How can you measure the value delivery of programs in the tech sector?

As a Tech Program Manager, I tackle the challenge of defining value by aligning clear objectives with stakeholders. We set measurable goals, tied to key performance indicators, ensuring a quantifiable measure of success. Continuous stakeholder feedback and a holistic view of both short-term deliverables and long-term impact guide the program's evolution. This approach ensures not just defined but consistently measured and optimized value delivery in the dynamic tech sector.

Defining program value is a pivotal challenge. In my experience, it requires aligning the perspectives of diverse stakeholders—customers, sponsors, and employees. Establish clarity by anchoring it to strategic objectives, customer needs, and anticipated benefits. A unified understanding ensures effective measurement of value delivery.

Revenue, Revenue, Revenue- What are the drivers of revenue? Price, volume, quality - and for software, expand to take rate, churn etc.

As a program manager, I define value as successfully delivering projects aligned to organizational objectives within budget, timeline, and quality targets. This enables data-driven assessments of value delivery.

value-Delivery involves everything to ensure every paying customer is a happy customer for the order processing, inventory management, delivery/fulfillment, troubleshooting, customer support.

As a Tech Program Manager, selecting metrics for value delivery is a critical challenge. I tackle this by aligning metrics with strategic goals, balancing quantitative and qualitative indicators. Key performance indicators evolve with the project lifecycle, adapting to changing needs. Collaborative stakeholder involvement ensures diverse perspectives. It's about not just having metrics but choosing the right ones that truly reflect value in the dynamic tech landscape.

Choosing metrics is crucial for measuring program value. In my experience, opt for SMART metrics—specific, measurable, achievable, relevant, and time-bound. Ensure alignment with the defined value, balancing across quality, scope, cost, time, risk, and benefit dimensions. Not all metrics are equally relevant; select indicators that effectively capture and communicate program value.

Relevant metrics I generally includes are project budget variance, schedule adherence, quality acceptance rates, and stakeholder satisfaction scores. These provide quantitative insights into efficiency, impact, and stakeholder experiences.

Defining success criterias for a project is crucial in the initiation stage of the project. It helps to guide the team to measure the success of the project. To get the right kind of KPIs to track success of the project is a task for the team as a whole. It needs to be achieved by discussing the goal and what we want to achieve with the team. Having a good mix of qualitative KPIs and Quantative KPIs, is also a good way to consider to get an effective success KPI on top of "SMART" .

Time-to-Market: Measure how quickly a product or feature is delivered from conception to launch. Cost Efficiency: Evaluate whether the program is delivering within budget and identify areas for cost optimization. Quality Metrics: Assess software quality through metrics like defect rates, system uptime, and customer-reported issues. User Adoption: Monitor user adoption rates and user satisfaction to gauge the program's success in meeting user needs.

Data collection for program value is complex. Plan a strategy ensuring data availability, accuracy, and validity. Address challenges like inconsistent formats and reliability. A robust approach is vital for evidencing program value.

I would gather data on the selected metrics through planning documents, status reports, quality testing records, and stakeholder surveys administered at project closure. Centralized collection allows analysis.

At the start of every project it is important to learn how to gather and sift through the data from coarse (high level) data to fine details that help create the value. e.g. someone wants a switch to reliably turn a light bulb on. So in this case reliably turning the bulb on is the key value. In addition what happens when this action takes place, such as, the clicking sound, tactile feel of the switch, location, and visibility of the switch are the actionable data elements that tells the engineers how to design and engineer the switch. So data collection has to be multi-level - coarse to fine.

Especially in my new role at Aareon, i discovered that there is no single true way. Data is generated in every organization trough various channels. The Challenge is to make the data visible to relevant stakeholders using the right tooling.

Analyzing program value data is complex due to volume, diversity, and interpretation challenges. Apply a flexible framework—select techniques, perform tasks, visualise results, and communicate findings for a comprehensive evaluation. Adaptability is key.

The Analysis of Data is nothing that is built into a person. It takes a structured learning approach to make everyone involved sensitive for looking at data and using the data to drive your decisions.

Trend analysis of aggregated metric data across projects would reveal performance patterns. I would correlate findings to identify root causes and opportunities for continual improvement towards optimized value delivery.

The most unpredictable element that affects most projects, especially the ones that take years to complete is that the world is changing and competition evolves with time. So if you find yourself in a situation where the competition and/or user preferences have moved away from what was defined as a value at the start of your project, follow the 'sunk cost' rule in finance - don't throw more money after sunk cost. Most of the time the situation may not allow you to rescope things easily e.g. tools are made, testing is complete etc. In these situations data collection can be helpful to decide how to steer the project based on the new value segmentation in the market.

I would present data-supported insights to leadership to inform strategy changes for enhancing processes, policies, and resource allocations to boost value delivery over time.

The hardest thing is measuring value when there’s no obvious monetary gain, like training, documentation, or improved DevOps procedures. For me as a developer, there are times I know we’re taking extra unnecessary time on deploying code, perhaps because we’re not auto-building CSS but rather committing it to our code, which causes merge conflicts for no gain of useful information in Git. But that’s complex, and all a project manager may see is an 8 hour unbillable task. So measure. Measure deployment times, estimate (accurately) the decrease by implementing, and show how long it should take before the improvement pays for itself. And if you can’t, maybe merging CSS isn’t so bad…

Conduct thorough post-implementation reviews to capture lessons learned, identify successes and challenges, and apply insights to future programs.

One thing I feel is important is to check the actual adoption level at the ground level and how soon is it taking for people to get onboarded on to the new system. The program will only add value once the end user starts to use and take advantage of the value add that was perceived.

Do not build features that are not validated beforehand. It is crititical to understand not only what to build but also HOW to build it. After implementation, it is fundamental to analyze the usage of a functionality to learn how to improve it.