Preamble
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.
I like your “one can not stop a running train by running ahead to stop it”. Yet, scientists have just confirmed for the first time Einstein’s gravitational waves, whose existence may jumping before the train possible.
I concur, you mention that information can become unworkable and meaningless, great point! I have always maintained that data processed through events and instances, changes the view and value of its contents; what I fear, is that businesses and the IT industry has lost controlling or harnessing data. Through instant-i-ation (mostly inadvertently) businesses create insurmountable quantity of data thus making it impossible to store these data for future uses (history, regulations, security, etc.) and consequently technology experiments in finding solutions that attempt to manage the influx Big Data, Cloud Technologies, etc. are a result of these attempts. Through EA methodologies, businesses, not IT, can solve this but the mindset that IT can solve this is erroneous, one can not stop a running train by running ahead to stop it; you can stop it at the station and that’s what businesses need to do through EA by creating the business events that matter, with these templates, the train’s boundaries are on its tracks.