Information in the ‘Post-Truth’ Age

Being a new blogger, I am now always on the lookout for blogs I enjoy, be they geological, technological or the abstract/absurd.* They are rich sources of opinions. Occasionally, information sufficiently analyzed to yield insights. Information and opinion are often confused or confounded be it blog or traditional media.

Obviously, quality first-hand information feels like an ever rarer commodity. Nowhere was this more apparent than in what passed for election politics in the recent United States election cycle. The debates focused on character and past indiscretions as proof neither party’s candidate was fit to rule from the perspective of the other. Policy analysis and an understanding of the intended consequences received little mention and even less critique. That lead me to ponder: just how important is information to us, the benefactors of that wonderment: The Information Age? Now that ‘post-truth’ is a fact of life, what role does information play, if any?

Why do we even need to seek information, or evaluate it, or even worry about where it comes from or its quality? After all, with information so ubiquitous, surely quality, relevance, and reliability are ‘givens’, just like the reliability of the power systems we put in place, right? Sadly, no. If information’s reliability drops to 50/50 (i.e. any given piece of information could be reliable or not, with an accuracy no better than chance) trust will logically become completely absent. If true facts don’t matter anymore, how can we trust anything or anyone?

Reliability of information is never 100%. Even the best data is incomplete or contains errors. Collecting it takes effort. Confirming it takes time. Just ask any published author for a scientific journal or your mechanic who’s tried to extract data from your car’s computer. Often, information quality assessment includes views on its relevance, its reliability, its applicability and its quanta (resolution, fidelity, granularity and vintage). Arguably a recent inflation discussion misses the point on this front, as the measures which inform them fail to capture quality as a characteristic of goods & services in society and only focus on their prevalence. (It’s a shame he doesn’t have a blog.)

We are now at the point where we collect data (via a plethora of sensors on the IoT) simply for its option value: “let’s just collect a bunch of data and hope in the future we can find something interesting in there to help us do things better.” However, even unreliable information is not ‘free’ and always comes with risks, not the least that the information may not be irrelevant to the situation at hand (decision frame). That mindset of ‘some possible future optionality’ reflects an implicit view the information will have relatively high value in current or future decision-making context. This assumption benefits from a thoughtful challenge: what is the value of information in (your specific) Big Data application?

Modern systems (AI, neural networks, Watson, etc.) all rely on ‘judgment’  about the information (i.e. probability) to some degree; often this is updated from some known or estimated base rate. Knowing the reliability of information underpins a basic learning/state-update-model made famous by the Reverend Bayes‘ mathematical description.  While high reliability is a goal of Big Data systems, the reliability of the data itself, and perhaps, more importantly, the interpretation of the data/analysis –  its meaning as applied to the real world – also has some reliability. Sometimes this can be quantified; sometimes not. This is a key consideration for any use of information regardless of its source. As we embrace ‘post-truth’ correctly characterizing that reliability becomes ever more important. We must always be mindful that the characterization process is itself subject to the basic rules that govern how our mind works with respect to risk and benefit.

The reliability of any first-hand information is thus slowly (rapidly?) shifting from high quality / high reliability (implying high trust) to being no more informative than chance would dictate.**  I find it not a little ironic that trust of first-hand information (and its confusion with opinion) is decreasing rapidly while a simultaneous explosion of data generation, storage, and analysis. If these could be seen as extreme poles of information quality, one type is racing to minimum quality while another is racing towards full reliability (to the best of human ability) on high-reliability systems.

An advanced application of the use of information gets short shrift in the Big Data discussion: Value of Information. “Value” seems to focus exclusively on the direct cost of building, running and using the systems necessary to apply advanced data processing and storage. Value of Information analysis seems MIA, or only of interest to academia. Its adoption is easier than ever, provided an appropriate framing is to hand.

Big Data is an enabler of decision making, just like any other information. Increasingly good curation with sophisticated computing systems adds to the reliability of accessing that information, applying it in real-time and quantifying its reliability or even its applicability; it does not replace the other pillars of quality decision making. I have yet to see a Big Data system advertised as being able to improve Commitment To Action. Perhaps instead of an “impact score” based on citations, all publications should be required to have a reliability score on their publications, taking into account past accuracy on 1) the actual facts 2) actual statistical significance and 3) any forecasts they make in relation to those. The score would have to be created by an independent body, preferably fully algorithmized.

Big Data will not end Decision Analysis. It will reduce the cycle time for some decisions. It enables increasingly relevant data/information to be used in routine decision making. Routine decision making lends itself to being algorithmized. That trend will continue. The non-routine, the novel, the non-repetitive (dare I say ‘strategic’) will command ever more of the mental energy of people / businesses / governments. Information reliability under these circumstances will continue to rely on judgment. Supplementing that judgment with statistics has arguably become the permanent mission for frequentists; their raison d’etre. The blurring distinction between information volume and information value gets harder to disentangle daily. Judging its use in decision making becomes a commensurately more rarified art form.

* I probably owe my readers and apology for not letting them know when I link to a Paywall site. Several of the news feeds I read are fastidious about this and I mean to emulate this practice, but somehow I never do. I try not to link to arcane or moderate readership subscription services and like to imagine my readers have general interest subscriptions similar to mine. For those who do not, my apologies. Paid news services are still able to bring excellent content to readers attention, and despite the abundance of self-publishers, I do not envision paid information services (i.e. The Subscription Economy) subsiding anytime soon.
**Where first-hand information/opinions to be fully unreliable, they would ironically become highly reliable – for their ability to anti-predict their supposed topic of authority.

Well Equipped?

The Australian Financial Review publishes an ongoing theme of Leadership articles. The authors in this section have a wide remit. Anything from business fashion to consulting trends, entrepreneurs, and just generally big personalities. Recently, they highlighted findings from a survey done by Ernst&Young. The punch line: CFOs and accounting/finance professionals, in general, feel they lack the skills for- and too much is asked of them regarding- setting strategy, planning and even operational management for their companies. In short: decision making.

The AFR’s Agnes King says, “EY finance management consulting leader Donal Graham said it was concerning that more than half of Australian CFOs surveyed did not have the right mix of capabilities in their finance teams to execute and support the organization’s strategic decision making.” A  more scathing remark on the state of decision making is difficult to fathom: half of those tasked with doing so felt under- or ill-equipped. Others have noticed similar anecdotal trends and commented upon them; written books about it even.

CFOs are not the CEOs. The roles are different and they should be. They might not even be the most important decision maker in an organization. They are almost always on the Decision Team. When CEOs start to rely on CFOs & their teams to do more of the things CFOs feel scarcely able to do, one has to wonder at the cause. Do the CEOs (shorthand here for Board / Chairman / CEO / Other Executive Who Might Be Qualified to Do That Work) themselves feel unable? Do they lack the headspace? Skills? Time? Resources? Support? Perspective? Capacity? Or are those rare CFOs who can actually able to do those things asked of them making it harder for their peers (and/or neglecting their other CFO duties)?

It is no secret there is a lack of global-class expertise on the Australian Management bench. Training in MBAs and other management programs abound, but don’t seem to penetrate Boards. I’m generally ambivalent quotas of any kind for Boards (gender or otherwise). I prefer to believe that any group with that much intellectual horsepower is aware of the pitfalls of having a team of carbon copies & group think. They will self-select appropriate diversity (or perish if one is not afraid to put too fine a point on it). If I had to favor a quota system it would definitely include significant depth in DA/DQ, formal management training and diversity of all kinds (cumulative experience, industry, a range of experience, business cycle(s), gender, profession, etc).

Again according to Ernst&Young it was much more common for CFOs outside of Australia to have the general Management training an MBA confers. (The general comments about the lack of ongoing professional training is disturbing in itself.) Extending that line of thought to the formal decision training undertaken by Executives in Australia and its small wonder at the consequences that dearth of knowledge enables in the real world.

A number of MBA programs (AGSM at UNSW, MBS, UofA,UniSA, UofS, UofQ, QUT,  and Curtin amongst others) are offered in Australia. Only one (AGSM) appears to have subject focused on decision-making (5374)*. This emphasizes the psychological aspects of decision making rather than treating it as a whole subject which draws on many disciplines. QUT seems to go a step further with separate introduction framing and data analysis, but both appear to focus on only one aspect of DA/DQ.

It has been my anecdotal experience that MBA’s are not highly regarded in Australia. It’s not a stretch to say one (of many) causes might be that none of them really seem to prepare candidates for doing what a manager must: make good decisions for the company. Focusing instead on the skills of managing a more diverse workforce; a more technologically advanced workforce (with associated productivity enhancements right?); and a more restive workforce. These are indeed valuable and complementary skills, but why the neglect for decision analysis?

Certainly, in the Oil and Gas industry in general, they have a somewhat poor return on investment for those who stay with operators (again anecdotally). Even if MBA programs were designed to equip future managers (and leaders) with a thorough and complete skill set in DA/DQ, the benefits of doing so lie well into the future, while difficult decisions arise  with regular immediacy in many parts of the economy. Yes, they must have skills to manage people; even know accounting and strategy and what Big Data really is and why one might need it; to inspire; to promote & market; to transform; to manage change; and all the rest. Tradeoffs in what skills to focus on are inevitable in any education program (and company). All the more reason to underpin, indeed lay the foundation for, making all those tough, agonizing choices with genuine skill.

The combination of Accounting only (or not even that) certification and the call on CFOs to take more general management responsibility has worrying implications for the state of Australian Management (at least with respect to CFOs). I laud the Innovation and Entrepreneurial Spirit that promote failing fast (or whatever ) – there are few things better for building experience.  That failure needs to impart the lessons that come along with it. When failing fast (because we are innovating even faster, presumably), poor decision culture (due to lack of a critical mass of trained persons), more problems and more complex problems with shorter cycle times combined – a sort of perfect storm of social regression might be primed to emerge . Hardly the stuff of legends when faced with the task of preparing Australia for the post-Boomer (and PostBoom) years.

Recently I have begun to notice more and more the prevalence of mention in media related to decision making. I relish this. Awareness of a problem is the very first step in finding a solution. You can find examples of people pointing out…issues with decision-making nearly e v e r y w h e r e. The abundance of advice does not appear to translate well into adaptive behavior [citation needed].

*I’m going to assume that any subject with the word “Accounting” in its title (e.g. Accounting for Decision Making) is really about Accounting rules and concepts, and any discussion of a decision process, frame, quality, analysis, or similar topic receives cursory treatment. If that.

Pockets, Islands

In my last blog, I asked a pointed question concerning the activity of communities of practice that promote good decision-making behaviours. As a fellow attendee to DAAG noted to me: there are pockets, islands of practitioners that probably see the Australian decision analysis community as I do – a bit disconnected. A meeting this week with a couple of local champions reinforced this view.

Two of those are fairly vocal about their use of DA in the E&P space (in Australia*): Chevron and the ASP at University of Adelaide.

Chevron was an early adopter and eventually had a very strong culture of applying it to their upstream business. Their downstream business has recently embraced it with gusto. When you’ve been doing DA as long and as completely as Chevron has, it’s easy to succumb to pitfalls along the journey – even with a well-trodden path. People come and go, the dynamics change, the community stagnates; it becomes a box-ticking exercise and a laborious one at that. As Apple is (was?) well aware, people avoid cumbersome things. DA/DQ makes their life harder, so managers of all stripes find workarounds, then ignore it all together. In truth, IPA exists for no other reason than to document the results of this kind of behaviour (though they might disagree) and the impact on the execution of major projects. The fact that so much success continues to come from the application of DA attests to the robustness of thought in the decision quality paradigm.

The issue is choosing the right scale to intervene at seems daunting. The HBR nailed it recently at the simple end of the spectrum. As I mentioned in my last post, real value exists for DA’s application at many scales. Like good active listening, the art lies with relating to (or aiding discovery of solutions for) the other party’s Perceived Problem, regardless of the Real Problem. In Chevron one “looks like help.” Those people who have deep management consulting expertise that comes with mastering the DA toolkit often don’t bring the ‘consulting presence‘ to the table; they do bring the needed thinking.

At the educational end of the spectrum, the curriculum of the ASP explicitly includes management focused subjects in this vein: economics, D&RA, project management etc. The best and brightest (at least that I witnessed) excel during the learning of the fundamentals. Do they get hired as DA’s into the E&P industry? Rarely as graduates in my experience, and definitely not in this price environment. Those with training tend to be technically minded – not least because analytics, valuations and a fluency with uncertainty tend to cluster there also. The ‘convince me’ approach often falls on its face when decision makers believe they already know how to make good decisions and/or need to retain obfuscation in the process (for whatever reason).

Not so much of an army then, as artisan(s). Little wonder only islands flourish.

Some companies value the skills but regard them as a small part of a much larger technical role. They see decision trees, or stochastic simulation, or framing as portions of a body of knowledge they can cherry pick to suit their needs…better than nothing. Experienced practitioners know: unless a 100% on every dimension mindset permeates your application(s), your process suffers from a weak link.While an improvement on advocacy, partial adoption only serves as weigh-station to a high-quality decision culture. As every student of Peter Drucker knows, culture eats strategy for breakfast.  Culture strong on all the dimensions of the decision quality paradigm has the highest probability of having good decision-making behaviors. Connected communities reinforce the sense of purpose and guard quality. The reputation of a knowledge practice probably eats its culture; poor practice erodes reputation in whole fields.

In the 1800’s productivity resided with artisans; in the early 1900’s  work-based hierarchies redefined human productivity enabling the modern economy; in the 2000’s information connectivity allowed network(ing) effects to begin the long process of eroding the power of hierarchical productivity [citation needed]. That very coarse grading of the stages of economic development notwithstanding, a move towards connected communities of influencers, now entirely global, practically defines the Knowledge Age. Moving from pockets & islands towards connected community (and maintaining them) ought to rate highly on the priority list of all knowledge practitioners.

The implications for the disparate nature of the Australian DA/DQ community and its sustained application of DA/DQ at persistent, expert and fluent organization levels seem far from clear.  I doubt that a “strong signal of excellent health” features as a high probability interpretation. A quick search this week on for keyword “decision” would lead one to the conclusion only qualified accountants, selected financial professionals, ICT professionals with system architecture design experience or the odd commercial manager constitutes the entirety of the workforce in need of professional aptitude for decision making.

SDP’s efforts notwithstanding…that won’t do at all.



* In case it escaped your attention, the centre of knowledge for DA/DQ happens to be somewhere near northern California. A Pacific basin focus should come naturally.



Introducing: The 5280 Group Blog

Blogging is hard. You need to be committed. One of my favourites, The Aussie Oil and Gas Observer, smashes them out daily. They contain wit, are often insightful and full of intelligent commentary about the state of play of some of the more colourful players in whatever space they occupy. They are never, ever cynical; or immoderate; or sarcastic. Others I like have practically become modern day institutions in their own spheres of influence for their wide (some wider than others) syndication: The Slugcatcher, Nate Silver and Patrick Leach.

At their best they add sensible, intelligent commentary to forums and communities that need to be tended, rejuvenated, curated by those who care. Its a form of modern day intellectual gardening. When done well, blogging is (almost) better than publishing: low cycle times means insights are fresh, topics relevant, but put in perspective of the shared topical history. That’s a hard ask for anyone, let alone someone new to the genre of Decision Analysis, and even harder for someone lacking a serious writing background. (I’ll probably need to get my wife to be a permanent guest editor at some stage).

So, here I go: take the challenge of adding-to rather than detracting-from the cacophonous trumpeting of the internet blog-o-sphere.

The main reason this blogger finally screwed up his courage and took up the keyboard is this: I like Decision Analysis (or Decision Quality depending on who you ask). When introduced to it during my Masters at the University of Adelaide’s ASP (a permanent thanks to Steve), my first reaction was along these lines: “why the heck isn’t this the first thing you learn at University?” Honestly, 2 years of calculus were next to useless after you have access to Matlab or WorframAlpha (ok, not totally useless); but better decision making skills enhance every aspect of a persons life, where as doing triple- or line- integrals for hours on end is not exactly promoting enhanced quality of life for all concerned (as intellectually satisfying as they may be when you do solve them). I want to read more about its use. I want to apply it more. I want to find a community of practice and help it grow.

This new found passion eventually lead me to want two things: 1) do more of it professionally, and 2) find more people who were doing it well, learn from them and see if we could help others do it better together.

To that end, I joined the SDP and attended DAAG 2016 (in Banff). For those less familiar with Aussie slang, the DAAG moniker could hardly have been more poorly or in fact more properly chosen. In Australia, a Dag is the odd but love-able outsider; the underdog; the likeable quirky person who always seems to be doing not quite the same thing as everyone else. It can be a horrible put-down, or an expression of endearment. These are the DA/DQ professionals. They are that go-to person who always seems to have more clarity about your problem(s) than you do; but who is not the over-paid consultant-y type (no one from McKinsey & Company was there to the best of my knowledge, nor any of the other Big 4); who exudes sensibility, but not officiousness; who you know that, after a conversation with them, your mind will have expanded. They love data and hard problems, but don’t obsess over it like Engineers or Data Science types. Problems or decisions can be progressed to acceptable solutions with a conversation, or a matrix, a workshop or a huge model developed over years. They can be smothered in process, or chatted about over coffee. Insights are a permanent feature, designed-in to the thinking and methods, not grappled for at the last minute. They recognise uncertainty, characterise it like any other abstraction and incorporate it into their thinking as a matter of course. Properly identifying it and distinguishing it from decisions is one of their key tenants.  I loved it.

Formal Decision Analysis is an art form – it is not widely practition-ed, but widely applicable. The people present at DAAG know the power of their knowledge base, but continually struggle to find open ears. Advocacy dominates human decision making[citation needed]. Using formal systems takes effort, and when decision making gets hard, “more process” is often the first thing to go (lazy System 2). When you find such a like minded group of people: dedicated to doing better with the resources humanity has at its disposal (or similar definition of a decision), its hard to fail to be recharged.

The application of Decision Science appears, to this blogger at least, light on the ground in Australia. Whole industries suffer from poor practice of it. Yet where are those dedicated to ensuring we do/are better (aside from at DAAG) rather than just point out the poor behaviour of others; to ensuring good fundamentals in decision making are not just available, but embedded, permanent, dynamic, alive and applied as widely and as thoroughly as possible?

This blog will dedicate itself to the exploration of the application of Decision Science in Australia, and mainly in the Oil and Gas context, since that is my professional foundation. I know its application is wider and broader than I can now identify. If you are keen to join me on this journey, do so now.