Are you in the top 2.5% of companies?
If not, your tech project is probably going to fail
Brace yourself, we’re about to talk about some truly unbelievable numbers.
A PwC study of over 10,640 projects found that only a tiny, tiny portion of companies — 2.5% to be exact — completed 100% of their projects successfully. The remaining 97.5% either failed to meet some of their original targets or missed the original budget or deadlines.
And it’s not just the time wasted or disappointed customers that are the problem here. These failures extract a heavy cost: failed tech projects alone cost the United States $50 — $150B in lost revenue and productivity.
What is happening here?
Are we simply kicking off projects blind and hoping for the best? Unlikely. In most cases what’s actually happening is that we’re misusing the data we have, or making poor decisions based on the data we do have and allowing both of those things to lead to project failure.
“If you watch close, history does nothing but repeat itself. What we call chaos is just patterns we haven’t recognized. What we call random is just patterns we can’t decipher.” — Chuck Palahniuk, the author of Fight Club wrote this in his satirical novel Survivor.
This quote makes a good point — that if we properly utilise data and recognise patterns that emerge from projects, we can significantly improve the performance of current and future projects.
Successful leaders simply use data and metrics
Ingrained in modern ways of thinking is the myth that successful people take gigantic risks, are the most innovative and, in return, earn big. In the book Great by Choice, Jim Collins paints a picture of organisations who performed 10 times better than their direct competitors. How? They actually didn’t take a whole lot of risk, but instead focused on a few core principles.
“Entrenched myth: Successful leaders in a turbulent world are bold, risk-seeking visionaries.
Contrary finding: The best leaders we studied did not have a visionary ability to predict the future. They observed what worked, figured out why it worked, and built upon proven foundations. They were not more risk taking, more bold, more visionary, and more creative than the comparisons. They were more disciplined, more empirical, and more paranoid.”
Namely, these principles include studying past performance, running small tests, figuring out what worked and why it worked, and then doing these things repeatedly.
There were no big gambles, huge overnight innovation or predicting of the future. There was simply figuring out what worked and doing these things consistently and frequently to achieve sustained success, way above the competition.
Why most companies aren’t learning and actually improving
One of the best examples of analysing experiences and using that data to constantly evolve is the AAR process in the US army.
An AAR (After Action Review) is a structured review or debrief process for analysing what happened, why it happened, and how it can be done better by the participants and those responsible for a project or event.
Sadly, even organisations that focus on learning still often repeat mistakes.
Why? When you look at the army’s AAR and similar “lessons learned” processes at different companies, the fundamentals are essentially the same at each. Following a project or event, team members gather to share insights and identify mistakes and successes. Their conclusions are expected to flow — either by formal or informal channels — to other teams and eventually combine into best practices and global standards.
Mostly though, that doesn’t happen. Although companies actively look for lessons, few learn them in a meaningful way. And that’s because in the corporate world, AAR’s are often an afterthought — done at the end of a large project — when mistakes are often forgotten. And even when they’re recorded, it’s usually in a report format that’s saved in a shared drive somewhere, never to be seen again.
A military AAR is simple. It’s a short, regular meeting between everyone involved in a project or event. It’s a judgement-free zone where everyone briefly mentions any mistakes or failures, lessons learned, and the information is quickly shared and used in the next project and event. When the learning is proven true, it becomes part of the team or organisation DNA and carried forward permanently. These AAR’s are not a report: they’re a short meeting that occurs all the time. And, as a result, the army rarely makes the same mistake twice.
Organisations are failing to become more data driven
In the 2019 Big Data and AI Executive Survey, 64 C-level executives from fortune 500 companies were asked how their organisations use big data and AI to transform their businesses. The results show:
- 72% of survey participants report that they have yet to forge a data culture
- 69% report that they have not created a data-driven organisation
- 53% state that they are not yet treating data as a business asset
- 52% admit that they are not competing on data and analytics
Similar to you I’m finding those results hard to digest. It really makes you wonder how they’re going to bridge the gap between where they are now and where companies such as Google, Apple and Facebook are.
In addition to the alarming stats above, the report also found that the percentage of firms identifying themselves as being data-driven has declined in each of the past 3 years: from 37.1% in 2017, to 32.4% in 2018 and 31.0% last year.
What this tells us is that there’s not only no strategy in using data, but there’s no understanding of how data could be used to improve our businesses.
How data can solve current problems and predict future ones
In the book Traction, Gino Wickman talks about building an organisational scorecard — made up of a series of key metrics that make sense for your organisation and product.
“The best leaders rely on a handful of metrics to help manage their businesses. This frees you from the quagmire of managing personalities, egos, subjective issues, emotions, and intangibles by focusing on metrics. You won’t have to suffer from the uneasy feeling of not quite knowing what’s going on in your business, nor will you have to waste time asking a half dozen people for the real story.”
Essentially you want to decide the right metrics that make sense for your organisation and project, make them easy to track on a regular basis, and monitor progress and make decisions based on these metrics to ensure success.
“By using a scorecard your leadership team will become more proactive at solving problems because you’ll have hard data that not only points out current problems — but also predicts future ones. By solving them, you’re assuring that you’re on track with your vision.”
Meaningful insights from scorecards and raw data
Settle in, it’s time for a success story from an unlikely corner: government.
For over a decade, the U.K government has been collecting and storing project performance data across certain parts of the sector. This data has been used to develop “The Green Book” — which in simple terms is a guide for the public sector on how to appraise and evaluate projects and programs of work to reduce the rate of failure. You can read the full version here.
We’re not going to talk about this guide in its entirety, but instead focus on a key insight: the variables that impact how projects are planned and executed.
Can you guess what the most recurring problem was? Human error. Not just in a “oops someone pushed the wrong button” form, but complex and difficult to diagnose forms like optimism bias and planning fallacy.
What the Green Book demonstrates is just how easy it is for human error to get in the way of an otherwise good idea. It shows that by using different data points as indicators, an impartial and unbiased perspective is given, limiting the scope of human error (you can’t argue with numbers).
The Green Book is a great attempt by the U.K government to learn from history by turning raw data into insights so we don’t repeat the same mistakes. I think it’s time we take that to 2020 and beyond.
The Green Book has proven that the data exists and we can turn this raw material into insights and lessons learned, to inform our forecasting so that we’re proactively making better decisions about our next steps.
In the future, AI will be able to take into account thousands of projects, their failures and success points, risks they faced, delays, budget overruns, and several other data points — to much more successfully predict when projects are likely to be delivered, and for how much.
In the meantime, it’s important we are making much better data-informed decisions. As leaders we need to:
- develop the right scorecards
- implement a learning culture
- regularly assess and review project success and failures
- and share these learnings so we’re not constantly making the same mistakes
In our own story, we imagined a world where our people were focused on solving complex human problems that machines couldn’t. In this world we’re constantly teaching the machine to do our job so we can move to solving higher value problems, serving society and humanity.
If this sounds like the kind of future you want to bring to life, but aren’t sure how to get there, we encourage you to join us on The Lab Book. There, we’ll be sharing what we’re learning and thinking about in this space, with lots of resources to keep you moving forward with us.
Hi, I’m Rob.
By day, I’m a General Manager at digital transformation consultancy Beaker & Flint, where I help organisations unlock their potential and get on the digital front foot. By night you can find me making a bbq or planning my next overseas adventure.
If this post sparked a question or idea you want to run by me, I’d love to chat it over with you. Reach out via the comments below or by via LinkedIn here.
Big thanks Mac Korasani for co-authoring this article
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