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Data Subassemblies and Data Products Part 1: Modern Data Management Building Blocks

Digital Humanoid 3d Printed Robot Looking At New Modern World Thinking About World Domination Deep Learning Artificial Intelligence 3d Illustration
Digital Humanoid 3d Printed Robot Looking At New Modern World Thinking AbouDeep Learning Artificial Intelligence 3d Illustration

We are witnessing the transformation of Data Management into a modern-day Business Discipline. I’ve challenged the business and IT communities to reframe the data management conversation; to transform data management from an IT practice into a Business Discipline focused on leveraging data and advanced analytics (AI / ML) to derive and drive new sources of customer, product, service, and operational value within a continuously learning and adapting organization (Figure 1).


Figure 1: Accelerating the Data-driven Value Creation Flywheel

As a reminder:

A Discipline consists of systematic research, observation, measurement, and experimentation resulting in the assimilation of learnings into laws, theorems, concepts, principles, practices, frameworks, and formulas to enable the consistent application and on-going enhancements from the real-world application of that discipline.

I believe that there are two key modern data management “products” required to transition data management into a business discipline focused on helping organizations accelerate their data-driven business innovation. One of those “products” – Data Products – is already gaining wide acceptance as a way for organizations to monetize their customer, product, service, and operational insights or predicted behavioral and performance propensities. However, I want to introduce the concept of a second data management product – Data Subassemblies – which are the packaging of data and its supporting accoutrements to accelerate the development, operationalization, and scaling of Data Products (Figure 2):

  • Data Subassemblies (or Data-as-a-Product) are the packaging and pre-wiring of data and its supporting accoutrements (e.g., enriched metadata, data access methods, data governance policies and procedures, data access security protocols, data quality scores, privacy rules and regulations, usage patterns) into a single “package” with the objective to simplify data discovery, access and exploration to optimize the effectiveness and productivity of data workers (e.g., data engineers, data scientists, business analysts).  Data Subassemblies enable key data management outcomes including accelerating the discovery of relevant data sources, simplifying data access and data exploration, optimizing analytics experimentation and AI / ML modeling, augmenting feature engineering, and enhancing the development and validation of analytic (AI / ML) models.
  • Data Products (or Data Apps) are a packaging of AI / ML analytics and customer, product, service, and/or operational analytic insights (predicted behavioral and performance propensities) as an application to help end users (non-data workers) achieve specific business or operational outcomes. Data Products include the integration of data visualizations, Key Performance Indicators (KPIs) and composite metrics, data transformations and data enrichments, analytic (predictive) scores, intelligent data pipelines, ML features, ML models, and APIs. Data Products require comprehensive product management and DevOps capabilities including user interface, user experience, application development, operationalization, support, maintenance, and upgrade management.

Figure 2: Data Subassembly versus Data Product

These two modern data management “products” are critical in helping organizations become more effective at leveraging data and analytics to power their business and operational models. Let’s look at some real-world examples to make these important data management products come to life.

Understanding Data Subassemblies

Data Subassembly is the packaging of data and its supporting features and capabilities to optimize the productivity and effectiveness of data workers.

Data Subassemblies package data and its accouterments to help data craftspeople improve their data science and data engineering productivity and effectiveness. A great real-world example of the important role of subassemblies is in the automotive industry. In the automotive industry, sub-assembles are the pre-packaging and pre-wiring of related components to accelerate the building of the final automotive product (Figure 3).


Figure 3: Automotive Subassemblies

Automotive subassemblies increase production efficiencies and reduce production risks by reducing time-to-final-product, decreasing manufacturing risk, improving worker safety, reducing assembly failures, improving product quality and reliability, and decreasing costs associated with labor, manufacturing, procurement, inventory, logistics, maintenance, and support.  The main beneficiaries for subassemblies are the engineers and technicians who assemble and build the final automotive product.

In the case of Data Subassemblies, the beneficiaries are the data engineers, data scientists, and business analysts who are now trying to accelerate their ability to discover, explore, analyze, model, and visualize the data in the delivery of business and operational outcomes.

Understanding Data Products

Data Products are the packaging of domain-infused, AI/ML-powered apps designed to help non-technical users manage data and analytics-intensive operations to achieve specific, meaningful, and relevant business or operational outcomes.

Data Products deliver well-defined business or operational outcomes like improving customer retention, improving customer up/cross-sell effectiveness, reducing unplanned operational downtime, reducing inventory out-of-stocks, optimizing load balancing and asset utilization, improving marketing and sales effectiveness, and reducing unplanned hospital readmissions.

My favorite Data Product example is Uber.  The Uber data product packages passenger, driver, and traffic data and analytic insights to optimize my desired outcome of getting from where I am to where I want to be.  And it does it with a simple-to-understand, game-like user experience (UEX). 

There are many real-world examples of data products – end-user products that leverage data and advanced analytics to uncover and apply the analytic insights to deliver well-defined business or operational outcomes, including (Figure 4):

  • Google Nest Thermostat regulates the temperature of your house in response to your heating and cooling requirements – raising temperatures when you are home and lowering temperatures when you go out – to ultimately reduce energy usage and energy costs.
  • BeClose Elderly Care Monitor uses a range of sensors placed throughout the home to track an elderly person’s routines, enabling those living independently to continue doing so while still providing family and caregivers to monitor their wellbeing.
  • Babolat Play is a tennis racquet designed to improve performance by letting a player or coach set specific goals and chart progress using data analysis. The system adds a social element by making it easy to post gameplay data on social networks, challenge a friend in a side-by-side stats battle, or see how your numbers line up against the pros.
  • GreenIQ saves you time and resources by keeping plants fed based on their growing needs and conditions while automating much of the labor processes.
  • Echelon Smart Lighting allows a city to intelligently provide the right level of lighting needed by time of day, season, and weather conditions.
  • OnFarm combines real-time sensor data from soil moisture levels, weather forecasts, and pesticide usage to spot crop issues and remotely monitor all of the farm’s assets and resource usage levels.

Figure 4: Examples of Real-world Data and Analytics-infused Data Products

And what’s most exciting about the potential of Data Products is the ability to integrate AI / ML into the operations of these Data Products so that they can continuously learn and adapt, subsequently becoming more valuable the more the Data Products is used (i.e., becoming more predictable, more accurate, more relevant, more reliable, and more personalized).  That’s a topic that I’ll explore in a future blog.


I hope that I’ve made a strong case for the need for two important data management products:

  • Data Subassemblies are the packaging and pre-wiring of data and its supporting accouterments into a single “package” to accelerate data worker productivity and effectiveness.
  • Data Products are the packaging of domain-infused, AI/ML-powered apps designed to help non-technical users manage data and analytics-intensive operations to achieve specific, meaningful, and relevant business and operational outcomes.

In part 2 of this blog series, I will explore the economic aspects (of course) of Data Subassemblies and the role that Data Subassemblies and Data Products play in accelerating your Data Management Journey from one-off data projects to engineering composable, reusable data products.