Perfection is achieved,
not when there is nothing more to add,
but when there is nothing left to take away.
– Antoine de Saint-Exupery, The Little Prince
Readers can find the previous entry in this arc at The Unfulfilled Promise of Online Analytics, Part 1.
First, I want to thank all the people who read, commented, twittered, emailed, skyped and phoned me with their thoughts on Part 1.
My special thanks to the people with reputations and company names who commented in Part 1. Avinash Kaushik and Jim Novo, I thank and congratulate you for stepping up and responding (I queried others if I could include them in this list, they never responded). Whether you intended to or not, whether you recognize it or not, you demonstrated a willingness to lead and a willingness to get involved. Please let's keep the discussion going.
Also my thanks to those who took up the gauntlet by propagating the discussion via their own blogs. Here Chris Berry (and I also note that Chris' The Schism in Analytics, A response to Carrabis, Part II post presages some of what I'll post here) and Kevin Hillstrom come to mind. My apologies to others I may not have encountered yet.
Second, I was taken aback by the amount of activity this post generated. I was completely unprepared for the responses. It never occurred to me there was a nerve to be struck; only one person interviewed responded purely in the positive. The lack of positive response caused me to think this information was self-evident.
Well…there was one of the problems. It was self-evident. Like the alcoholic brother-in-law elephant in the living room, it took someone new to the family to point and say, “My god is that guy drunk or what!”
And like the family who's been working very hard making sure nobody acknowledges the elephant, the enablers came forward — okay, they emailed, skyped and phoned forward. One industry leader commented, saw my response and asked that their comment be removed. I did so with great regret because there can be no leadership without discussion, no unification of voices until all voices are heard.
Please note that some quotes appearing in this entry may be from different sources than in part 1 and (as always) are anonymous unless a) express permission for their use is given or b) the quote is in the public domain (Einstein, Saint-Exupery, etc).
Okay, enough preamble. Enjoy!
There was a sense of exhaustion among respondents regarding the industry. It took two forms and I would be hard pressed to determine which form took precedent.
One form I could liken to the exhaustion a spouse feels when their partner continually promises that tomorrow will be better, that they'll stop drinking/drugging/gambling/overeating/abusing or otherwise acting out.
It wasn't always the case. Once upon a time (that phrase was actually used by more than one respondent) there was a belief that if things were implemented correctly, if a new tool could be developed, if management would understand what was being done, if if if… Things could and would be better. Promises were made that were never kept and were then comfortably forgotten.
The second form I could liken to the neglected child who starts acting out simply to get attention. Look at me, Look at me! But mom&dad always have something else to focus their attention on. There's the new product launch, opening new markets, having to answer to the Board, (and probably the worst) the other children (marketing, finance, logistics, …), …
“When you know the implementation is correct you have to wonder if the specifications are wrong.”
Several respondents showed an impressive level of self-awareness. Many of them have moved on, either out of the industry completely or into more fulfilling positions within. All recognized that any industry that succumbs to promise and hype will ultimately end in disappointment.
The disappointment took two primary forms (clear schisms abounded in this research. Clear schisms are usually indicative of deep level challenges to unification in social groups) and the division was along personality types. Respondents who were more analytic than business focused were disappointed because “…a fraction of implementation achieve business goals. A tiny faction of those actually work.”
Respondents who were more business than analytics focused were disappointed because the industry didn't help them achieve their career goals.
For many in both camps moving on was a recognition of their own personal growth and maturation, for most it was frustration based, a running away-from pain rather than a movement towards pleasure. This latter again demonstrates a victim mentality, a caught in the middle between warring parents.
“When the tools don't agree management's solution is to get a new tool.”
Respondents demonstrated frustration with clients/organizations and vendors that refuse to demonstrate leadership. This was such a strong theme that I address it at length below. Sometimes a lack of leadership is the result of internal politics (“…and that's (competition, keeping knowledge to themselves, backstabing) is starting to happen (we see the schism (right word?) between Eric's 'hard' position and Avinash 'easy' (and others)…”).
Leadership vacuums also develop when power surges back and forth between those given authority positions by others. Family dynamics recognizes this when parents switch roles without clearly letting children know who's taking the lead (think James Dean's “You're tearing me apart” in Rebel Without a Cause). This frustration was exacerbated when respondents began to recognize that no tool was truly new, only the interfaces and report formats changed.
There was a sense among respondents that vendors and clients/organizations were switching roles back and forth, neither owning leadership for long, and again, the respondents were caught in the middle.
“Management pays attention to what they paid for, not what you tell them.”
Some respondents are looking at the horizon and reporting a new (to them) phenomenon; as vendors merge, move and restructure there's an increasing lack of definition around “what can we do with this?” This is disturbing in lots of ways.
Analysts will begin to socially and economically bifurcate (there will be no “middle class”). Those at the bottom of the scale will get into the industry as a typical “just out of school” job then move elsewhere unless they're politically adept. The political adepts will join the top runners, either associating themselves with whatever exemplars exist or by becoming exemplars themselves. But the social setting thus created allows for a multitude of exemplars, meaning there are many paths to the stars, meaning one must choose wisely, meaning most will fail and thus the culture bifurcates again and fewer will stay long enough to reach the stars. “You have to pick who you listen to. I get tired figuring out who to follow each day.”
Respondents admitted to lacking (what I recognize as) research skills. I questioned several people about their decision methods — had they considered this or that about what they did or are planning to do — and universally they were grateful to me for helping them clarify issues. Those that had appreciable research skills were hampered by internal politics (“Until my boss is ready nothing gets done.”)
Most respondents confused outputs with outcomes (as noted in part 1) because tools are presented and trained in two levels (this is my conclusion based on discussions. I'm happy to be corrected). There's the tool core that only few learn to use and there's the tool interface that everyone has access to.
Everyone can test and modify their plans based on the interface outputs but what happens at the core level — how the interface outputs are arrived at — is the great unknown hence can't be defended in management discussions and “…I can't explain where it came from so I'm ignored.” Management's (quite reasonable, to me) response follows Arthur C. Clarke's “Mankind never completely abandons any of its ancient tools”, they go with what they know, especially when analysts themselves don't demonstrate confidence in their findings. “I can only shrug so many times before they stop listening, period.” Management is left to make decisions based on experience and now we see the previously mentioned bifurcation creeping into business decisions. Those with the most experience, the most tacit knowledge, win. As John Erskine wrote, “Opinion is that exercise of the human will that allows us to make a decision without information” and management — asking for more accountability — is demanding to understand the basis for the information given.
“Did you ever get the urge when someone calls up or sends e-mails asking, 'How's that data coming?' to say, 'Well, we're about two hours behind where we would be if I didn't have to keep stopping to answer your goofy-?ss phone calls and e-mails.' This is called project management, I guess.”
“Ignore them” as a strategy for responding to business requests works two-ways. Management repeatedly asking difficult to solve questions results in they're being ignored by analysts until the final results are in. By that time both question and answer are irrelevant to a tactical business decision and once again the “promise” is lost. In-house analysts can suggest new tools and must deal with their suggestions gaining little traction. “Management works in small networks that look at the same thing. They're worse than g?dd?mn children. You have to whack them on the side of the head to get their attention.”
Management's reluctance to take on different tools and methodologies is understandable. Such decisions increase risk and no business wants risk.
“To change the form of a tool is to lose it's power. What is a mystery can only be experienced for the first time once.”
I asked for clarification of the statement on the right and was told that yes, there are times when old paradigms need to be tossed aside and knowing when is a recognizable management skill that can only be exercised by extreme high-level management, by insanely confident upstarts and lastly by (you guessed it) trusted leaders/guides. The speaker had recently returned to the US from a study of successful EU-based startups. When and how paradigms should be shifted and abandoned is a hot topic among 30ish EU entrepreneurs.
“We're suppose to be solving problems. But I can't figure out what problems we're suppose to solve.”
(the quote on the right is from Anna O'Brien's Random Acts of Data blog)
Business and Science are orthogonal, not parallel. Any science-based endeavor works to overcome obstacles. If not directly, then to provide insight into how and what obstacles can be overcome. Business-based endeavors work to generate profit. Science involves empirical investigation. Investigation takes time and only certain businesses can afford time because unless the science is working at overcoming a business obstacle, it's a cost, not a profit.
So if you can't afford the time involved in research and are being paid to solve business problems your options are limited. Most respondents relied on literature (usually read at home during “family time” or while traveling), conferences, private conversations and blogs. Literature is only produced by people wanting to sell something (this includes yours truly). It may be a book, a conference ticket, a tool, consulting, a metaphysic, …, and even when what they offer is free (such as most blogs) consumers pay with their attention, engagement and time (yes, I know. Especially with my posts).
Conferences and similar venues are biased by geographies, time and cost (again, even if free you're paying somehow. Whoever is picking up the bar tab and providing the munchies is going to be boasting about how many attended).
Private conversations provide limited access and that leaves blogs. The largest audiences will be (most often) offline in the form of books and online in the form of blogs.
Behold, and without most people realizing it's happening, exemplars form. The exemplar du jour provides the understanding du jour, hence a path to what problems can be solved du jour. Who will survive?
Historical precedent indicates that exemplars who embrace and encourage new models will thrive. More than thrive, they will continue as positive exemplars. Exemplars not embracing or at least acknowledging new models will quickly become negative exemplars and the “negativity” will be demonstrated socially first in small group settings then spill over into large group settings once a threshold is reached (and once that threshold is reached, watch out!). The latter won't happen “over night” and it will definitely happen (my opinion) because all societies follow specific evolutionary and ecologic principles (evolutionary biology, Red Queen, Court Jester, evolutionary dynamics, niche construction and adaptive radiation rules (along with others) all apply). The online analytics world is no different.
<TRUTH IN ADVERTISING DEPT>
Some people contacted me about Stephane Hamel's Web Analytics Maturity Model. I knew nothing about it, contacted Stephane, asked to read his full paper (not the shortened version available at http://immeria.net/wamm), did so, talked with him about it, told him my conclusions and take on it and got his permission to share those conclusions and takes here. I also asked Stephane if I could apply his model to some of my work with the goal of creating something with objective metricization that would be predictive in nature and he agreed (if you treat Stephane's axes as clades and consider each node as a specific situation then cladistic analysis tools via Situational Calculus looks very promising (asleep yet?)).
</TRUTH IN ADVERTISING DEPT>
A case in point is Stephane Hamel and his Web Analytics Maturity Model (WAMM). Stephane will emerge as an exemplar for several reasons and WAMM is only one of them.
WAMM is (my opinion) an excellent first step to solving some of the issues recognized in part 1 because it does something psycholinguists know must be done before any problem can be solved; it gives the problem a name. Organizations can place themselves or be placed on a scale of 0-5, Impaired to Addicted (Stephane, did you know that only 1-4 would be considered psychologically healthy?). WAMM helps the online analytics world because it creates a codification, an assessment tool for where an organization is in their online efforts.
I asked Stephane if he thought his tool was a solution to what I identified in part 1. He agreed with me that it wasn't. Its purpose (my interpretation, Stephane agreed) was that it creates a 2D array, creates buckets therein and then explains what goes in each bucket.
I asked Stephane if he believed WAMM provided a metricizable solution with universally agreed to objective measures (I told Stephane that I wasn't grasping how WAMM becomes an “x + y = z” type of tool and asked if I'd missed something). Stephane replied “…no, you haven't missed anything, because it is NOT a x+y=z magical/universal formula, that's not the goal. The utmost goal is to enable change, facilitate discussion, and it's not 'black magic'. A formula would imply there is some kind of recipe to success. Just like we can admire Amazon or Google success and could in theory replicate everything they do, you simply can't replicate the brains working there – thus, I think there is a limit to applying a formula (or 'brain power' is a huge randomized value in the formula).”
WAMM and any similar models would be considered observational tools (I explain “observational” tools further down in this post). Most observational tools (I would write “all” and don't have enough data to be convinced) trace their origins (and this is a fascinating study) to surveying; People could walk the land and agree “here is a rise, there is a glen” but it wasn't until surveying tools (the plumb&line, levels, rods&poles, tapes, compass, theodolite, …) came along that territories literally became maps (orienteers can appreciate this easily) that told you “You are here” and gave very precise definitions of where “here” was.
The only problem with observational tools is that the map is not the territory. Yes, large enough maps can help you figure out how to get from “here” to “there” and how far you can travel (how much your business can successfully change) depends on the size of your map, your confidence in your guide/leader, … . Lots of change means maps have to be very large (ie, very large data fields/sets), updated regularly (to insure where you're walking is still where you want to walk). The adage “Here there be dragons” places challenges in a fixed, historical location, it doesn't account for population and migrational dynamics (market movements, audience changes).
Or you need lots of confidence in your leaders.
“…any science first start as art until it's understood and mature enough, no?”
A conclusion of this research is that online analytics is still more art than science, more practitioner than professional (at least in the client/organization's mind). This was demonstrated as a core belief in responses as the ratio of respondents using practitioner to professional was 6:1. This language use truly shocked me. Even among non-AmerEnglish speakers the psycholinguistics of practitioner and professional makes itself known. “Practitioner” is to “professional” as “seeking” is to “doing”, “deed” to “task”, “questing” to “working”, …
Online analytics makes use of mathematics (statistics, anyway) and although some people use formulae the results are often not repeatable except in incredibly large frames hence any surgical work is highly questionable. As the USAF Ammo Troop manual states “Cluster bombing from B-52s is very, very accurate. The bombs are guaranteed always to hit the ground.”
A challenge for online analysts may be recognizing the current state being more art than science as such and promoting both it and themselves accordingly. They are doing themselves and those they answer to a disservice if they believe and promote that they're doing “science” while the error rates between methods are recognized (probably non-consciously) as “art” by clients. Current models and methods allow for high degrees of flexibility (read “unaccountable error sources”).
A good metaphor is modern medicine. Without a diagnosis there can be no prognosis. You can attempt a cure but without a prognosis you have no idea if the patient is getting better or not. Most people think a prognosis is what they hear on TV and in the movies. “Doctor, will he live?” “The prognosis is good.” Umm…no. A prognosis is a description of the normal course of something, a prediction based on lots of empirical data seasoned with knowledge of the individual's general health. A prognosis of “most people turn blue then die” coupled with observations of “the skin is becoming a healthy pink and the individual is running a marathon” means the cure has worked and that the prognosis has failed.
Right now the state of online analytics is like the doctor telling the patient “We know you're ill but we don't know what you have.” The patient asks “Is there a cure?” and the doctor responds, “We don't know that either. Until we know what you have we don't know how to treat you…but we're willing to spend lots of money figuring it out.”
This philosophy is good in the individual and not in the whole (as recently witnessed by the public outcries about the recently published mammogram studies and no more demonstration of communicating science to non-scientists has occurred in recent years).
But once the disease is named? Then we have essentially put a box around whatever it is. We know its size, its shape and its limits.
There can be no standardization, no normalization of procedure or protocol, when the patient can shop for opinions until they find the one they want.
The challenge current models and methods face is that they serve the hospitals (vendors), not the doctors (practitioners) nor the patients (clients/organizations). It doesn't matter if all the doctors agree on a single diagnosis, what matters is whether or not there is a single prognosis that will heal the client. In that sense, WA is still much more an art than it is a science, and while we may all attend HogwartsŽ, our individual levels of wizardry may leave much to be desired.
If you wish to claim the tools of mathematics then you must be willing to subject yourself to mathematical rigor. Currently there can be no version of Karl Popper's falsifiability when the same tool produces different results each time it's used (forget about different tools producing different results. When the same tool produces different results you're standing at the scientific “Abandon Hope All Ye Who Enter Here” gate).
“…gathered data that [we] knew how to gather rather than asking what data would be useful to gather and figuring out how to gather it.”
All the online tools currently available are “observational” (anthropologists, behavioral ethologists, etiologists, …, rely heavily on such tools). “Observation” is the current online tool sets' origin (going back to the first online analytics implementation at UoH in the early 1990s) and not much has changed. The challenge to observational tools is that they can only become predictive tools when amazingly large numbers are involved. And even then you can only predict generalized mass movement, neither small group nor individual behavior (for either you need what PsyOps calls ITATs — Individualizing Target Acquisition Technologies), with the mass' size determining the upper limit of a prediction's accuracy.
At this point we start circling back to part 1's discussions about “accountability” and why the suggestion of it gets more nervous laughter than serious nods. Respondents' resulting language indicates there is more a desire to currently keep WA an art than a science . There is less accountability when things are an art form. But “metrics as an art” is in direct conflict with client goals. And unless a great majority of practitioners wish their industry to mature there is no cure for its current malaise.
One solution to this is giving the industry time to mature. Right now there is conflict between the art and science paradigms, between Aristophanes' “Let each man exercise the art he knows” and Lee Mockenstrum's “Information is a measure of the reduction of uncertainty.”
Time as a solution has been demonstrated historically, most obviously in our medical metaphor. Village wisdomkeepers gave way to doctors then to university degrees in medicine because the buying public (economic pressure) demanded consistency of care/cures. Eventually things will circle back and again due to economic pressure. Enough clients will seek alternatives not provided by institutional medicine and go back to practitioners of alternative medicine at which point the cycle will begin again. People have been openly seeking alternative cures to catastrophic illnesses since the 1960s. Eventually money began escaping institutional medicine's purview and insurers were being forced to pay. The end result was that institutional medicine and insurers started recognizing and accepting alternative medical technologies…provided some certification took place, usually through some university program.
It will be interesting to see how WAMM economizes the online analytics ecology: will practitioners decide institutions lower in the WAMM matrix are too expensive to deal with? This means such institutions — which require experienced practitioners to survive — will only be able to afford low quality/low experienced practitioners to help them. This can be likened to a naval gunnery axiom, “The farther one is from a target, either the larger the shell or the better the targeting mechanism” and companies will opt for larger shells (poorly defined efforts) rather than better targeting mechanisms (experienced practitioners).
“A dominant strand for [online analytics] the past ten fifteen years has been incorporating web information with executive decisions.”
So far no single solution to concerns raised in this research is apparent (to me). Instead a solution matrix of several components seems most likely to succeed (WAMM is a type of solution matrix; you can excel along any axis and to be successful you need to excel evenly along all axes). So far three matrix elements — time, a lack of leadership and realism — have been identified. Time to mature is culture dependent so the online community as a whole must do the work.
(I believe the quote on the right originated with Ira Glass)
Realism — in the sense of being realistic about what should be expected and what can be accomplished is obvious — deals with social mores and leads in the “lack of leadership” concern. There can be no “realism” until the social frame accepts “realism” as a standard, until hype and promise are dismissed and this isn't likely to happen until leaders/exemplars emerge that make it so.
Progress in any discipline depends on public debate and the criticism of ideas. That recognized, it is unfortunate that the current modes of online analytics public debate and criticism are limited to conferences, private conversations and (as witnessed here) online posts. Conferences (by their nature) only allow for stentorian and HiPPOish debate. Private conversations only allow for senatorial flow. In both cases the community at large doesn't take part.
Blogs and related online venues are an interesting situation. They provide a means for voices to be raised from the crowd. Social mechanics research NextStage has been doing (we're working on a whitepaper) documents how leaders emerge (become senatorial, sometimes stentorian and in some cases HiPPOtic), how they fade, how to create and destroy them (for marketing purposes), (probably most importantly) how a given audience will perceive and respond to a given leader and what an individual can do regarding their own leadership status.
I bring this into the discussion because several people commented publicly (both in Part 1 comments and elsewhere) and privately (emails and skypes) that the industry (more true of web than search) suffers from a lack of leadership.
People who enjoy the mantle of leadership yet refuse to lead are not leaders. Recognized names had an opportunity to both join and take leadership in the discussion (I mention some who did at the top of this post). Yet the majority of others either failed to respond, chose to ignore the discussion or — as indicated by the quote opening this section — simply backed away when the discussion was engaged. No explanation, no attempt at writing something else. Considering the traffic, twits, follow-up posts on other blogs (for something I posted, anyway), this was an opportunity for people to step forward. Especially when lots of other people were writing that there was a leadership vacuum.
Leaders/Influencers take different forms (as documented in the previously mentioned social mechanics paper). Two forms are Guide and Responder. Guides are those who are in front. They may know the way (hence are “experts”) and may not. Experts may or may not be trusted depending on how well they can demonstrate their expertise safely to their followers (you learn to trust your guide quickly if you've ever gone walking on Scottish bogs. They demonstrate their knowledge by saying “Don't step there”, you step there and go in over your head at which point they pull you out and say “I said, 'Don't step there'.” A clear, clean, quick demonstration of expertise).
Guides who don't know the way rely heavily on the trust of those following them and can be likened to “chain of command” situations; they are followed because they are trusted and have the moral authority to be followed.
The Guide role is definitely riskier. It's also the more respected one because Guides lead by “being in front of the pack, stepping carefully, being able to read the trail signs hence guiding them safely”. The Responder doesn't lead by being in front. Instead they assume a position “closer to the end, perpetually working at catching up, but always telling the pack where to go, where to look and what to do”. The major problem for Responders is that people don't have lots of respect for that latter role. They may respect the individual and most people will quickly recognize the role they play and the lack of respect will filter backward to the individual.
This plays greatly into any industry's maturation cycle. New school will replace old school and unless our forebears' wisdom is truly sage — evergreen rather than time&place dependent — the emerging schools will seek their own influencers, leaders and guides. This is already being demonstrated in the fractionalizing of the conference market.
One industry leader offered three points in a comment, saw my response and asked that I remove their comment before it went live. I'm going to address two points (the third was narrative and doesn't apply) because I believe the points should be part of the discussion and more so due to their origin.
First, Web Analytics is not a specific activity.
I responded that nothing I'd researched thus far led me to think of 'Web Analytics' as an 'always do this – always get that' type of activity and offered that while different people use 'Web Analytics' for different purposes, the malaise is quite pervasive. Whether or not 'Web Analytics' includes a host of different activities or not is irrelevant to the discussion. The analysts' dissatisfaction with their role in the larger business frame, their dissatisfaction with the tools they are asked or choose to use, their dissatisfaction with their 'poor country cousin' position in the chain-of-command, …, are what need to be addressed.
Second, the individual wrote that there was no “right way” to do web analytics.
I both agreed and disagreed with this and explained that there are lots of ways to dig a hole. In the end, the question is 'Did you dig the hole?' More specifically, if one is asked to excavate a foundation hole, dig a grave, plow a field, dig a well, plant tomatoes, …, all involve digging holes, each requires different tools (time dependency for completion becomes an issue, I know. You can excavate a foundation hole with a hand trowel. I wouldn't want to and you could). Stating that 'There is a right way to do it' is a faulty assumption demonstrates a belief that standardization will never apply, therefore chaos is the rule.
Chaos being the rule is usually indicative of crossing a cultural boundary (such as a western educated individual having to survive in the Bush. None of the socio-cognitive rules apply until the western individual learns the rules of the Bush culture) or crazy-making behavior (from family and group dynamics theory). Culture of any kind is basically a war against chaos and what cultures do is create rules for proper conduct and tool use within their norms.
One could conjecture that the cross-cultural boundary is the analytics-management boundary. So long as management controls that boundary a) there will be no “one-way” to do analytics (the patients will self-diagnose and -prescribe) and b) analytics will never be granted a seat at the grown-ups' table.
So there better be a 'right way to do it', at least as far as delivering results and being understood are concerned, because without that the industry — more accurately, the practitioners — are lost.
“I could tell them 'It is not possible to send in the Armadillos for this particular effort but communication will continue without interruption' and they'd nod and agree.”
Two needs surfaced quickly:
- recognize what's achievable when (so people aren't set up to fail) and
- learn how to promote faster adoption of an agenda (without going to Lysistratic extremes, of course. Everybody wants to keep their job).
Accepting increased accountability addresses some issues and not all. Concepts from several sources (some distilled and not in quotes, some stated more elegantly than I could and in quotes) revealed the following additional matrix components:
1) “[online] Analysts need to share the error margins, not the final analysis, of their tools”
2) stop or at least recognize and honestly report measurement inflation
3) “Trainings need to focus on a proficiency threshold”
4) “…provide a strong evidence of benefit”
5) understand what [a tool] is really reporting
6) “It's better to come at [online analytics] from a business background than the other way around…” (“…but who wants the cut in pay?”)
7) “We should standardize reports because the vendors won't”
8) initiate regular, recognized adaptive testing for higher level practitioners
9) include communication and risk assessment training (some time we're at a conference, ask me about the bat&ball question. It's an amazingly simple way to discover one's risk assessment abilities)
“The problem is uncertainty…”
That's a long component list and most readers will justifiably back away or become overwhelmed and disheartened. Fortunately there's historically proven, overlapping strategies for dealing with the above items collectively rather than individually.
- Analysts live with uncertainty, clients fear it, so “…get uncertainty off the table” when presenting reports (this was termed “stop hedging your bets” by some respondents).This single point addresses items 1, 2, 4, 5, 8 and 9 above (hopefully you begin to appreciate that working diligently on any one component suggested here will accrue benefits in several directions (so to speak)).
- Identify the real problem so you can respond to their (management's) problem. This point addresses items 1, 2, 3, 4, 5, 6, 7 and 9.
- Speak their (management's) language. Items 4, 5, 6, 7 and 9.
- Learn to communicate the same message many ways without violating the core message (we've isolated eight vectors addressing this and the previous item: urgency, certainty, integrity, language facility, positioning, hope, outcome emphasis (Rene, I'm seeing another tool. Are you?)) Items 3, 4, 5, 6, 7, 8 and 9 are handled here.
- Be drastic. Rethink and redo from the bottom up if you have to. This point deals with items 1, 2, 4, 5, 8 and 9.
- Focus on opportunities, not difficulties. This point deals with items 4, 5, 6 and 9.
Any one of the above will cover several matrix components right out of the gate. The benefit to any of the above stratagems is that implementing any one will cause other stratagems to root over time as well, and thus the shift
- in what the numbers are about,
- how they are demonstrated,
- how to derive actionable meaning from them and
- how accountability is framed
mentioned at the end of part 1 can be easily (? well, at least more easily) achieved.
<ABOUT THIS RESEARCH>
I wrote a little about how this study was done in part 1. We contacted some people via email, performed various analysis on their responses, others via phone, ditto, others via skype, ditto, and some in face-to-face conversation. All electronic information exchanges were retained and analyzed using a variety of analog and digital tools. Face-to-face conversations were performed with at least one other observer present to check for personal biasing in the resulting analysis.
Like any research, others will need to add their voices and thoughts to the work presented here. I make no claims to its completeness, only that it's as complete as current time and resources allow.
</ABOUT THIS RESEARCH>