We live in a digital world where experience delivery is as essential as a product or service, if not more. Hence, extending the same context of experience dimensions is only natural to build and consume the data and its by-products within the value chain.

We have become subconsciously programmed to embrace anything and everything that comes under the umbrella of digital interaction. And with absolute reason. Consumers or demand generators are true digital natives.  Hence, as consumers, we are comfortable as physical and digital interaction boundaries continue to blur. For example, digital commerce transactions have overtaken direct traditional sales transactions in the B2B space. Digital selling and buying experiences are becoming mainstream. Today’s buyers expect contextualized, insight-driven, educative, and informative/entertained sales and service conversations.  Sales is just one example of a value chain process portfolio.  In similar ways, transformations must happen for other process portfolios and their interconnected handshaking processes.  Data experience across persona, processes, and systems needs revisiting retrospectively to figure out the gaps to define the transformation journey map towards modern data experience.  

Foundation for a modern data experience is in treating data as the core asset to define and drive the purpose of a business. A comprehensive data strategy framework defining the value realization dimensions of data should be in place to traverse the journey of modern data experience.  Data strategy must align with actors/data consumers and their respective roles, cultural alignment, activities, programs, initiatives, governance framework and overall architecture to map a journey and its associated data and analytics embeddings. Suppose an enterprise has a well-defined data strategy. Then its modern data experience is evaluated from two complementary angles – experience delivery from the supply side and consumption expectations from the demand side. A true modern data experience will have the maximum impact at the intersection of both.   

Let’s analyze internal data consumers with respect to modern data experience. These consumers eventually deliver the data and data-driven interaction and service experience to end consumers/customers. The consumption dimension comprises expectations of data consumers (personas), data stewards (governing and managing data lifecycles), and processes that embed and consume the data insights.  A data consumer’s objective is to cut short the journey between a request for a dataset to its availability.  Time is money in the commoditized world, and hence, it is pretty vital to optimize the data and consumption velocity.  

In the sales process scenario discussed above, we addressed that the end consumer centricity context and the data coverage to deliver contextual insights, information, and education is critical for the journey towards modern data experience. Today’s buyer/customer (end-consumer) is conscious about what you are offering and is also curious about the what, why, how, who, when, and where. Hence, the modern data experience provisioning should also have the internal and external data curation to meet this 360-degree coverage of consumer expectations.

Data consumers would be looking at ready-to-consume data bundled as a unit package in a data marketplace shelf to cater to the expectations mentioned above.  Readily consumable datasets are called ‘Data as a Product’ in the modern data world. Data consumers would be expecting a metadata-based business data glossary visualized as a knowledge graph structure to understand the curated dataset’s purpose and context.  Meta profiling of the data consumer personas also would be an essential building block for the modern data experience to map suitable datasets for driving accelerated adoption.

From an experience delivery perspective, to deliver Data as a Product, a platform modeling of the data lifecycle is essential with clear I/O interface definitions for driving accelerated and seamless data onboarding and consumption process flow. Though the data platform should have the necessary capability of explainable DataOps with a white box view for the data stewards, it is crucial to abstract the Data as a Product consumer from the internal transformations of the data from inceptions to consumption.   Metadata profiling of the data consumer on the consumption front and the business metadata profiling of the data from the delivery front is crucial for achieving the intersection maximization for accelerated data adoption.  In the modern data experience landscape, the metadata of the data should have multiple tiers across systems, transactions/processes, and business contexts.  

The delivery dimension should focus on elevating the upstream and downstream coverage index of data for the end-customer centricity context. This alignment will help a business streamline its sales, marketing, service experience, transaction contexts, data to insights velocity, data coverage index, the ratio between concept modeling to insights cost, transformed outcome savings/profit, and so on.  The delivery dimension should also have the right data fabric to abstract the complexities and challenges of multi-cloud, hybrid data infrastructure ecosystems. While optimizing resource consumption, data fabric should ensure a seamless data and information exchange to enable an authentic modern data experience. The ready-to-consume expectation of the data consumer also mandates the delivery to adopt a true data mesh architecture that can ensure a domain-centric and distributed architecture approach to data curation.  

In the era where citizen data consumption personas are evolving and expanding in the enterprise landscape, it is important to have a platform approach for the data with consumption abilities to self-serve. A platform model should have a robust governance framework for federated data governance that comprises data security and access controls at the micro-level and allow for global regulatory mandates. Platform-centric approach for delivery should also ensure an intuitive user experience delivery in the form of a data marketplace. Such a platform should abstract all the engineering complexities associated with data flows, transformations, pipelines, and integrations at the data consumer level while providing business and system level context of data lineage.  Data consumers at the user interface level should have access to a detailed Data as a Product catalog that delivers a business glossary and the system level context of the data including the journey lineage of data from generation to consumption.  Data consumer interface should have the collaboration capabilities to collaboratively build datasets as products and consume them across interconnected process contexts.

You can get a modern data experience right by conceptualizing and constructing it more or less like how an e-Commerce platform is perceived and constructed.