What is Technical Debt?
Technical debt is the consequence of a digital project that is completed to meet an immediate need without taking the time to address other concerns. The project may be completed sooner, but there is a price to pay for taking shortcuts.
How do you measure technical debt?
In an ideal IT environment, there would be plenty of time to make sure every line of code is perfect before releasing a project. However, development cycles rarely have that luxury and racking up a certain amount of technical debt is both common and to be expected.
Teams can monitor and find what’s known as a technical debt ratio to manage development and control issues mindfully. A technical debt ratio can be expressed as the cost to repair a system divided by the cost of developing it in the first place, or Remediation Cost / Development Cost.
Measuring these costs can be difficult to do directly, since you’re looking at projections rather than actual costs incurred, but there are indirect means to approximate the debt. The following are a few good examples:
· Cycle time: Resolving bugs that crop up when accelerating project delivery over best practices takes additional time, delaying a project from moving on to the next development step. Shorter time required to accomplish a task indicates optimized processes.
· Stability: Rushing through a project can lead to code that’s not clean or well-organized and result in a backlog of tasks to stabilize and clean up a poorly designed system.
· Quality: Defects are a common occurrence when shortcuts are taken. As they add up, overall IT productivity declines.
HPE and technical debt
Overcoming technical debt doesn’t need to be painful. Teams can turn to an architecture that is secure, scalable, and flexible so they can refactor and make adjustments to code quality seamlessly and integrate feature additions efficiently.
For example, the industry’s first hybrid, analytics-ready data fabric, HPE Ezmeral Data Fabric, allows you to gradually modernize without refactoring or rebuilding. That’s because HPE Ezmeral Data Fabric doesn’t require developers to work from scratch. Instead, it connects distributed data across the enterprise, greatly simplifying application and workload access and eliminating the need to manually provide unique access to unique systems. This way, data integrity increases because errors and duplicate files are removed, which allows you to get more value from legacy big data estates.
If you’re already falling behind in addressing your technical debt, using HPE Ezmeral Unified Analytics to diagnose system and network occurrences of long cycle time, instability, and poor quality can help as well. By unlocking data faster from the cloud, HPE Ezmeral Unified Analytics modernizes legacy data and applications to optimize data-intensive workloads, no matter the deployment environment.
In addition, you can leverage a fully managed service that combines HPE Ezmeral Data Fabric with HPE GreenLake hardware, software, and services. HPE GreenLake for Data Fabric simplifies setup and configuration, offering simplified data analytics so your data engineers can bring back real-time and batch data from the edge to train models, run queries on metadata, or execute more complex queries to determine anomalies.