Technology Debt & Technology “Carbon Dating”
So about two years ago I was invited by a group of C-Suites to paint to them the future of technology and how that future would feed into their 10-year master plan. The purpose of the Master Plan was to enable the company to adopt a futuristic perspective spanning 2019–2029, review various thematic areas that would be crucial to their growth, and utilize the plan to develop a 5-year Strategic Plan.
I was excited and naturally swayed into the usual buzzwords, Artificial Intelligence, Machine Learning, Big Data, Blockchain, IoT. I was revving and ready to dazzle them with what tech-prophets before me have spoken so much about.
As I developed my speaking notes, contextualising the future of technology and the company’s realities, I kept wondering about its technology debt and tech issues that kept them awake. Technology debt is a concept commonly used in software development but I like to use it in the entire tech space (Technology, Process & People). In software development, it reflects the implied cost of additional rework caused by choosing an easy solution now instead of using a better approach that would take longer(Wikipedia).
I choose to use the phrase to describe the “elevation costs” required to correct easy/cheap/quick or just plain bad choices ranging from hiring, process design and technology stack. It follows that a bad hiring decision will probably lead to bad process design and poor choice of the technology stack. For example, most companies in the formative stages, due to budgetary constraints are inclined to start with entry-level techies, not a bad decision, however it most likely leads to a higher technology debt. As I reviewed the existing technology, I was shocked at the magnitude of the debt, technology had reacted to the company’s growth phases by layering on top of a shaky foundation. There had been no deliberate effort to repay some of these debts by rethinking the tech.
The sheer size of technology debt and business pain-points indicated to me that the company was struggling with outdated technology challenges. Whereas the company had invested heavily on certain aspects of its tech, the challenges it was struggling with felt lifted from a Gartner report of 2007. It was contradictory that the company was using some of the latest technology but struggling with old problems. I wondered the age the existing technology, at least from a business standpoint, and without a standard model for making such a conclusion, I went for a non-scientific, somewhat experimental model. For lack of a better phrase, termed it Technology “Carbon-Dating”.
Technology “Carbon-Dating” takes a company’s technology implementations and business problems on one side and maps those issues against global trends, recording the emergence of that technology or when such business issues were prevalent. For example, a company’s whose IT Disaster Recovery and availability implementation is purely based on offline disks can be dated 15–20yrs. On this aspect alone, a CIO’s struggle is to reduce the age to under 3 years. The cost of reducing the age of technology becomes technology debt. This particular company had a full-fledged disaster recovery site with the latest hardware however the DR site was configured on a passive mode. Configuration changes were never maintained across the different sites and it would take at least a day to kick in the DR site in the event of a disaster. This was against the lowest recovery time objective of 10minutes. Using the “Carbon-Dating” model, this issues was dated 8 years but with a minimal technology debt.
The greatest challenge for IT leaders is to maintain a viable technology debt. This can be achieved by continuous capacity audit in all aspects of technology including people and processes. It possible to deploy the latest tech but face outdated challenges. Defining Technology Debt and Age of Technology as a monitored KPI can further help an organisation achieve service excellence.
