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The 2020s will be Guided by Architectural Data

Posted by Marbenz Antonio on September 20, 2022

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What technical and financial analytics from enterprise architects should CIOs and decision-makers anticipate in 2022?

An increase in applications and an acceleration of transformation are currently affecting businesses.

Just looking at the application landscape, we can see that according to industry studies, the typical business uses 500 to 1,295 custom applications in addition to cloud services. According to IDC, the global corporate applications market will reach $241 billion in 2020, expanding 4.1% over the previous year.

Enterprises’ foundational architectures, which are composed of interactions between people, processes, and technology as well as usually physical assets (IoT), are also expanding and changing quickly.

Enterprise architects use IT cost calculations, technical data, and lifecycle analytics to keep CIOs and business units informed.

In addition to projections to help with planning for future business scenarios and digital transformation projects, they usually gave expenses and technical indicators for the current IT ecosystem.

In previous years, common analyses might have included:

  • Counts of applications
  • The total cost of application ownership (also ROI and NPV)
  • Which procedures depend on a specific piece of software or equipment (dependency analysis)
  • How long will technology be supported, and when will the business need to change or upgrade?

Although this basic data is helpful, decision-makers might still wish for more analysis or greater detail.

Business units want to know how much enhanced technical metrics (uptime, responsiveness), or improvements to procedures that are crucial for successful customer journeys, will result from revised processes or apps.

Enterprise Architecture Data Analysis in 2022

Data-driven enterprise architecture may now give forecasts more precision and assurance. To create the necessary KPIs on demand, architects and business users must construct calculations that quantitatively aggregate data across the architecture.

Data scientists and numerate business analysts are familiar with using operations like Add, Subtract, Multiply, Divide, Min, Max, Average, and Count. Additionally, you can compute trends, and probabilities, attribute values and measure or predict the effects of business choices by using operations like Power, Log, and Atan.

EAs usually need to be prepared to produce reporting dashboards that incorporate the following in addition to diagrams and roadmaps:

Technology Cost Analysis:

  • Cost estimates for outdated technology that are available on demand
  • Using the links and interactions in the architecture, the total cost of a particular Business Capability or Process can be determined.
  • Costs of the underlying infrastructure or resources used, for example, when estimating software, support, or external service costs based on business function EAs can determine the overall cost of ownership for outdated technologies (available as a pre-built cost simulation in tools such as ABACUS from Avolution)
  • Costs of cloud migration
  • Metrics for technical debt like the cost of compliance, complexity, and remediation

Lifecycles & Trends in Metrics

  • Costs associated with applications and technology-related risks and vulnerabilities
  • Information summaries for the technology and vendor lifecycles, such as the number of years till technology is retired
  • Applications and technologies are analyzed for their business and technical fit in application portfolio assessments. For instance, do our applications use recognized technologies, and is the vendor still providing support for those technologies? (Resources like Technopedia can be used to get the fundamental data for this research.)
  • Technical KPIs such as Resource Utilizations, Availability, and Response Time
  • Trends in measurements like the rate of cost growth, the rate of increase in availability, or the rate of increase in reliability
  • Predictions based on machine learning, such as using lists of apps, lifecycles, financial information, and other architectural content For instance, ABACUS from Avolution’s machine learning engine offers a quantitative forecast of the values that should go in any vacant cells. Machine learning will offer a TIME (Tolerate-Invest-Migrate-Eliminate) proposal for an “empty cell” application, which the architects might decide to accept, in exchange for a more comprehensive dataset.

Adding KPIs to Diagram-based Enterprise Architecture

  • The future state and existing state are contrasted. Architects can use these and associated metrics to track transformation initiatives by using dashboard side-by-side comparisons of information or technical architectural designs and related catalogs.
  • Risks related to particular processes (security ratings and risk ratings can be rolled up from technologies and applications to the processes they support)
  • Connecting processes or tasks to capacities to compare tasks. For instance, architects can compute expenses or technical KPIs on processes as part of consolidation during a merger or acquisition to determine the efficiency of the two versions of the process.
  • Process dependence analysis: highlighting areas of dependency on outmoded technology utilizing diagrams and Graph Views to see relationships.
  • Displaying systems, interfaces, and APIs in process diagrams

Analysis by Architects of data from APIs

Architects can also provide stakeholder dashboards with data retrieved from CMBDs or other corporate data sources (through API queries). Compared to lists of facts, charts and interactive visualizations are usually clearer and simpler to understand.

  • Architects can use applications like Postman to do API inquiries.
  • Technopedia, a variety of CMDBs, and VMware products are examples of common API integrations.

 


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