WOOL: Today there’s a whole lot of supply of new products and ideas that are easy to buy and deploy. At the same time, there are giants like Facebook, Amazon, Google who are disrupting the market. It is both a great place to be in and a place where you cannot win against those giants. How do you think they will be the game changers for new technology adoption?
Geoffrey: One of the good things about the new cloud architecture is that it has got the mid-market, the small business market and the consumer into play. As long as there was any technology responsibility put on the end user side of the equation, there was always a gating factor as to how far you could deploy technology. But with cloud, and mobile particularly, that barrier is low. That is why the mid-market for software and service companies has grown faster than the enterprise market.
The amazing value proposition of mid-market is that you can get 80% of what a big corporation gets and yet, not have to go to a data centre. SaaS companies went first in the mid-market segment.
About platforms, that world has been levelled out, made possible by huge, global de-facto standards established by Amazon, Google, Facebook, and Apple. In every era, you need a level playing field to build on. Everything and everybody unconsciously self-organizes around market leaders and makes them the de-facto standard. Once that happens, it is difficult to compete directly against market leaders in that category.
WOOL: Your latest book "Zone to Win" oﬀers a practical framework to help large enterprises add a new line of business to their established portfolio. What kind of dynamics will come into play for companies wanting to balance “keeping the lights on” with bringing new digital innovations to market?
Geoffrey: In any business, there are three natural centres where capital and resources would be allocated. The first one is in the business that makes money. That's the performance problem. We have software systems that help a lot of people arrive at the core of things. The productivity is now all of the cost centre functions that you have to do in any business in order to support the performance. It is your back office functions, like HR, IT, legal, supply chain, customer support- anything that you don't charge the customer directly for but you need to do all your regulatory compliance to serve security and that is in the productivity sum.
Everybody in business understands that they have to fund the productivity sum in order to support the performance sum. Then there is the Incubation zone - the place where you need new and disruptive technology. I find those zones in every company I go to. They are probably spending 45% of their budget in the performance zone, 50% in productivity zone, and 5% or less in the incubation zone. What happens in most corporations is that the governance mechanisms default to funding performance zone first, productivity zone second, and the incubation zone the last. When the incubation zone says it needs funds to become the transformation zone but there’s no budget, it is literally asking the CEOs to step in to create the transformation zone and dramatically re-allocate resources in a way it hasn’t been done before. So, it is a big change.
Today there is enormous amounts of fairly accurate data, which is an actual behavioral log of people’s digital interactions.
Data is without a doubt a much better set of signal for decision making, but there is also a lot of noise around this signal. This is where machine learning is becoming so important.
WOOL: With data playing a critical role in customer acquisition and market segmentation; how can businesses sharpen their chasm strategy with data?
Geoffrey: In the 80s and 90s, there wasn’t a lot of data to use for decision making. It was a lot of intuition and trying, assessing and attempting to solve customer's problem. It was a very human-centric thing, and as you know, it didn't lend itself to scale particularly well. Today you have enormous amounts of fairly accurate data, which is the actual behavioral log of people’s digital interactions. The data is, without a doubt, a much better set of signals, but there is also a lot of noise around the signal. This is where machine learning is becoming so important. If I can filter the noise or capture the signals, then I can actually see segmentations from behaviors. I would really let the computer do the segmentation around the algorithm, rather than me. That I feel is very unnatural as I always try to humanize things with metaphors, and computers don't use metaphors. Computers use SAP theory and do it through a bottom-up granular analysis of data. Metaphors tend to work from a top-down of insight. Earlier, we couldn’t see and only react to it but now we actually have the opportunity to engage with it.
WOOL: With the consumerization of enterprise IT and adoption of Shadow IT, how do you think marketing tactics change?
Geoffrey: Oh, dramatically. This is the whole idea of a marketing tactic called "land and expand". Most enterprises allow a certain amount of shadow IT because it is the safety valve; it allows the group to move ahead of the IT department because IT departments have commitments and need to be somewhat conservative in being mindful of security obligations especially with global data processing regulations or GDPR. As IT departments move more methodically - and possibly slowly - shadow IT and individually configured IT are a good safety valve.
At some point, we will need to normalize what is going on because this approach risks creating the next generation of silos around different – and differing – views of customers. Aggregating various kinds of data streams and ingestion engines, pulling information from the edge will be important. Governance will be critical. The important question is at what point should governance kick in? When shadow IT becomes overpowering, it can repudiate the IT department, which is not healthy and nor a sign of good governance.