Make someone’s life easier with digital technology, capture the created value in a business model and improve your offer through the data insights generated. Digitalization is as simple as that.
In late summer 2016, I boarded a flight bound for Frankfurt. With the emotional complexities of an intercontinental relocation weighing heavily on my mind, it is worth reflecting on some of the things that I did not need to stress about. Checking in for the flight was an underwhelming notification on my phone. Getting to the airport was a tap or two on the Uber app. Presenting a boarding pass was a simple swipe. A notification upon boarding my connecting flight that my checked luggage had also made it provided much-needed reassurance that a month’s worth of necessities didn’t get lost in transit. It was remarkably unremarkable. And that’s brilliant.
Ultimately, digitalization is about leveraging technology to make life easier. It truly is as simple as that. Digitalization often gets distorted due to buzzwords and activities that are perceived to be cool, but that yield little utility – a fundamental requirement for enterprise adoption. I would argue that the key to transformation is clearly tying the concept of digitalization to value creation in a way that fully leverages the data set that is generated, which I refer to as pragmatically radical digitalization.
Pragmatically radical digitalization consists of three components: user experience (make someone’s life easier via technology), business model (capture the value generated), and predictive analytics (use the data generated to make the user experience better and the business model more lucrative).
It begins by focusing on the user – this could be an external customer or supplier or an internal employee. By focusing on a user and making that person’s life easier with technology, you create value. If you can extract that value, you create a business model. Fortunately, excellent user experience combined with a business model provides intrinsic sustainability, which over time generates lots of data. The ability to then use that data to make the user’s life even easier and the business model even more lucrative provides a robust framework within which enterprises can approach digitalization.
The painful process of ordering a taxi
Let’s take Uber as an example. I realize Uber is a fairly clichéd example when discussing digitalization and particularly digital disruption, but I am less interested in the fact that they do not own their cars. Instead, I want to highlight the fact that they started by simply making life easier for people with technology.
Prior to Uber, ordering a taxi was actually an unusually complicated process. First, you needed to find the phone number of a taxi service. Then you had to physically call that service, upon which you needed to tell them exactly where you were. At which point you waited. And hoped that at some point the taxi would arrive. Assuming you got picked up and made it to your destination (potentially arguing with the driver about the route along the way), you then needed to find some cash and determine how much you were going to tip. Transactions are always subconsciously difficult processes, which makes them particularly ripe for disruption.
This is an extremely painful process! Additionally, becoming a taxi driver is not a trivial process either! You need to go through training, obtain certification, get a car (either for yourself or by working for a taxi company), solicit rides and process payments.
Uber: How to simplify a painful process
Uber made both the riders’ and drivers’ lives easier. Simply opening the app allows the rider to hail a car to his or her precise location with precise transparency as to the location of the driver. Route is determined by computer, based on efficiency. Payment is automatic and invisible. Similarly, becoming a driver became much simpler, as long as you have a clean background check, a valid license, an appropriate car, and some free time.
Value was clearly added for both the rider and the driver. Uber then managed to capture value from the driver. After all, Uber fares are typically lower than traditional taxis’, so the rider not only has a simpler experience but also pays less overall; therefore it is the driver who is willing to give Uber a portion of his or her fare in return for a lower barrier to entry for employment and access to customers (and a potentially larger customer base due to increased demand from improved experience).
Use data to continuously improve
As the platform grew, the amount of data collected was staggering, and the insights gleaned from it were used to improve both the experience and the business model. Rating data could be used to vet drivers (and riders) and build trust in the system. Simple supply and demand data were used for surge-pricing models. New products were created, such as uberPOOL, which linked passengers with one another along similar routes.
While creating a novel, disruptive >$ 60 billion company may not seem entirely realistic for your current digital journey, let’s take another example on the complete opposite end of the digital spectrum – basic infrastructure. More specifically, a company’s internal Internet speed.
Think of all of the time office employees spend on their computers. As the majority of office applications become cloud based, network speed will become a fundamental limiter in employees’ ability to work. Therefore, increasing bandwidth will reduce the time spent waiting for pages to load and in turn make life easier. And the value of this is totally measurable.
Sometimes a pragmatic approach is all you need
The 10 largest German companies employ 3,687,123 people globally, giving us an average of 368,712 employees per company. Let’s assume half of those employees spend a significant amount of time on a computer. It is reasonable to assume that at a minimum they have 200 page loads / day (it is probably significantly higher in reality). If it takes six seconds/ page load, then each and every workday, employees at each of those ten companies are spending 3,687,120 minutes waiting for web pages to load! That is over seven years of wasted time each and every workday! Doubling the bandwidth and reducing load time would therefore save 1,843,560 minutes of wasted time/ day. By applying data science to web usage data, you can then proactively manage network connections to optimize load time and provide the fastest experience possible.
As you think about how you apply this to your own situation, it is crucial to spend time to understand what your customers and employees are actually doing. How are your customers interacting with your employees, your products and each other? How are your employees engaging with one another? This can be accomplished by conducting ethnographic research, which is the process of studying your target audiences in their natural environments. Translating those findings into clear pain points and possible technological solutions will then allow you to determine where tangible business value may lie. Finally, look into building internal data science capabilities, or partnering with data science companies, to determine how to use the data generated to further improve the experience and create even more value.
Digital does not need to be complicated. After all, the most radical transformations tend to be firmly rooted in pragmatism.