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Predicting Future IT Debt across Entire Enterprise IT Landscape

IP.com Disclosure Number: IPCOM000249350D
Publication Date: 2017-Feb-20
Document File: 5 page(s) / 307K

Publishing Venue

The IP.com Prior Art Database

Abstract

Disclosed is a system for predicting future information technology (IT) debt across an entire enterprise IT landscape. This solution comprises three core methods: to model the component life cycle of components that can be detected on an infrastructure, to calculate and predict a time-phased future IT debt, and to facilitate a transformational business change providing the ability to plan future IT investments to address ongoing IT debt requirements.

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Predicting Future IT Debt across Entire Enterprise IT Landscape

Today’s enterprise includes hundreds or even thousands of applications. The collections of applications that run an enterprise use a variety of hardware, operating systems, and middleware (MW) across many data centers, globally. Each of these components involves some independent, owned life cycle. Determining the overall impacts of even one end-of-life (EOL), end-of-service (EOS), or Out-of-Support (OOS) component can be extremely difficult. In particular, this is true when considering the aggregate enterprise situation; there is currently no known way to identify, let alone predict, aggregate application impacts and effort required to sustain application currency.

No known solution is available for predicting future IT debt related to IT assets/systems across an enterprise. Existing solutions are reactionary, simplistic, and have difficulty adhering to budgets. The complexity of manually predicting future IT debt related to one-to-n number of hardware/software (HW/SW) components is impractical if not nearly impossible. The variables and relationships between components are complicated; predictions cannot be reasonably done in a fast or reliable fashion without sophisticated instrumentation. Additionally, the number of assets shifting infrastructures, such as moving to Cloud based hosting where the application owners are not keenly aware of HW or middleware components, dramatically complicates the task.

The novel contribution is a system for predicting future IT debt across an entire enterprise IT landscape. This solution comprises three core methods, described below.

Method 1: Model the component life cycle of components that can be detected on an infrastructure (e.g., dedicated, cloud, logical partition (LPAR) or other). This includes the system’s abilities to model:

· HW, SW, or MW component life cycle · Drop dead implications for components · Vendor and custom (i.e., user defined) applications and related support

implications · Extended support impacts (i.e., costs) over time by component · Any subset of components that are important and modeled/analyzed in real-time

Method 2: Calculate and predict a time-phased future IT Debt (in real-time) whereby each asset and the various components on the environment for that asset (1 to n):

· Identify an inventoried number of Objects under an Asset that are impacted (or not impacted), thereby being able to see a relative magnitude of the debt as related to an asset

· Identify the distance of debt an object to look at the “how back-level” the organization is, which often represents a stacked risk of currency

· Identify the non-debt (i.e., good results) at an Asset level to reveal the IT debt as a ratio of total items impacting an asset

· Identify existing debt as well as future debt all within the same relational model

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· Put these items in grouped time buckets to support forecasting of the work required to remediate the issues

· Anal...