July 25, 2023
The 2023 State of the CIO survey suggests operational efficiency is currently the number one priority for CIOs. It’s not surprising; service and other cost inflation has transformed the economic and budgetary landscape for technology leaders. Salaries, cloud fees, software fees have all increased. Finding efficiency in technology delivery has now become the priority. Whether you rely on in-house tech teams or outsourced vendors, or both, you have to ensure that you are maximizing value. The question then becomes, how do you do that? How to do more with less? And how do you do it rapidly? In this article we look at traditional transformational approaches to improving operational efficiency in technology delivery and why they don’t always work. And we make the case for a data-driven approach that allows leaders to leverage small gains for massive impact.
There are many solutions and approaches that promise to improve operational efficiency in technology delivery, from implementing new technology stacks to full-on organizational restructuring. Solutions like Agile, cloud and others are commonly touted as routes to operational efficiency.
As effective as they are, many of these solutions require significant investment, both in terms of time and money. This is problematic in the current environment when impact is required cost-effectively and at speed. What’s more, while they may promise great results, the reality can often be different. Many of these large-scale multi-year operational efficiency initiatives fail with devastating results. Resources are wasted, morale is hammered, and the reputation of the organization is damaged. For leaders, the toll of such failure can be immense, even career-ending.
But why do these such projects underdeliver or fail? Many times it is because an off-the-shelf solution is being applied to the wrong problem. These are one-size-fits-all consulting playbooks and not specific solutions. Solutions are based more on opinion and novelty bias rather than on hard data and evidence. These solutions often chase the latest trends without considering whether they are the right fit for your teams and organization. And, of course, if you get an Agile consultant, they’ll find Agile problems, and propose Agile solutions.
The truth of the matter is that every organization is unique. And every team is different. Technology delivery performance optimization needs to embrace this and find operational efficiencies in this context. In a way, it needs specific team-tailored playbooks that work together to deliver for a whole organization.
A better approach, we believe, is to base your operational efficiency strategy on data and metrics – not on playbooks or the latest trends. That is, understand the data and metrics of your own organization perofrmance to better understand your technology delivery issues. Look at your engineering metrics. Look at your release metrics. Look at your project metrics. Look at where you actually are actually investing your time. Benchmark and baseline these to understand what performance actually looks like in each of your teams. This will give you the ability to see what’s right in your teams and organization, what’s wrong, and where the opportunities are to improve operational efficiency.
What this kind of data gives you is the ability to see operational inefficiencies and their impact. It allows you to see how processes, tooling, or culture are impacting performance. It allows you to see what you need to improve for highest impact. And it’s not about big multi-year projects. What we find most extraordinary is seeing how one, two or three small changes at the team level can have massive impact. That’s the power that comes with data. That small things, at the team level, can have massive efficiency impact.
Let’s be clear, the data and metrics may show that your technology maturity is such that you require a significant investment. And that may be a multi-year investment after all. However what having the benchmark data also allows is that, in the meantime, you can do immediate things to deliver impact.
Indeed, what’s even more exciting is that the insight often allows teams themselves to see how to improve. They are able to see how to do more with less. And do it better. And faster. It’s empowering in a way that a consulting a deck seldom is. This is the power of achievable marginal gains over the one-size-fits all playbooks.
This data-driven approach is nothing new. Tech leaders in startups and product-first companies have long embraced data-driven approaches like this to optimize performance of their teams. Their ability to leverage engineering performance metrics, rapidly execute optimizations, and make massive impact is what makes the rest of us envious of them.
The lag in embracing this data-driven approach by tech leaders in industry is largely because of the inherent complexity of technology delivery larger organizations. Those organizations that try to do it themselves discover the many challenges of gathering and normalizing data across teams and vendors, finding relevant benchmarks, and having to learn how to read the metrics to get those insights that are going to deliver impact. It’s no surprise that the off-the-shelf playbook may appear more attractive.
There are also SaaS solutions like our own Implement.io that do all of it out of the box. Hundreds of teams and organizations have used our data and metrics to improve their operational efficiency. But again, for tech leaders in large complex organizations struggle to scale self-service SaaS solutions across teams and vendors. That’s why we created Implement Partners. To meet these specific challenges for tech leaders in industry.
We work with our clients as an on-demand technology delivery performance operations team. We leverage our platform, Implement.io, to gather the data and insight on your teams and organization. We then use our industry knowledge to build your unique performance model. And we then bring our technology performance expertise to help to identify where you can find operational efficiency. And we do this at speed.
The focus is on impact and value. The improvement to delivery is rapid. It can be about changing a workflow. Swapping out a tool. Improving team behaviors. But it’s the objective data and insight that inform the changes and the small tactical changes that deliver the results. It’s not unusual for individual teams to see productivity improvements of 10% to 15%, ticket throughput increase by 50%, and code quality improved by 70%. And this is in a matter of months.
As a technology leader, you want to make impact. You want to more with less. Successfully implementing a data-driven approach can transform the ability to do that and improve operational efficiency. And what’s more you’ll know you’re making decisions based on facts, not opinions. And you’ll be able to track the impact on your efforts. The path to operational efficiency is not about grand gestures or following the latest trends. It’s about making informed, data-driven decisions and focusing, where possible, on targeted, tactical improvements.
Small changes can make a big difference.
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