliminate $40 million in losses annually.

Other examples: Employee 360 view, Product catalog.

According to the Harvard Business Review (HBR), companies that treat data as a product can reduce the time to implement new use cases by up to 90% and reduce costs by 30%.

If this concept sounds familiar to you, you’ve probably heard Zhamak Dehghani’s definition of Data Mesh (data as a product being one of the four pillars):

 “Domain data teams must apply product thinking to the datasets they provide; considering their data assets as their products and the rest of the organization’s data scientists, ML and data engineers as their customers… In summary, “data as a product” is the result of applying product thinking into datasets, making sure they have a series of capabilities including discoverability, security, explorability, understandability, trustworthiness, etc.”
 (Source: HBR: A better way to put your data at work.)

Analytic Product: A data-driven product that uses data and analytics to facilitate an end goal. An analytic product’s clients include the end user, whether a client, an employee, a manager, etc.

Examples include:

  • Hyper personalized assistant
  • Recommendation engine
  • Robo advisor
  • Fraud detection
  • Next best action

AI/ML analytic products are a unique case product that should require special attention since they take us from a deterministic process to a probabilistic and unpredictable one. AI and ML products include the following characteristics: 

  • Uncertainty (schedule, accuracy, relevance)
  • Challenge to plan and estimate due to degraded performance over time
  • Opacity (models difficult to understand and explain)
  • Fairness issues
  • Difficult to sell to upper management.
  • Disrupt existing processes
  • Impact potential: very high

Adopting a product mindset can be extremely valuable for the data community. It deals with many of the traditional roadblocks CDOs have faced for years.

So, considering the crossroads CDOs now face, which road will they take? Data as a supportive, enabling function, empowering product teams? Or, data as THE product, initiating data/analytic products whose primary objective is to use data to provide value?

In my opinion, eventually, it should be BOTH. We must be there for the organization and enable product teams to be data-driven and evidence-based. At the same time, we must position the data function as a business function, actively searching for “problems” and value opportunities, helping people not just to “do what they do, better,” but to help them “do what they didn’t imagine possible.”

About the Author

Einat Shimoni is the EVP of Research at STKI, an industry analyst and research firm based in Israel.

Shimoni has been researching data, analytics, MarTech, and customer experience technologies for over 25 years, advising organizations in their journeys toward becoming data-driven and customer-centric.

She is considered a thought leader and was named one of the Top 100 Digital Influencers in Israel by the Israeli Digital Association. Shimoni is a keynote speaker at conferences and workshops, and an instructor at universities, colleges, and CXO (CIO, CDO) certification programs. She co-chairs the Israel CDO community.

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