Data as a Product
A recent article in Harvard Business Review (HBR), “A Better Way to Put Your Data to Work,” by Veeral Desai, Tim Fountaine, and Kayvaun Rowshankish (July-August 2022 edition) argues that in order to realize value from data, organizations need to treat data more like a product. The authors, from McKinsey, offer some specific examples of the challenges organizations face as well as recommendations for how to achieve business goals from data initiatives. McKinsey has also published a companion article on this topic with some additional visuals called “How to unlock the full value of data? Manage it like a product,” by the same authors (June 14, 2022).
A key insight from this work should interest anyone taking on major data upgrade initiatives and value realization strategies: “In our work we’ve seen that companies that treat data like a product can reduce the time it takes to implement it in new use cases by as much as 90%, decrease their total ownership (technology, development, and maintenance) costs by up to 30%, and reduce their risk and data governance burden. In the pages that follow we’ll describe what constitutes a data product and outline the best practices for building one.”
At CognitiveScale, our clients are always working on various data-centric initiatives in order to provide the key ingredient of any AI application or insight engine. We are helping them leverage our Enterprise AI Platform, Cortex, and key capabilities like our Profile-of-One component that helps build personalized data profiles of any entity, to address data challenges and drive value from data. Our Cortex AI platform and Profile-of-One capability enable our clients to make data a product for use across the enterprise in any application.
Challenges with Data Projects
In “A Better Way to Put Your Data to Work,” the authors discuss a couple of case study examples of data strategies that failed to deliver on their full potential. In one, a large, multinational banking organization used what they called the “big bang” approach “to build pipelines to extract all the data in its systems, clean it, and aggregate it in a data lake in the cloud, without taking much time up front to align its efforts with business use cases.” After 3 years, they found that they had neglected numerous end users of their data lake, and technology requirements excluded many potential data end users.
A second case study organization, a large North American bank, used what the authors refer to as a grassroots data strategy wherein different data end users developed bespoke or one-off solutions. While there were some modest successes, this approach resulted in “a messy snarl of customized data pipelines that couldn’t easily be repurposed. Every team had to start from scratch, which made digital transformation efforts painfully costly and slow.”
McKinsey’s Definition of “Data as a Product,” with Insight into Addressing Key Data Challenges
An answer to “What is a Data Product?” McKinsey argues: “A data product delivers a high-quality, ready-to-use set of data that people across an organization can easily access and apply to different business challenges.” We would add that data products include reusable data connectors and data prep capabilities - and the ability to consume and distribute declared, observed and inferred data.
They go on: “(a Data Product) might, for example, provide 360-degree views of customers, including all the details that a company’s business units and systems collect about them: online and in-store purchasing behavior, demographic information, payment methods, their interactions with customer service, and more...” Again, we would highlight the need for key capabilities like access to data across the enterprise - not just in Marketing and Sales use cases - as well as the ability to develop entity profiles that can include multiple personas (e.g. in health insurance, where we can manage customer, member, and patient data as part of one person’s profile).
“...Or it might provide 360-degree views of employees or a channel, like a bank’s branches. Another product might enable “digital twins,” using data to virtually replicate the operation of real-world assets or processes, such as critical pieces of machinery or an entire factory production line.” Similarly, CognitiveScale helps clients with “digital twin”-type profiles for many different entities: customers, businesses, service providers, or transactions like an insurance claim, for example.
CognitiveScale Solutions Enable “Data as a Product”
CognitiveScale clients are continually upgrading their data management strategies in an effort to support their technology and business goals related to value realization - whether this is from the data itself or for analytics and AI projects. We have clients that have used monolithic (“big bang”) or grassroots data strategies, and we are bringing to all of them unique capabilities specifically tailored to data management for use as prerequisites for AI initiatives. Whether they have one massive data lake, solutions like Customer Data Platforms (CDPs), or initiatives like Customer 360 (“C360”), our solutions are augmenting these data strategies as a key component to accelerate the development time and speed to value of their key initiatives and use cases that are dependent on data.
We have written on this topic in more detail in a recent white paper, Enhance Customer Data Initiatives With AI-Powered Personalization, where we elaborate on how we are helping clients accelerate their customer data strategies here.
To learn more about CognitiveScale and how we are helping clients drive value from their data strategies and enhancing “data as a product” initiatives, please get in touch.