Digital Systems Maturity Model
By Geoffrey Moore, Aug 17, 2017
Digital Transformation: A Stairway to Heaven
Every so often a phrase emerges from the Word Cloud to achieve capital importance, the sort of thing that authors and pundits can dine out on years to come (well, we do have to eat too, you know). At present that phrase is digital transformation. Even as you read this blog, board members around the world are quizzing their CEO about his or her “digital strategy,” and global systems integrators are lining up phalanxes of consultants to help bring these to fruition. There is a massive amount of cheese to move, everyone wants a piece of the action, and the claims that are being made are, if not outright lies, are what Huck Finn would have called stretchers.
Now it is not all twaddle—it never is. But if you can’t frame the challenge crisply, then you will be subject to whatever digital “flavor of the day” is currently in vogue, and you and your team will be struggling just to keep your heads above water. There has to be a better way. How do you get ahead of this sort of thing?
Back in the day some bright folks at Carnegie Mellon published a systems maturity model that helped a generation of IT professionals get a fix on their current state, target a desired future state, and map out the steps to get from A to B. It worked very well, and I think we need to take another page from their book. Here’s how I see the state of play in IT as it stands today:
This digital systems maturity model is presented as a stairway to heaven, that is, an escalating series of steps, each building on the one prior, each enabling the one subsequent. Each of these steps represents a considerable achievement as the following paragraphs will illustrate. The key questions are:
Where is your organization today?
Where does your company’s exposure to digital disruption require it to be tomorrow? and
What is it going to take to get you there?
Keep these three questions in mind as we look into each stair in the model.
1. Systems of Record
ERP and CRM systems provide a single view of the customer and streamline the quote to cash process.
This represents a major reorientation of the core systems of the enterprise. For the entirety of the 20th century, demand for goods and services substantially exceeded supply, so the focus of systems of record was on improving the supply chain’s effectiveness and efficiency. The rise of the Internet and the deployment of client-server ERP systems led to a wave of globalization that dramatically increased the supply of low-cost goods and services worldwide. As a result, at the beginning of this century the balance shifted such that today in most sectors of the economy supply substantially exceeds demand. This makes the customer the scarce ingredient in the economic equation.
Unfortunately, our systems of record are still organized around the products, and they make it very difficult to get a single view of the customer. Moreover, they are optimized for internal efficiency, not external effectiveness—hence all the quote-to-cash projects under way. So the first step to “getting digital” in 2017 is to retrofit your systems of record for a customer-centric operating model—no small feat, especially considering that you have to do it in parallel with the next step in the staircase.
2. Systems of Engagement:
Mobile applications and omni-channel communications improve customer experience, reduce time to transact, and eliminate disintermediation.
At the turn of the century, driven in part by anxiety about the potential for Y2K bugs to be lurking in older systems, enterprises across the globe spent heavily on upgrading their systems of record. This effort effectively pre-spent the next decade’s IT budget, the result being that in 2001 enterprise software sales went deep in the tank, the tech bubble popped, and attention shifted dramatically from B2B to B2C systems and applications. Thus we saw the rise of the great consumer tech giants of this century—Apple, Google, Facebook, and Amazon. Exploiting a digital business model as opposed to an industrial one (see prior blog post), they and their brethren drove dramatic cost reductions in cloud computing and mobile devices, extending the reach of software applications by several orders of magnitude. That, in turn, has created the defining B2B2C IT opportunity of this decade—namely, taking advantage of all this free infrastructure to reengineer enterprise relationships with customers, consumers, and clients across the board.
Every sector of the economy is now under pressure to modernize its systems of engagement to enable the kinds of digitally enabled customer experiences today’s users take for granted. This is not optional. You simply must modernize your operating model, and you must do so now. Thus, if your systems of record are woefully behind in their accommodation of customer-centricity, you now have a “two stair” challenge ahead of you. You have to get both your systems of record and your systems of engagement up to par before the end of this decade. And even then, you will still likely find yourself a bit behind those of your peers who have already done so and are now in position to take on the next stair.
3. Engagement Analytics:
Dashboards and reports extract insights from Systems of Engagement about customer preferences, market trends, systems inefficiencies, and user adoption.
At first glance this looks like good old Business Intelligence, but don’t let the terminology fool you. Traditional BI was conceived as a tool for extracting insights from systems of record. It’s idea of big data was Teradata, and its lingua franca was SQL applied to data in tables. This new world is all about extracting insights from unstructured data coming from the log files of systems of engagement as well as publicly available information on the Web. Forget Tera- (so last century!)—now we are talking about Peta-data, and we are more likely to be working in Python on data than Hadoop. There is a whole new stack to install and get up to speed on.
That said, this is still human-in-the-loop computing, where the scarce ingredient is human intelligence detecting patterns and inferring relationships. As a result, the impact of these efforts, regardless of how profound the insights may be, will unfold at a human-centric pace—weeks to analyze, more weeks to discuss, more to design and implement, and more still to deploy. The new BI makes us a lot smarter, but we need to get faster too, and that is the driver that leads to the next stair.
4. Systems of Intelligence:
Machine learning detects near-invisible correlations, infers causation, enables prediction, and proposes prescriptions, in order to optimize all types of interaction.
This is the current frontier for all but the most advanced enterprises. To reach this stair there are two key challenges to surmount. First you need to secure the data science expertise to work the algorithms, and then you need to get access to enormous amounts of data to feed the beast. Machine learning is only as smart as the amount of data it consumes. If there is not a giant river of data, then you are going to have to take the path of cognitive AI, synthesizing knowledge that is already documented, and at present that is neither as swift nor as scalable as its less cerebral brother.
But take heart. If you can get any purchase on this stair at all during this decade, you are likely to create substantial competitive advantage for your enterprise. This really is the bleeding edge for any industrial business model. It is not, however, the ultimate frontier reachable today. That title goes to the final stair in the model—the stand-alone digital business model.
5. Systems of Disruption:
Systems of Intelligence leverage proprietary insights to disrupt inefficient markets with novel digital services.
Digital-first business models really are systems of disruption. They commoditize whatever portion of the industrial model they are attacking—effectively they are willing to give it away for free, or close to it—in order to get proprietary access to time-sensitive data that contains signals that can be acted upon preemptively by their monetization engine. This is the business model of Google, Amazon, Netflix, Uber, and Airbnb, all enterprises created to be digital first, and wherever they have appeared to date, they have simply eviscerated the competition.
That said, the time is approaching for the Empire to Strike Back. Established industrial enterprises have had more than a decade to observe the workings of these amazing digital-first companies, to hire away some of their best experts to learn their secrets, and it is now possible—though hardly easy—for any enterprise to engage with this categorically new field of opportunity. It is not easy because it requires a new infrastructure model, a new operating model, and a new business model—a trifecta of new investments that run counter to, and are likely to be cannibalizing of, your existing infrastructure model, your existing operating model, and most damagingly, your existing business model. So here is the key—you must never try to digitally disrupt yourself. Deploy a system of disruption by all means, but not in your own back yard. Use digital at home to modernize your existing business model and fight off those digital disrupters attacking your installed base. Use digital as a way to disrupt someone else’s business model, creating carnage in other companies’ ecosystems, not yours.
The preceding is just a sketch of the digital disruption maturity model as it stands mid-way through 2017. It is intended to help you and your colleagues diagnose your enterprise’s current state and help you target your desired future state, all with the proviso of taking things one step at a time—no wishful get-out-jail-free leapfrogging. It is also intended to frame a deeper dive conversation about upcoming big bets in IT—when and why will you be making them. Digital disruption is forcing all enterprises to make big changes. The intent of this model is simply to reduce the thrashing involved to a minimum.
That’s what I think. What do you think?
This blog was originally published on LinkedIn Pulse
About the author
Geoffrey Moore is an author, speaker, and advisor who splits his consulting time between start-up companies in the Mohr Davidow portfolio and established high-tech enterprises, most recently including Salesforce, Microsoft, Intel, Box, Aruba, Cognizant, and Rackspace.
Moore’s life’s work has focused on the market dynamics surrounding disruptive innovations. His first book, Crossing the Chasm, focuses on the challenges start-up companies transitioning from early adopting to mainstream customers. It has sold more than a million copies, and its third edition has been revised such that the majority of its examples and case studies reference companies come to prominence from the past decade. Moore’s most recent work, Escape Velocity, addresses the challenge large enterprises face when they seek to add a new line of business to their established portfolio. It has been the basis of much of his recent consulting. Irish by heritage, Moore has yet to meet a microphone he didn’t like and gives between 50 and 80 speeches a year. One theme that has received a lot of attention recently is the transition in enterprise IT investment focus from Systems of Record to Systems of Engagement. This is driving the deployment of a new cloud infrastructure to complement the legacy client-server stack, creating massive markets for a next generation of tech industry leaders.
Moore has a bachelors in American literature from Stanford University and a PhD in English literature from the University of Washington. After teaching English for four years at Olivet College, he came back to the Bay Area with his wife and family and began a career in high tech as a training specialist. Over time he transitioned first into sales and then into marketing, finally finding his niche in marketing consulting, working first at Regis McKenna Inc, then with the three firms he helped found: The Chasm Group, Chasm Institute, and TCG Advisors. Today he is chairman emeritus of all three.
Accelerate your organization’s journey to analytics maturity
Get the data sheet to learn how the Research & Advisory Network advances analytics capabilities and improves performance.