Intelligent Computing Systems: How will Enterprise Architecture Evolve?

As I have discussed in prior blogs, the focus of enterprise computing for most of the 20th century was on deploying Systems of Record, first on mainframes, then minicomputers, then client-server systems. These were and continue to be the transaction processing backbones that drive global commerce. In the first fifteen years of this century, however, we have seen a profound shift in spending emphasis away from Systems of Record, which are now in maintenance mode, and toward Systems of Engagement, the focus being on connecting with customers, partners, and employees in digitally effective ways leveraging the ubiquity of smart phones. That movement has been inside the tornado for some time now such that, while there will be a lot of money spent here over the next ten years, I think it is time to look ahead to the next wave. That’s the one driven by big data, machine learning, the Internet of things, and artificial intelligence (for additional context, see a prior blog post “Machine Learning is From Mars, Artificial Intelligence is from Venus”).

In talking about this next wave with Qi Lu, formerly head of Office Applications at Microsoft, now Chief Operating Officer at Baidu, one of the key questions that came up is, how will real-world enterprises integrate these next-generation technologies into their established legacy systems? To help address this issue Qi proposed a high-level enterprise architecture that I have sketched out below. Its purpose is to call out the various zones of investment and to show how they can fit into a single coherent system. Here are the key points.

Systems of Record still are the anchor tenant in any enterprise information system architecture, but now they need to interoperate with Systems of Engagement so that businesses can leverage the digital capabilities consumers have become so accustomed to. This is no small feat as Systems of Engagement are designed to be accessible and Systems of Record need to be unhackable—how can each operate effectively and efficiently without holding the other back? The answer appears to be some version of a micro-services-based architecture running on top of an inter-system bus such that each type of system can connect with the other at arms’ length yet in real time. How one develops, deploys, and secures such an architecture is a work in progress, but the basic idea seems sound enough.

The next step, the one that initiates the next wave, comes from post-processing the log files from Systems of Engagement in combination with the data from Systems of Record in order to discover or detect actionable circumstances relevant to the enterprise’s mission. Today in most enterprises such post-processing work is confined within the IT function itself, focused on improving operations and security. That said, all the great business disrupters of the past decade have taken things far beyond IT’s walls. Amazon, Google, Microsoft, Apple, Tesla, Uber, Airbnb, Netflix—they are all running Systems of Observation against the data flows they are privileged to access or host, and then feeding them into Systems of Intelligence to extract insights from them. 

Today’s Systems of Intelligence are largely focused on machine learning, not artificial intelligence. Deep neural networks, in particular, are extracting complex patterns from highly unstructured data—notably video imagery and speech—which, when combined with transactional records allows disruptive companies to take their business performance to a whole other level. One of the key points in Qi’s diagram is that from a Dev/Ops point of view, it is important to separate Systems of Observation, more in the realm of the Internet of Things, from Systems of Intelligence, more in the realm of analytics, because each stresses the compute fabric in dramatically different ways. 

So now, instead of just two zones to manage, there are a total of four. Moreover, if any of these extracted insights are going to be used to optimize transactions, they have to be fed back into Systems of Engagement and Systems of Record. All this calls for a platform solution, one Qi has labeled a System of Operation. This is the point of insertion where the IT organization asserts its control over the interactions among the prior four systems. In this new order, although there is a natural flow of data and processing following the diagram from left to right, each of the four data processing systems must iterate and interoperate with the other three to learn, improve, and perform, all under a common set of protocols and governance rules.

Needless to say, there are boatloads of technology involved across this entire landscape that are far beyond my ability even to describe, much less prescribe. However, I expect that may also hold true for many enterprise CIOs who come to their position through a line-of-business as opposed to a technical route. The value of the Intelligent Computing Systems framework is that it helps a business-oriented generalist understand where investments need to be made and in what order. That is, without Systems of Engagement log files, there is little need for a System of Observation or a System of Intelligence—you can just use a data warehouse and traditional Business Intelligence analytics. With the advent of log files, on the other hand, you are now well and truly in the world of big data, which creates demand for the twin engines of a System of Observation and a System of Intelligence. Here you want to be careful to keep these two separate to optimize for performance, or you may end up getting surprised with a whopping bill from your favorite cloud computing provider. And finally, we all need to remember that machine learning itself has to be given time and space to learn so that ensuring it can iterate back and forth among the various systems critical to getting the training outcomes needed to make a material contribution to enterprise success.

All in all this is a lot to digest. For the time being, I hope that this framework can at least help you organize your meals.

That’s what I think. What do you think?

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