Novuz approach transforms modernization from a services model to a productized platform that systematically interprets and transforms workflows, data and metadata. The productization approach enables enterprises to codify their data and cloud related experience and expertise in software.
Service-Heavy, Labor-Intensive T&M Engagements create multiple leakages in cloud valuestacks
Enterprise cloud modernization has historically been delivered as a service offering. It's mostly custom, iterative projects and people-driven. The cost and pricing models have been predominantly T&M based. Hence, they are highly variable and difficult to compare and benchmark.
The as-a-service cloud modernization that most of the service providers currently focus on accumulates multiple inefficiencies. These leakage points are not easily fixable in the end-users' cloud estate and data landscape. Both assets are becoming increasingly dynamic and critical for enterprise AI success.
The leaky cauldron of as-a-service modernization includes three major inefficiencies:
* The industry's inefficiency is not due to complexity alone. It's due to how modernization is delivered. By productizing expertise, Novuz removes the fundamental constraint, human bandwidth and replaces it with software-driven scale, consistency, and speed.
Pivot to Cloud Modernization as a Product: Take ownership and control of your stacks the Novuz way
The Novuz approach transforms modernization from a services model to productized platform. It's a platform that systematically interprets and transforms workflows, data and metadata. The idea is to codify expertise in software. Productization enables three critical success levers of enterprise data and cloud modernization:
With the rapid maturity of AI and ML techstacks for knowledge and experience codifiability and automation, "Services as Software" is increasingly becoming the most technically feasible and cost-efficient way of cloud modernization.
The most well-known SaaS companies like ServiceNow are taking these opportunities into full stride and are even shifting their revenue and pricing models from per-seat basis to per-usage or consumption and value-based pricing models. They are becoming integrated end to end platform solutions for enterprises that can leverage intelligence across their workflows and workforce. The AI-ML use cases are maturing that way into self-augmenting and self-assuring business cases with deterministic ROI.
With the increasingly mature enterprise knowledge capture and codification opportunities unleashed by AI, complex programs like data and cloud modernization can exponentially scale in terms of lesser cost and higher speed and predictable accuracy and outcomes. These are highly skill-intensive and expensive resource-dependent programs that are often run in loops of recursive projects.
Every time there are major changes in the data or cloud stacks, it's yet another incremental leap of faith that the client organizations have to take. This T&M project approach creates great recursive revenue opportunities for service providers, but it keeps the enterprise clients in a perpetual loop of endless modernization projects.
The Productization opportunity breaks this ever-spiraling perpetual costs and efforts curve, by toolizing the cloud modernization work for enterprise employees and partners who know the data and cloud estates inside out.
This approach enables the enterprise tech leaders to be in full control, ownership and visibility into the entire modernization process, every time a new requirement arises. It transfers the tools ownership to the enterprise leaders and teams, with the product teams working as partners and training the internal members in LCNC enablement.
Hence, the key outcomes that manifestdirectly in a client's data and cloud operations landscape are:
What's in it for Enterprise tech leaders
This productized cloud modernization approach is not just a tool improvement. It's a delivery model shift, in the following ways:
It empowers the enterprise leaders and teams to gain full control and visibility into their data and cloud modernization project, while also transforming the recursive T&M based project costs into a product/ platform costing model.
The training and incremental engineering support from the platform/ product company like Novuz comes at a far less cost, due to extensive internal knowledge codification and enablement and the LCNC nature of the product.
What do you gain: Before vs After
