i. Introduction
Introduction
Think back to the dawn of the consumer internet. An era that those of us who were around to experience it recalls memories of Netscape and the horrid buzzing of a dial up modem. The year was 1996.
Then there was “Web 2.0”, a phrase that sounds almost silly to say, now. This marked our transition from words and images on a “page” to pages with which we could more readily interact. The origins of the modern web app. The year was 2004.
The early to mid-aughts gave rise to the public cloud, to our long (and, unfortunately, still underway) transition to globally scalable platforms including Azure and AWS from the on-premises computing infrastructure upon which we had relied for decades. Let’s call it 2014.
Platform-first technologies like Power Platform began to emerge in 2019 (we called them “application platform as a service”, or “aPaaS”, back then), followed by generative artificial intelligence in 2022. We spent 2023 gasping for air. Catching our breath. Wondering how we might set ourselves up for the next wave. Notice the timeline.
We were working with eight to ten years between major disruptions from the dawn of the consumer internet. But these “wave periods”, that is, the time between the crest of two waves, have shortened to three to five years since the rise of the public cloud. It makes sense: As the evolution of computing technology and capacity picks up steam, it similarly accelerates.. Innovation begets innovation. Generative AI was only made possible by the incredible computing power and connectivity available in the cloud. Now, AI is further accelerating this pace of change, shortening the time we have available before new waves crash upon the shore.
The grace period for organizations to get their act together and position themselves for the next wave is growing much shorter; the margin for error is much narrower.
When I think about the non-technical barriers that so many organizations can’t seem to get over when it comes to platform-first, I really wonder how many will miss out on the AI wave because they lack the wisdom or the willpower to make the most of it.
Accelerating wave periods present challenges to even the nimblest of private sector organizations. It will absolutely wreck traditional public sector models that have hitherto been anchored in careful, deliberative decision making, multi-year budget cycles, and lengthy software implementations.
The ”cloud ecosystems” being developed by the organizations that are leaning into this transformation—which is to say, the organizations that will survive and thrive—rise to the occasion of these accelerative trends while solving significant problems faced by public sector organizations around the world. 3 CloudLight.house
Antiquated and disconnected systems lead to poor employee satisfaction, even worse citizen and constituent outcomes, and a persistent inability of organizations to extract value from their data even as their technical debt make them more expensive to maintain over time.
Monolithic “point” solutions such as ERP, HRS, and CRM: Costly, inflexible implementations that do not age well, nor can they be easily replaced because doing so risks toppling an agency’s entire IT tower, which in turn further drives up the cost of the alternative.
Incredible levels of risk incurred as agencies seek cheaper workarounds to their monolithic point solutions, often finding “solutions” such as the “SharePoint app” or the “Power App built with SharePoint as its data store” (because “the licenses were seemed free”) whose cost savings apparent cost savings have been seductive, but which have ultimately led organizations to expose massive amounts of their data stored in unsecure locations.
I’m going to linger on that last thought, because I cannot make this point enough. Organizations that have overbuilt using SharePoint or Excel spreadsheets as a data store have exposed themselves to perilous risk. SharePoint and Excel hydrate an organization’s Microsoft Graph with data. The Graph augments generative AI workloads such as Copilot. I implore you to not be the organization that learns this lesson the hard way.
Microsoft Graph is like a central hub that organizes and connects data from across Microsoft 365 services. It stores data from SharePoint lists, Excel files, Teams conversations, OneDrive, Outlook, and more, all in one place. Think of it as an interlinked productivity data network allowing you to pull data from different sources, like files, messages, or tasks, and use it seamlessly in your apps. It’s all about making Microsoft 365 data easy to find, use, and manage in one unified place.
Ecosystem-oriented architecture (EOA) presents a future-ready path forward, allowing organizations that embrace it to more readily absorb successive waves of technological change in artificial intelligence, data, and beyond. EOA calls on these organizations to move their monolithic point solutions from the center of their architecture to its outskirts, place data at the core of their cloud ecosystem, and adopt composable development approaches such that when one workload is added or removed, the rest of the ecosystem continues to function and evolve.
This white paper explores the principles, application, and real-world examples of ecosystem-oriented architecture through a public sector lens. I could have chosen to write generically, applying these principles broadly across many sectors, but I believed that the uniqueness of the public sector environment called for its own exploration of EOA.
Wave periods between major innovation in the cloud are growing shorter. We no longer have the luxury of waiting it out, of adopting later. Cloud ecosystems built on strategic foundations create the conditions to absorb successive waves of change.
Cheers,
Andrew Welch
Author | CTO | Founder
Cloud Lighthouse
Produced in partnership with Codec
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