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Delivering Low-latency Analytics Products for Business Success

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On-prem data sources have the powerful advantage (for design, development, and deployment of enterprise analytics applications) of low-latency data delivery. Low-latency data delivery is a system level requirement that is tied to a critical business user requirement: low-latency analytics product delivery! You cannot have the second without the first.

In my early days as a data systems manager at NASA, I learned this distinction: business requirements specify what must be delivered to provide value to end-users; and system requirements specify how the proposed system will accomplish the business requirements. In my early years as a scientist (doing my own research on my own computer), I cared little about the “system” and more about the end-results. As I progressed in my career into management roles for enterprise data systems, I gained a deeper understanding and appreciation of the synergies and interdependencies between system and user requirements.

The criticality of these synergies becomes obvious when we recognize analytics as the products (the outputs and deliverables) of the data science and machine learning activities that are applied to enterprise data (the inputs). Low-latency data access and data delivery (system requirements) are necessary for low-latency delivery of analytics products (business user requirement).

Before you continue reading this article, you may wish to see some special categories of analytics products in this article: “Three Emerging Analytics Products Derived from Value-driven Data Innovation and Insights Discovery in the Enterprise.”

[continue reading the full article here]

Source: https://blog.purestorage.com/purely-technical/pure-e-family-technical/

AI Readiness is Not an Option

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This year, artificial intelligence (AI) has become a major conversation centerpiece at home, in the park, at the gym, at work, everywhere. This is not entirely due to or related to ChatGPT and LLMs (large language models), though those have been the main drivers. The AI conversations, especially in technical circles, have focused intensively on generative AI, the creation of written content, images, videos, marketing copy, software code, speeches, and countless other things. For a short introduction to generative AI, see my article “Generative AI – Chapter 1, Page 1”.

While there has been huge public interest in generative AI (specifically, ChatGPT) by individuals, there has been a transformative impact on organizations everywhere, both in strategy conversations and tactical deployments. Businesses and others are seeking to leverage generative AI to increase productivity (efficiencies and effectiveness) in nearly all aspects of their enterprise.

To support essential enterprise AI strategy conversations, here are 12 key points for organizations to consider within the context of “AI readiness is not an option, but an imperative”:

[continue reading the full article here]

Built for AI – https://purefla.sh/41oS2Dp

Editorial Review of “Building Industrial Digital Twins”

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https://amzn.to/3fl195P

I was asked by the publisher to provide an editorial review of the book “Building Industrial Digital Twins: Design, develop, and deploy digital twin solutions for real-world industries using Azure Digital Twins“, by Shyam Varan Nath and Pieter van Schalkwyk. For this, I received a complimentary copy of the book and no other compensation.

Let us begin…

This book is a very timely contribution to the world of industrial digital transformation. The digital twin is more than a data collector. It is an insight engine, providing not only data for descriptive and diagnostic analytics applications, but also providing essential data for predictive and prescriptive analytics applications. This is all fueled and facilitated by data flows across processes, products, and people’s activities, used in synergy with computational models and simulations of the system being digitally twinned. In order to help an organization get started with a DT (digital twin), this book outlines the process of building the MVT (Minimum Viable Twin). All phases of the MVT process are discussed: strategy, designs, pilot, implementation, test, validation, operations, and monitoring. 

This book knows and forcefully proves what is the enabler and value producer of digital anything (especially and most emphatically the DT) — it is all about the data and the simulation — that’s business modeling at its finest, incorporating the best of technology (physical assets, sensors, and cloud), techniques (analytics, algorithms, and modeling), and talent (culture, people, and strategic plans).

There were many themes and topics (both broad and specific) that fascinated me and kept me engaged in discovering serendipitous knowledge nuggets throughout this book. Here are a few: 

1) Azure DT, whose cloud-based PaaS (Platform-as-a-Service) provides a viable, scalable, and accessible launchpad for DTaaS in any organization.

2) Streaming sensor data from the IoT (Internet of Things) and IIoT (Industrial IoT) become the source for an IoC (Internet of Context), ultimately delivering Insights-aaS, Context-aaS, and Forecasting-aaS.

3) The consistent emphasis on and elaboration of key DT value propositions, requirements, and KPI tracking.

4) The DT Canvas (chapter 4)!

5) Helpful discussions of phased DT deployments, prototypes, pilots, feedback, and validation.

6) Specific Industry 4.0 examples, with constant reminders that’s it all about the data plus analytics!

7) Forward-looking DTs in the industrial enterprise.

Beyond being a technical how-to manual (though it is definitely that), this book delivers so much more! It is truly a business digital transformation manual.