To keep up, forward-thinking companies in nearly every industry are investing in digital transformation initiatives and re-evaluating how they should be operating in a digital environment. The one factor that separates the success stories from the failures is the speed of their iteration cycles. The companies that succeed build excellence in collecting and acting on data with speed, so that they can iterate and make smarter decisions faster.
Gaining competency in a new capability
Historically, managers would commission large-scale research upfront, before investing in an initiative or project. They would design a study to evaluate a business opportunity, and it would take months to get data from a massive sample size. In simpler times, that may have worked, but consider what happens when companies do that today:
In mid 2017, a multinational bank used Alpha to test facial recognition for mobile app logins. The results were abysmal – users didn’t trust that facial recognition was secure, consistent, or convenient. So the bank rightfully scrapped the idea.
Just a few months later, Apple announced “Face ID,” a new technology coming to their flagship iPhone X device that would enable users to unlock their phones after sensors scanned their face. Of course, many people were and continue to be skeptical, but Apple is the most successful technology company in the world when it comes to driving behavior change. Needless to say, Face ID will soon become the new standard for securely logging into consumer devices.
A completely unpredictable and external factor made an idea suddenly great when, just months before, the same idea was terrible.
In one version of the world, the aforementioned bank exclusively conducts research before a project and makes a permanent decision about what they will invest in afterward. In a more forward-thinking reality, the bank continually tests assumptions – even the same assumptions – to track consumer preferences and make informed decisions throughout the product lifecycle based on the most relevant data. This is the fundamental difference that defines the digital age and how to win in it. You need to be able to experiment and iterate rapidly because if you don’t, you will fall behind whichever competitor does.
A recent article in The Harvard Business Review summarizes the necessity of this approach:
An alternative perspective on strategy and execution — one that we argue is more in tune with the nature of value creation in a world marked by volatility, uncertainty, complexity, and ambiguity (VUCA) — conceives of strategy as a hypothesis rather than a plan. Like all hypotheses, it starts with situation assessment and analysis — strategy’s classic tools. Also like all hypotheses, it must be tested through action. With this lens, encounters with customers provide data that is of ongoing interest to senior executives — vital inputs to dynamic strategy formulation. We call this approach “strategy as learning,” which contrasts sharply with the view of strategy as a stable, analytically rigorous plan for execution in the market.
It may not be a choice after all. “Strategy as learning” is perhaps the only option in today’s world.
Make smarter bets
Building successful products requires making difficult decisions about what users want. Continuous iteration is the best way to make those decisions. The more rapidly you iterate, the quicker you can learn and ultimately make better decisions.
The most effective digital transformation leaders don’t wait until they have the perfect idea or the perfect plan. They start running tests with potential users as soon as possible, and use what they learn to inform the next set of tests.
Amazon CEO, Jeff Bezos, shares why moving quickly is so important in his 2016 letter to shareholders:
“If you’re good at course correcting, being wrong may be less costly than you think, whereas being slow is going to be expensive for sure.”
Fortunately, large companies have the resources to take more shots, stomach the misses, learn, and continuously iterate. The most successful transformation leaders work to reduce the time it takes to go from question about what users want to an answer.
Empower decision makers closest to users
The people closest to users are usually the best suited to solve their problems. That’s why the companies achieving successful digital transformations have less centralized decision making structures. They empower teams to get the data they need and make decisions.
In the digital age, even people who don’t have the words “product” or “digital” in their titles need to learn what customers want and iterate accordingly. For example, a human resources manager needs to understand what motivates employees to join and stay with the company. Salespeople are always making small changes to their pitch to see what gets the best results.
Everyone in the organization needs to have the ability to contribute to digital transformation. If those people don’t have access to data, and the ability to act on it, they’re not going to be as effective. That’s why the most effective leaders provide education and tools to empower their teams to make better decisions.