The Problem with Digital Strategy
Background
The noise is cacophonous — the number of organisations claiming that they are digital transforming or preparing to! But many of these claims are slight-of-hand (at worst) or mis-informed (at best). Many Chief Digital Officers don’t quite grasp the notion of digital transformation. And so I’m dedicating my 11th article on how organisations should go about their digital (transformation) strategy.
(I write a weekly article on bad thinking and bad practices in data analytics / data science which you can find here.)
Of late, there is a conflation of data literacy with digital literacy. Some organisations use these terms interchangeably, although they are, in fact, different. The reason for this ‘overlap’ stems from the human intuition that there is something quite similar about the two agendas — building data analytics capabilities and building digital capabilities; but they can’t quite put their fingers on it. I have often found it useful to go back to first principles when trying to understand and align strategic thinking and problem-solving. Let’s start from the ground floor on Digital Strategy.
Definition Is Important
You may have heard this before — there 3 are sequential definitions of what we mean when we use the term ‘digital’. Firstly, there is digitisation. Digitisation is the conversion of analog forms of data into digital formats. Examples would be the conversion of physical statements into e-statements or wet signatures into e-signatures. Digitisation is usually associated with cost reduction in storage and retrieval.
Only with digitisation in place can we then pursue the second definition which is digitalisation. Digitalisation is the re-engineering of existing workflows to remove ‘friction’ by leveraging data in digital formats. Examples would be straight-through-processing of applications or automated reconciliation in operations. Digitalisation is usually associated with business growth and efficiency.
In the final sequence we get digital transformation. This is about expanding or pivoting the business into new business models, leveraging new capabilities created through digitisation and digitalisation efforts. Examples would be Netflix pivoting from content streaming to content creation, and Amazon expanding into cloud services with AWS.
You can now appreciate that most businesses are, in fact, pursuing a digitalisation strategy rather than a digital transformation agenda. In fact, I would argue that the sweet spot for the majority of organisations in in unlocking growth and efficiency opportunities through digitalisation rather than wasting precious resources figuring out if there is an pivoting opportunity for digital transformation.
It’s also important to note that digitisation and digitalisation run in tandem through cycles. Even when you think you are done re-engineering your workflow processes, new digital formats continue to emerge (e.g. physical signature to wet signature to biometric signature) and these bring new digitalisation opportunities. Consider the current Gen AI trend where the digital format is now all about text, and how this can be used as the input for new workflow improvements.
Losing My Cognition
So how does Digital intersect with Data Analytics? I’ve not seen this point made in B-schools, but the more you digitalise, the more you lose organisation cognition. Let me explain. Digitalisation leads to the removal of the ‘man-in-the-middle’ as part of workflow re-engineering. It may not be the removal of the whole ‘man’, but at least some part of him. The ‘man-in-the-middle’ is more than just a processing terminal; he also plays the role of data collector. The ‘man-in-the-middle’ is continuously collecting data through observations and activities, and making adjustments accordingly or surfacing emerging concerns.
Consider the example of Amazon Go, a completely digitalised grocery store where you simply walk in, take the items you want, and walk out. No scanning and no paying at the automated cashier. The technology to automate this described workflow has existed for quite some time already, and Amazon could have easily implemented it. Instead, Amazon invested in facial and item recognition technology so that it could monitor the behaviours of the shoppers within the Amazon Go stores. It understood that if it had simply automated without investing in this technology, Amazon would not have been able to continuously improve its offering or learn anything new about shopper behaviours and experiences — something that floor assistants and cashiers (i.e. man-in-the-middle) could have provided for.
And this is the nature of the intersection between Digital and Data Analytics. Digitalisation requires us to be extremely thoughtful in building data instrumentation capabilities into the re-engineering initiatives; we need to collect data that would be lost through the elimination of the man-in-the-middle as well as new data that the man-in-the-middle could not systematically obtain before. And we need to know, up front, exactly how we intend to analyse and use these data. We need good data analysts and translators to bring data thinking into design thinking.
Digital Economy
This segues into the ultimate question that is being asked by many Chief Digital Officers — “What kinds of digital or digitalisation strategies can I pursue so that my organisation can have a greater shared of participation in the growing digital economy?” The answer is surprisingly simple: all economies are built on the tenet of exchange, and any digitalisation that either increases the speed or variety of goods and services to exchange should be the focus. It starts by identify the frictions in customer interactions, product creation and transactions processing, and working through the digital technologies that exist that can be leveraged for the re-engineering work. Design Thinking is essential. Data and Analytical thinking is critical. Don’t waste money investing in proprietary solutions.
Conclusion
Going back to first principles is critical to developing an effective digital strategy. Because digitalisation goes hand-in-hand with data analytics, it is important that we don’t rush headlong into this exercise and pursue wholesale enterprise-wide digitalisation without first understanding the cognitive loss and opportunity for new data collection that can lead to faster ways and more varied goods and services to exchange.
