Enterprise governance has to face combined changes in the way business times and spaces are to be taken into account. On one hand social networks put well-thought-out market segments and well planned campaigns at the mercy of consumers’ weekly whims. On the other hand traditional fences between environments and IT systems are crumbling under combined markets and technological waves.
To overcome these challenges enterprises strategies should focus on four pillars:
Data and Information: massive and continuous inflows of data calls for a seamless integration of data analytics (perception), information models (reasoning), and knowledge (decision-making).
Security & Confidentiality: new regulatory environments and costs of privacy breaches call for a clear distinction between data tied to identified individuals and information associated to designed categories.
Innovation: digital environments induces a new order of magnitude for the pace of technological change. Making opportunities from changes can only be achieved through collaboration mechanisms harnessing enterprise knowledge management to environments intakes.
An often overlooked benefit of artificial intelligence has been a renewed interest in seminal philosophical and cognitive topics; ontologies coming top of the list.
Yet that interest has often been led astray by misguided perspectives, in particular:
Universality: one-fits-all approaches are pointless if not self-defeating considering that ontologies are meant to target specific domains of concerns.
Implementation: the focus is usually put on representation schemes (commonly known as Resource Description Frameworks, or RDFs), instead of the nature of targeted knowledge and the associated cognitive capabilities.
Those misconceptions, often combined, may explain the limited practical inroads of ontologies. Conversely, they also point to ontologies’ wherewithal for enterprises immersed into boundless and fluctuating knowledge-driven business environments.
Ontologies as Assets
Whatever the name of the matter (data, information or knowledge), there isn’t much argument about its primacy for business competitiveness; insofar as enterprises are concerned knowledge is recognized as a key asset, as valuable if not more than financial ones, and should be managed accordingly. Pushing the comparison still further, data would be likened to liquidity, information to fixed income investment, and knowledge to capital ventures. To summarize, assets whatever their nature lose value when left asleep and bear fruits when kept awake; that’s doubly the case for data and information:
Digitized business flows accelerates data obsolescence and makes it continuous.
Shifting and porous enterprises boundaries and markets segments call for constant updates and adjustments of enterprise information models.
But assessing the business value of knowledge has always been a matter of intuition rather than accounting, even when it can be patented; and most of knowledge shapes up well beyond regulatory reach. Nonetheless, knowledge is not manna from heaven but the outcome of information processing, so assessing the capabilities of such processes could help.
Admittedly, traditional modeling methods are too stringent for that purpose, and looser schemes are needed to accommodate the open range of business contexts and concerns; as already expounded, that’s precisely what ontologies are meant to do, e.g:
Systems modeling, with a focus on integration, e.g Zachman Framework.
Classifications, with a focus on range, e.g Dewey Decimal System.
Conceptual models, with a focus on understanding, e.g legislation.
Knowledge management, with a focus on reasoning, e.g semantic web.
And ontologies can do more than bringing under a single roof the whole of enterprise knowledge representations: they can also be used to nurture and crossbreed symbolic assets and develop innovative ones.
Knowledge is best understood as information put to use; accounting rules may be disputed but there is no argument about the benefits of a canny combination of information, circumstances, and purpose. Nonetheless, assessing knowledge returns is hampered by the lack of traceability: if a part of knowledge is explicit and subject to symbolic representation, another is implicit and manifests itself only through actual behaviors. At philosophical level it’s the line drawn by Wittgenstein: “The limits of my language mean the limits of my world”; at technical level it’s AI’s two-lanes approach: symbolic rule-based engines vs non symbolic neural networks; at corporate level implicit knowledge is seen as some unaccounted for aspect of intangible assets when not simply blended into corporate culture. With knowledge becoming a primary success factor, a more reasoned approach of its processing is clearly needed.
To begin with, symbolic knowledge can be plied by logic, which, quoting Wittgenstein again, “takes care of itself; all we have to do is to look and see how it does it.” That would be true on two conditions:
Domains are to be well circumscribed.
A water-tight partition must be secured between the logic of representations and the semantics of domains.
That could be achieved with modular and specific ontologies built on a clear distinction between common representation syntax and specific domains semantics.
As for non-symbolic knowledge, its processing has for long been overshadowed by the preeminence of symbolic rule-based schemes, that is until neural networks got the edge and deep learning overturned the playground. In a few years’ time practically unlimited access to raw data and the exponential growth in computing power have opened the door to massive sources of unexplored knowledge which is paradoxically both directly relevant yet devoid of immediate meaning:
Relevance: mined raw data is supposed to reflect the geology and dynamics of targeted markets.
Meaning: the main value of that knowledge rests on its implicit nature; applying existing semantics would add little to existing knowledge.
Assuming that deep learning can transmute raw base metals into knowledge gold, enterprises would need to understand, assess, and improve the refining machinery. That could be done with ontological frames.
A Proof of Concept
Compared to tangible assets knowledge may appear as very elusive, yet, and contrary to intangible ones, knowledge is best understood as the outcome of processes that can be properly designed, assessed, and improved. And that can be achieved with profiled ontologies.
A clear-cut distinction between truth-preserving representation and domain specific semantics.
Profiled ontologies designed according to the nature of contents (concepts, documents, or artifacts), layers (environment, enterprise, systems, platforms), and contexts (institutional, professional, corporate, social.
That provides for a seamless integration of information processing, from data mining to knowledge management and decision making:
Data is first captured through aspects.
Categories are used to process data into information on one hand, design production systems on the other hand.
Concepts serve as bridges to knowledgeable information.
The OOAD (Observation, Orientation, Decision, Action) loop is a real-time decision-making paradigm developed in the sixties by Colonel John Boyd from his experience as fighter pilot and military strategist.
The relevancy of OODA for today’s operational decision-making comes from the seamless integration of IT systems with business operations and the resulting merits of agile development processes.
Business: End of Discrete Time-Frames
Business governance was used to be phased: analyze the market, select opportunities, build capabilities, launch operations. No more. With the melting of the fences between actual and symbolic realms, periodic transitional events have lost most of their relevancy. Deprived of discrete and robust time-frames, the weaving of observed facts with business plans has to be managed on the fly. Success now comes from continuous readiness, quicker tempo, and the ability to operate inside adversaries’ time-scales, for defense (force competitor out of favorable position) as well as offense (get a competitive edge). Hence the reference to dogfights.
Dogfights & Agile Primacy
John Boyd train of thoughts started with the observation that, despite the apparent superiority of the soviet Mig 15 on US F-86 during the Korea war, US fighters stood their ground. From that factual observation it took Boyd’s comprehensive engineering work to demonstrate that as far as dogfights were concerned fast transients between maneuvers (aka agility) was more important than technical capabilities. Pushed up Pentagon’s reluctant ladders by Boyd’s sturdy determination, that conclusion have had wide-ranging consequences in the design of USAF fighters and pilots formation for the following generations. Its influence also spread to management, even if theories’ turnover is much faster there, and shelf-life much shorter.
Nowadays, with the accelerated integration of business processes with IT systems, agility is making a comeback from the software engineering corner. Reflecting business and IT convergence, principles like iterative development, just-in-time delivery, and lean processes, all epitomized by the agile software development model, are progressively mingling into business practices with strong resemblances to dogfights; and the resemblances are not only symbolic.
IT Systems & Business Competition
While some similarities between dogfights and business competition may seem metaphorical, one critical aspect is all too real, namely the increasing importance of supporting machines, IT systems or fighter jets.
Basically, IT systems, like fighters’ electronics, are tasked to observe environments, analyse changes in relation to position and objectives, and support decision-making. But today’s systems go further with two qualitative leaps:
The seamless integration of physical and symbolic flows let systems manage some overlapping between supporting decisions and carrying out actions.
Due to their artificial intelligence capabilities, systems can learn on-the-job and improve their performances in real-time feedback loops.
When combined, these two trends have drastic impact on the way machines can support human activities in real-time competitive situations. More to the point, they bring new light on business agility.
As illustrated by the radical transformation of fighter cockpits, the merging of analog and digital flows leaves little room for human mediation: data must be processed into information and presented instantly along two critical dimensions, one for decision-making, the other for information life-cycle:
Man/Machine interfaces have to materialize the merging of actual and symbolic realms as to support just-in-time decision-making.
The replacement of phased selected updates of environment data by continuous changes in raw and massive data means that the status of information has to be incorporated with the information itself, yet without impairing decision-making.
Beyond obvious differences between dogfights and business competition, that double exigence is to characterize business agility:
Instant understanding of changes in business opportunities (Observation) .
Simultaneous assessment of the reliability and shelf-life of pertaining information with regard to current positions and operations (Orientation).
Weighting of options with regard to enterprise capabilities and broader objectives (Decision).
Carrying out of decisions within the relevant time-span (Action).
That understanding of business agility is to be compared with its development and architecture cousins. Yet it doesn’t seem to add much to data analytics and operational decision-making. That is until the concepts of observation and orientation are reassessed with regard to EA maps and territories.
Agility & Orientation: Task vs Tack
To begin with basics, the concept of Orientation comes with a twofold meaning, actual and symbolic:
Actual: a position with regard to external (e.g spacial) coordinates, possibly qualified with abilities to observe, move, or act.
Symbolic: a position with regard to internal (e.g beliefs or aims) references, possibly mixed with known or presumed orientation of other agents, opponents or associates.
When business is considered, data analytics is supposed to deal comprehensively and accurately with markets’ actual orientations. But the symbolic facet is left largely unexplored.
Boyd’s contribution is to bring together both aspects and combine them into actual practice, namely how to foretell the tack of your opponents from their actual tracks as well as their surmised plans, while fooling them about your own moves, actual or planned.
Such ambitions once out of reach, can now be fulfilled due to the combination of big data, artificial intelligence, and the exponential growth on computing power.
According to a leading tools provider operational intelligence (OI) is the ability to “discover and analyze relationships between business events and corresponding IT events”.
From a marketing perspective, the moniker suggests some kind of cross-breeding between operational research, artificial intelligence, and real-time analytics. Yet, behind vendor dressing, problems and policies remain the ones traditionally dealt with by decision-making and knowledge management, and as far as marketing is concerned, pitches will hardly affect the assessment of field professionals.
Nevertheless, functional pitches may have a deeper influence if they try to outline the aims of operational intelligence to the people directly involved, affecting the way problems are understood and dealt with. That may be the case if business and system events are seemed to be put on a par: overlooking the directed dependency between actual events and their systems counterparts can critically hamper the very capabilities of systems decision-making.
Facts, Data, & Information
The new connected world of human brains and smart things have scaled down space and time by orders of magnitude, up to the point that events seem to come out as soon as they happen, wherever that may be. Facts and updates, that once were incoming as discrete and manageable batches of information, are now bursting continuously and massively as seamless streams of data that have to be processed on-the-fly into information lest they be cannibalized by ambient noise. That new configuration blurs the distinction between operational data (pushed, shallow, transient) and underlying information (pulled, deep, persistent), making it unworkable, if not meaningless altogether.
Taking inventories decisions as an example, traditional schemes rely on periodic readings of actual inventories and sales crossed with market foresight. Now, with on-line sales and the internet of things, real-time data can be used to build on-the-fly indicators whose biases and inaccuracy would be dynamically readjusted on the basis of information built on hindsight. At any given time (t), decision-makers will be presented with actual observations (a), initial estimations of previous observations (b1, b2), and revised estimations of previous observations (c).
Set along this framework, the debate about big data can be misleading as it puts the focus on the quantity of data feeding the processes, overlooking the process itself and the distinction between data, information, and knowledge.
Information, Knowledge, & Decision-making
Generally speaking, the distinction between data and information can be set with reference to time and context, data being instant and standalone, and information associated to a shelf life and domain. With regard to decision-making, it would mean that data can be directly used within the context of the current activities and circumstances; e.g, whereas on-line sales data may (or may not) be directly (i.e despite inaccuracies and biases) used to allocate inventories across depots, it has to be “mined” into consolidated information before being used in the broader perspective of inventories planning.
Compared to the transition between data and information, which is carried out by adding time and context, the one between information and knowledge is best understood in terms of decision-making.
Decisions are best defined as commitments set against some unknown circumstances: somebody, somewhere, or sometime. First, it ensues that decision-making calls for specific and timed information that has to be maintained up-to-date until decisions are taken. Then, taking decisions introduces some irreversible change in the state of affairs or expectations, making potentially obsolete all relevant information. So it may be argued that decisions is what transform information into knowledge.
Operational Intelligence: Objectives & Tools
Assuming decisions mark the nexus between information and knowledge, operational intelligence could be defined as the ability to put information to use, that ability being supported by the analysis of the relationships between business events and corresponding IT events.
Far from being academic, that distinction is essentially pragmatic as it marks the boundary between OI objectives and tools capabilities:
The aim of OI is to make sense (and profit) from the dynamic relationship between business (aka external) events on one hand, business objectives and enterprise capabilities on the other hand.
The role of supporting tools is to define and manage IT (aka internal) events used to reflect external ones and analyze them.
Since IT events are artifacts built on purpose there isn’t much to discover or analyze about them; not to mention the fact that confusing business events and their IT shadows is bound to undermine the whole decision-making process. So what is at stake for OI is how to design IT events as to timely and accurately trail the relevant business events.
Operational Intelligence & Actual Knowledge
As already noted, operational intelligence (OI) is about decision-making, which entails changing the state of objects, processes, or expectations. Compared to knowledge management (KM) which may or may not be time-related, OI is inherently bound to the actual state of affairs: on one hand it relies on specific and timed information, on the other hand it renders that information obsolete when it triggers decisions.
At the risk of oversimplification, operational intelligence can first be understood as a combination of traditional disciplines:
Data-mining is to filter facts and events, capture data, and analyze it into information.
Knowledge management chart information with regard to business objectives and enterprise capabilities.
Decision-making manage time-stamps and plan commitments subject to accuracy and likelihood.
But the specificity of operational intelligence is to be found in the way these functions are intertwined and cross-fed by operational concerns.
To begin with, data mining can be dynamically adjusted depending on what is needed for decision-making, and when. As a corollary, with the benefits of data so cooked in advance, some decisions can be taken directly, bypassing the mediation (and delays) of information processing. From a cognitive point of view that would be the equivalent of non symbolic (aka implicit) knowledge to be processed by neuronal networks.
Conversely, information processing could benefit from operational feedback so that knowledge management would be driven by business value, and the supporting information weighted by timing and shelf-life considerations. Whereas part of it could be done through implicit connections, it would be more comprehensively and explicitly achieved through symbolic representations.
Operational Intelligence: Signals vs Symbols
Assuming that intelligence is the ability to figure out situations and solve problems, one may conclude that it is inherently operational. Along the same reasoning, if knowledge is information put to use, it may be implicit as well as explicit.
Nonetheless, the merit of operational intelligence is to bring to a single functional roof symbolic and non symbolic knowledge, the former explicit, using mediation of semantic constructs and used to weight information and support managed decisions, the latter implicit, using direct associations between actual objects or phenomena, and supporting automated decisions.