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“Appropriate Empowerment” is the third and final element of the Holy Trinity, the three essential characteristics of sponsors of successful analytics practices, covered in the current series of posts. Appropriate Understanding and Appropriate Incentive were covered previously.
As before, this is an examination of the success mode and failure modes of the element in question. What does Appropriate Empowerment (or just “Empowerment” for short) look like when it succeeds, and what happens when it fails, or other elements fail to support it ? The success mode of Empowerment considers the situation where all elements of the Trinity are in place, but focuses on the role played by Empowerment.
The Failure Mode of Empowerment is the situation where the sponsor possesses Understanding and Incentive, but lacks Empowerment. We explore this situation, along with possible remedies, before concluding with the Isolation Mode, the situation where Empowerment is present, but alone, with neither Understanding nor Incentive in place beside it.
The success mode of Empowerment is simple, yet essential. Empowerment is the least visible element of the Trinity, more notable in its absence. Where the Sponsor sees the need for something to be done to the benefit of the business through analytics, and has the right Incentive to make it happen, then Appropriate Empowerment simply means : it happens. There is no one who can overrule, block, derail or otherwise unhelpfully modify any analytics initiative that has been put into motion.
Understanding ensures that the sponsor identifies the right analytics initiative for the greatest benefit to the business, and takes into account all that is required to enable it. Incentive ensures that the Sponsor actually wants this to happen. Empowerment then is simple : the Sponsor is in a position to launch the initiative, and ensure that it proceeds to the correct conclusion. He is able to support it with all the resources it requires, and protect it from unhelpful stakeholders. He is also in place to ensure that recipients of analytics recommendations act on them if the process requires them to do so. Tyrannical ? Perhaps. Far-fetched ? Certainly. But this is the ideal, however out of reach it may be for (current) real-world large organisations.
Empowerment is thus quite simple. It is the ability to make things happen.
It is also an absence of unhelpful constraints. A Sponsor with the Holy Trinity is sufficiently empowered not to worry about unreasonable or ill-defined expectations of value before the initiative or function is ready. Empowerment ensures that the function is not subject to IT-style management practices, deterministic waterfall and project management approaches. His analytics function is lean, agile and experimental : free to learn, fail repeatedly (for a time), as required to continually reach insights of massive value and exploit them.
The failure mode sets in when a sponsor has all the best intentions in terms of Incentive, and is well versed in Understanding what an analytics function can do, and what it requires to achieve it, alongside budget and a mandate to create the analytics function. Unfortunately, he may well lack the power to act as Understanding and Incentive may compel him to.
Any dilution of empowerment invites unreasonable expectations born of poorer Understanding and Incentive. A sponsor beholden to other managers, stakeholders etc is subject to constraints, expectations and pressures that may prevent an agile, experimental approach. The cargo cult of analytics, “Analytics in a Box” solutions promoted by some vendors stand in opposition to the agile approach, and enjoy attention and support from far too many senior executives. The resulting analytics cargo cult, subscribed to by much of senior management, expects great value from analytics, but does not know how to define this value, or even to measure it. This very lack of clarity may be what imposes inappropriate deterministic project management frameworks such as PRINCE2, and other inappropriate business analysis and management oversight by people who have no idea what they are managing or why. in such situations, project managers may grab the first objective metric, however irrelevant or minor and focus on it as a box ticking exercise. The analytics function is then little more than an IT production line, creating something of indeterminate value to satisfy a management fad. A sponsor beholden to such powers cannot be said to be sufficiently empowered. Worse yet, ignorant or indifferent management may relegate the sponsor under the auspices of IT. Needless to say, this is not an ideal outcome.
One large pathology crippling Empowerment is the modern corporate stakeholder model. A committee of stakeholders is not a Sponsor, especially when enough members of that committee have far from perfect Incentive or Understanding, and perhaps far too much Empowerment. A committee can be on the whole more stupid, poorly Incentivised and disempowered than any one member. A Sponsor beholden to such a Committee is hardly empowered, and the Committee as Sponsor is a far from ideal scenario. The fact that this situation is reality in so many large organisations does not diminish the fact that it is utterly pathological.
In the ideal situation the Sponsor is beholden to no one with excessive power who is inadequate in the other two key characteristics. The ideal Sponsor is therefore the CEO, and better yet a manager / owner. Again, this is perhaps unrealistic, but still needs to be identified as the ideal, and any deviation from it analysed in terms of potential failure of Empowerment. It is also the reason that the most innovative, valuable and agile analytics exist in tech startups and not large “Enterprises” (in quotes because they are usually the very opposite of that word)
Not all pathologies of Empowerment concern levels of power above the Sponsor. Other pathologies of Empowerment are lateral. The most immediate lateral power issue is one with IT : too many IT functions find it their job to block analytics access to tools, especially open source tools that are otherwise readily available, free and powerful. They may prevent access to adequate, and otherwise cheap and readily available hardware and useful online services such as cloud computing. They are also known for starving the analytics function of data. Too many analytics functions are in a situation where the main expenditure of effort is building business cases for data, tools and hardware. A sponsor who knows this to be the case but cannot fix it is clearly not sufficiently empowered.
Lateral Empowerment is also an issue with “trigger pullers”, people whose job it is to act on the recommendations of operational analytics. The most striking case of this is a pathology i have seen in a multitude of organisations making use of predictive operational risk analytics. Predictive models provide lists of targets (eg revenue leakage, non-compliance, suspicious behaviour, fraud risk indicators etc). In all cases a human being is provided a list of targets generated by the predictive model. Ideally, this human being proceeds to manually investigate the targeted cases. Unfortunately, in most situations, these individuals do not understand or trust these predictive models. In my experience, many such individuals cannot conceive the very idea of the inference of a model from data. It would appear that there are whole cultures of people who cannot imagine such a thing as statistical induction. They naturally voice their displeasure and challenge, stall and undermine the process. Much of a Sponsors job seems to be the thankless, draining and often never ending task of “winning them over”. A sufficiently empowered Sponsor would, however, be in a different situation. When asked why these people should trust these models he would be able to answer “because if you do not, I will fire you and perhaps hire someone who does what they are told. Or replace you with a smart pattern matching algorithm”. Again, this is perhaps not realistic, and perhaps suggesting something that certain Public Sector Unions would consider on par with a crime against humanity – asking that people do their jobs. The whole issue of uncooperative “trigger pullers” was only raised to make a point about Appropriate Empowerment: if a Sponsor is not able to ensure that human components of an operational analytics value chain do cooperate and act as a part of the analytics value chain, there is a failure of Empowerment. Perhaps effective analytics sponsorship, as defined in this series is impossible in most organisations where employee non-compliance and stakeholding is a given.
A lack of Empowerment is however, far from the end of the world, and the relatively dystopian situation described above matches many existing analytics functions, particularly in government and quasi-government organisations. They still manage to survive, and add some value, although arguably but a fraction of what would be possible if only sponsors were more Empowered. These organisations have in fact found themselves innovating in a number of fronts, dealing with insufficient Empowerment, and in some cases developing methods of generating more of it.
One key solution to the problem of insufficient Empowerment is Separation from IT. As far as possible, as quickly as possible, it is important to establish a “sandpit environment”, separate from the main IT network, where new hardware may be added, and software loaded outside of IT governance. This is essential if appropriate computational power and open source tools are to be leveraged quickly and effectively.
Another part of the solution, and one that is even more fundamental is Stealth Mode. It is imperative that a new analytics function has the ability to learn, experiment, and fail in its early stages. Expensive budget items such as vendor tools create massive, thought ill-defined expectations. Expectation management is yet another reason to avoid expensive vendor software early in the creation of an analytics function.
Ideally, the function has a small crew of capable, flexible people, a small budget and access to data and open source tools. Also, the function has a main focus that is a well-defined, business as usual task such as reporting. Actual analytics can be done on the side, as a side project, and not announced until it yields results. These results can then be presented as wins to formalise and Empower the nascent analytics function. There may then be sufficent leverage to acquire more staff, create a sandpit environment and acquire data reliably.
As discussed previously, the most important element of the Trinity is Incentive. With Incentive alone, the Sponsor knows that their first task is to increase their Understanding. Some of this is reading/study, some of this is consultation with experts, and much of this is experience which can be obtained in stealth mode. Empowerment is important, but as we can see it comes third in importance.
Indeed, most capable analytics professionals find themselves working for under-empower sponsors. This is not ideal, but not a career-ending situation. Indeed, the struggle for further Empowerment of the Sponsor is the defacto KPI of most analytics functions, and many professionals find it as exhilarating as they may find it frustrating.
It remains to discuss the “Isolation mode” of Empowerment. What happens when the Sponsor has all the power, but no Understanding, and,lacks the right Incentive? Here ignorance conspires with either a lack of real enthusiasm for Analytics, or an entirely different agenda, and gives them a hefty cheque book. So, what can happen ? A storm of Cargo Cults, management fads and buzzwords. “Analytics”, having something to do with “data” and software must clearly be some kind of IT, best managed and bought by the CIO and best explained by people who sell software. And that’s how the wrong kinds of Vendors happen. Long sales lunches. Exciting pre-sales presentations. Use of the words “Enterprise”, “Innovation” and “Insight” by people who don’t have anything to do with any of them. “Case studies” of previous such exchanges in other high profile corporations, presented as success. People who may not really care what they are selling, sell to people who don’t really understanding or care what they are buying. Consultants, the “best practice”, “brand recognition” kind jump in. More money gets spent. Everybody involved wins, except the (theoretical and distant) shareholders, citizens and other ultimate beneficiaries of the business. Almost always, none of the parties is an actual owner of the business in question. Most owners are far more sensible than that.
So what happens after that? Software get installed. Systems get integrated. People get hired, maybe, as an afterthought to mind the (far more important) Machines. These people are likely software developers, data base managers and project managers. Maybe even a token statistician. Gannt charts get ticked. Bonuses get paid (at least on the vendor side). Conferences benefit from new “Best practice” case studies. The Vendor-Consulting complex marches on in all its dinosauric grandeur.
So Incentive and Understanding matter, and Empowerment on its own is not a great idea, however common this situation may be.
The current series of posts deals with the “Holy Trinity”, the three characteristics that sponsors of analytics need to have in order to define, foster and support an effective and thriving analytics function. These are :
Appropriate Incentive – doing analytics for the right reasons, genuinely wanting analytics to succeed and thrive, and appreciating analytics product.
Appropriate Empowerment – having the political and financial clout to ensure that the analytics function gets the resources they need, that analytics is managed and directed appropriately and that analytics product is used appropriately by business users.
The first article introduced the Trinity, while the second article explored Appropriate Understanding.
In this article we explored the success and failure modes of the second element, Appropriate Incentive. This is the most important of the three, and the one without which improvement in the other two is almost impossible. As before, the exploration will divide into three parts. The first will discuss success modes, the ideal situation where all three are in place. The focus will be on the role of Appropriate Incentive in this situation, although there will be some mention of its interaction with the other two elements.
The second part will discuss failure modes of Appropriate Incentive : those situations where the other two elements are present, but Appropriate Incentive is not. This is the situation where the sponsor of analytics has a good understanding of what analytics entails, and what is required of him to make analytics a success. He also has the budget, mandate and seniority to make this happen, but for some reason choses not to do so.
Finally, we will explore the “Isolation Mode” of Appropriate Incentive, the situation where the sponsor has all the best intentions, but neither the understanding nor the empowerment required.
Appropriate Incentive – Success Modes
Where all three elements of the Trinity are present, all things are possible.
The ideal sponsor supports, protects and nurtures their analytics function because they see it as they key determinant of enterprise success, which is the sponsor’s actual key incentive. Actual, that is, not just stated.
This ideal sponsor is also that function’s number one client : the intelligence they provide is of enormous value to the sponsor.
The sponsor with Appropriate Incentive wants to see analytics thrive, and wants to see the organisation continually transformed by it. He wants to see effective, objective and unambiguous performance management at all levels, especially the senior executive, and especially around their ability to forecast, a key indicator of good decision making. He is prepared to face the inevitable pushback from those that might be uncomfortable performance measurement, change and complexity, and thrive on a world of status and subjectivity. This pushback is inevitable, and according to much documented Agile and Lean theory, far from being a negative this is a key sign that innovation is in fact successful.
The Appropriately Incentivised sponsor wants to see constant expansion of analytics into new areas of the business, and the inclusion of analytics insights in decision making. He also wants to see objective performance measurement in place, providing feedback on the value added by analytics, as well as that of all other functions in the business.
When the sponsor of the analytics function has an incentive to see analytics succeed, and deliver real business value. What are the sources of such incentive ? Usually, this is because the sponsor has “skin in the game”. This is the best and most rational incentive. When the sponsor is to some extent an owner, and committed to the success of the enterprise for a long period, then business objectives can override any conflicting or otherwise unhelpful career agendas or politics.
The sponsor with Appropriate Incentive protects and nurtures their team, weathers any pushback from the rest of the business, and keeps unhelpful influences from IT and other stakeholders at bay.
My personal filter for the ideal sponsor : “first of all, is this sponsor an owner”? Owners almost always have their interests aligned with enterprise success, and have in the bargain the Appropriate Empowerment to make sure that the right things happen. They thus almost always have two of the three elements of the Trinity in place. An owner with Appropriate Understanding is therefore someone who almost always has the entire Holy Trinity in place, and is thus my ideal consulting client.
Much and perhaps most of the analytics we see discussed publicly is practiced by people working in large organisations, where sponsors are employees rather than owners. Indeed, most organisations one encounters on data analytics blogs, or at conferences meet this description.
There may however still be corporate and government employees, senior managers and executives with mandate and budget for analytics who also have Appropriate Incentive in these situations. These are not as common as one might like, but they do exist.
Their Empowerment is not as great as that of owners, and their incentive might not be as perfect, but both are sufficient. These are people who usually for intrinsic reasons, be it a passion for analytics, or a personal set of professional values are able to transcend bureaucracy, cultural inertia and the political friction that successful analytics can create. These people are not easy to find but great to work with. Often, any minor shortfalls in Incentive and Empowerment relative to owners is offset by their deep Understanding. While an owner with the entire Trinity is ideal, they are rare enough due to a frequent shortfall of Understanding. Corporate executives with the Trinity are good enough, and the source of their Incentive can be an additional strength. They are inevitably charismatic, intrinsically-motivated people, able to inspire their teams to do great things. Further, the political backlash such figures can create can work to the advantage of analytics teams, creating greater team cohesion and motivation as they rally around their leader.
A sponsor starting out in analytics is incentivised to get informed, and to acquire more Appropriate Understanding. They may start with just enough enough Appropriate Understanding to know what they don’t know, and to realize that they need to learn more. they also know that to learn they must experiment, to consult with thought leaders in analytics and to grow their Understanding. They thus have the Appropriate Incentive in place to first of all determine what they need to learn, and are not afraid to be seen to be seeking advice, experimenting and constructively learning through failing .
Once they start building the analytics function, good sponsors have an Appropriate Incentive to hire the people who will do the most effective job, rather than the cheapest, those that look good on paper, those that will be the most sycophantic or those they have been forced to absorb as part of byzantine corporate quid pro quo. Appropriate Incentive means that the usual egoic or career incentives do not enter consideration in the construction of an analytics team. Indeed, most of the criteria used by HR departments need to be challenged directly : good analysts are seldom what HR considers to be model employees. A sponsor with Appropriate Incentive will not knuckle down to HR, and will not allow bureaucracy and politics cripple the effectiveness of an a alive function. They will select their own staff, usually through their own networks.
Once the team is in place, the sponsor with Appropriate Incentive supports them in their work, ensuring that they get all the data and tools they need (although “want” is not the same as “need” ). The Sponsor will ensure that the team is not mismanaged or otherwise subject to unhelpful stakeholding from IT or any other part of the business whose involvement should be minimized. Indeed, the Sponsor will be the effective Director of the team, with team leaders reporting directly to him, whether formally or otherwise. He will also be the number one consumer of analytics insights. Whether the team is inherently a strategic or operational analytics team, the sponsor will be the first recipient of high-level insights, which he will communicate to peers and superiors, winning more support and demand for analytics in the business.
This Sponsor supports an exploratory, agile approach to analytics, however it might be unpopular to IT and related mainstream project management / business analysis functions. (Yes, many enterprise IT functions do seem to be “converting” to “agile”, but this in name only. Actual corporate agility is as disruptive and nonstandard as ever). They also spend money appropriately, and have no inclination to spend money on expensive vendor tools until they are 100% sure that they can’t make do with commodity and open source. No amount of vendor or senior pressure will change their minds. This is because money wasted on expensive tools is money that could have been spent more wisely on good people, good coaching/training, perhaps even good data or cloud capacity. Now that’s Appropriate Incentive for you. On the other hand, if they do see a real need for an expensive vendor tool, they would know exactly the tool they need and no amount of pressure will make them buy another, less suitable tool just because it has the right political backing or marketing.
Incentive Failure Mode
What happens when there is Appropriate Understanding and Empowerment, but no Incentive ?
The failure of Appropriate Incentive can be one of degree, or intent. A failure of intent means an active interest in preventing or undermining the creation of an analytics function. The other option is less sinister and more mundane : the sponsor simply has other priorities, and there are political pressures in place that do not allow for a perfect, or even adequate analytics function to emerge.
The failure of intent is the most interesting. What if an executive has full understanding of what analytics can do, and how to bring this about, and also has the power to make this happen, but realises that this is not in their best interest? Can this happen ? Yes. Current power structures are not supported by objective measurement and the ability to bring any number of skeletons out of electronic closets at a moments notice. Effective status affiliation, conformity, credit taking, blame shifting and fad compliance have raised many power brokers to where they are today, and possibly into a position where they could sponsor an analytics function. Some of them may realize that analytics is in fact detrimental to their gravy train by introducing objectivity, rigour and resulting ongoing change. Data analytics can make people accountable or obsolete. Worse, it can affect allies and other key connections in the same way, disrupting power support structures. The resulting complexity and ongoing change is not going to be popular with everyone, certainly not with those who have traded so successfully of their “soft skills”. Indeed, the “Dark Triad” (another trinity ?) of Narcissim, Machiavellianism and Psychopathy is over-represented at the lofty heights of many organisations and probably not helped by effective analytics.
So, armed with the knowledge of potential consequences of effective analytics, and the budget, power and mandate to grow a function, what are the options ? If one welcomes this brave new world, and wants to build a world-beating organisation, see the description of the success mode above. If not, we have a somewhat different situation. Perhaps the would-be sponsor gently ensures that the analytics function does not emerge at all. This is a risky strategy, because it could after all emerge somewhere else, this time out of the misincentivised sponsor’s control.
Better to grow it, but make sure that it does no harm, by keeping it well away from the business, filtering all its communications and limiting its growth and,more importantly, its impact.This not a problem for a misincentivised executive, they are probably in charge of far more important and lucrative things, and the analytics function can be passed to a subordinate for baby sitting. This subordinate is best one with perfect loyalty and minimal imagination. Risk : managed.
There is a more common version of this scenario, where at the beginning the sponsor has poorer Understanding but better (though still far from ideal) Incentive. As a particular kind of executive they have made a career of (pretending to) excitement about buzzwords and fads that they frankly do not understand and see analytics as yet another bandwagon to jump on. The key with all of these fads from the sponsor’s perspective is that they grow your reputation while remaining Mostly Harmless. They do not see amy impact on the business, certainly not one that impacts them personally. Unfortunately, as the analytics function develops, and causes the inevitable shockwaves of inconvenient truth, transformation and unease, the executive starts to Understand more, perhaps all too well, and this be Incentivised less. The analytics function in this situation will find itself orphaned of appropriate support, “restructured”, neutered by mismanagement and probably wound down. I have seen a number of examples of this, you may have too. Readers are encouraged to comment particularly on this point and share their experience.
A related failure mode of changed Incentive, followed by the orphaning of the function, is the situation where the Sposor sees a temporary ally in analytics, usually at the expense of some other executive. Analytics is used as a weapon to unmask the weaknesses of some other individual, to promote the sponsor’s career. Once the deal is done however, the sponsor may leave analytics where he found it, or, worse, cripple it somewhat to ensure that karma does not rebound.
The other failure mode, the one of degree, is more common. The sponsor cares, but not enough. The sponsor wants analytics to thrive, but he doesn’t want to rock the boat. The sponsor is Appropriately Empowered, but wants to stay that way, and thinks he might not if analytics really flies. He isn’t CEO, Owner or King. Sadly, The outcome here is not too different from the cases above. The only difference is that perhaps the analytics function was created to be “Mostly Harmless” from the start, no “restructuring” required. The positive here is that some sponsors start this way in stealth mode due to insufficient empowerment, but use analytics to grow their clout as well as that of analytics. This is however more a failure of Empowerment than Incentive and will be explored further in the next article.
Isolation Mode
The Isolation Mode of Appropriate Incentive is the situation where it is the only member of the Trinity present. The trouble here is that not much can happen without Empowerment, and knowing where to start without Understanding is quite tricky. Nevertheless, with Incentive alone one can learn. A would-be sponsor of analytics can ask experts, attend courses, read books, hire trainers and coaches. You can download R or Weka, and try your hand at a Kaggle competition. I meet people every week who seek Understanding and find it, because they have the right Incentive. I have also guided new analytics functions with plenty of Incentive, less than enough Empowement and no Understanding through to success and growth. It can be done.
My advice for any sponsor in the Isolation Mode : step 1 : get Educated. Step 2: keep learning. step 3: never stop, but start doing stuff too, experimentally.
Step 4: you’re still learning, right ? Now grow the team.
Once both Incentive and Understanding are in place, a sponsor with budget and mandate can grow Empowerment in “Stealth Mode”. But that is for the next section on Appropriate Empowerment, the final one in the series.
The previous article introduced the idea of the “Holy Trinity” : the three key characteristics of analytics sponsors. These go beyond having budget and mandate to perform analytics : while those two raise an individual to the title of “sponsor”, the Trinity determines whether the sponsor is a good one. The “goodness” of a sponsor is defined by their analytics function delivering actual and recognized value, and thriving on those terms.
The Trinity consists of Appropriate Understanding, Appropriate Empowerment and Appropriate Incentive. The current series of articles explores each of these. We will examine what success or failure of each element looks like. We will also explore the cases where only one element of the Trinity is present, and, the direst of all, which is total Trinity failure.
For each element, we first examine the case where the sponsor has the entire Trinity in place, but we focus our attention on the element in question. This will be referred to as the “Success Mode” of that element. It will describe why that element of the Trinity is so important, playing well with the other two. We then examine the “Failure Mode”, the situation where the element in question is missing, even as the other two are in place. We then switch to the element’s “Isolation Failure” mode, which is the case where this element is the only one present, and the other two absent. Finally, after listing these for all three elements, there will be an account of “Total Failure”, where all three elements are absent.
Trinity Element I : Appropriate Understanding
Success Mode
Successful understanding means that the sponsor knows what to do in order to create to create, support, protect, nurture and grow an effective analytics function. That sponsor can evaluate recommendations and pitches from consultants, vendors and internal stakeholders to the analytics function, and make effective decisions to further the growth and success of the function.
Such a sponsor understands the importance of both effective IT support and IT non-interference in the analytics function. He understands IT’s role in the provision of sandpit environments, and easy access to open source and commodity tools and all relevant data. He also understands that once data is provided and systems are in place, IT’s main role in analytics is to get the heck out of the way.
The understanding sponsor can manage their analytics team, understand issues raised and recommendations from analytics team leaders and can direct those team leaders effectively to achieve required results.
The understanding sponsor of a strategic analytics function is their number one client as well as a thoughtful, reflective and demanding consumer of their analytics product. He understands that decision support is not decision replacement, and that he has a vital value add to the process, which is to make raw information actionable. He understands that good BI makes decisions better, but not easier. Indeed, good BI is voraciously consumed by good decision makers, even as it is rejected by poor ones as “not actionable”. He actively builds growing support and demand for BI product among his peers, and drives a culture of objectivity, empiricism and accountability within the businesses.
The understanding sponsor of an operational analytics function realises that operational analytics is difficult, and that there are no shortcuts to key components, regardless of what software vendors may say as they beat at his door, and those of his superiors, as well as the CIO’s. He knows that data must be cleaned, processed, prepared and no magic tool does even 50% of that. He knows that there are human components to the operational value chain, from data collectors at the coalface, to IT/DWH as data providers / data bottlenecks, to human executors of analytics-driven operational directives. These people need to be won over or otherwise directed to operate as a smooth, flawless machine, otherwise the benefits are not realized and analytics often takes the blame. He realises the need for appropriate measurement of effectiveness, and the frequent absence of this as applied to the analytics-free status quo. He realises the need to decouple measurement of effectiveness from analytics itself in the eyes of less understanding executive peers and stakeholders.
Finally, the sponsor in the know understands the potential consequences of successful analytics. He knows that an objective performance management culture, and a strong decision support culture favours proven performers and intelligent decision makers, even as it exposes sophists, credit takers and artful persuaders. He realises the cascade effect this can have on the entire executive class, and spillover to the board, shareholders or equivalent stakeholders in government or NGOs. He also understands the expected subtle efforts to derail analytics for precisely these reasons, and knows ways to counter them.
This sponsor is a very rare beast to say the least, but they do exist, their teams thrive and their organisations reap the benefits of analytics.
Failure Mode
This is the case where the sponsor has all the best intentions, at least as far as he understands analytics, and the power to make the function work, if only he knew what that entailed. Unfortunately, in this case, it is lack of understanding which lets analytics down.
This failure mode is more common in tech startups and small privately owned companies where the sponsor is the owner, and thus has all the best incentives and mandate to act, but nevertheless gets lost as to where analytics actually fits, how it could help, and what might be required from the sponsor to make sure that analytics delivers value .
The most common gap in understanding in small owner-managed companies is the commonly held view that analytics is part of IT and resembles it in skills, focus and practice. The fallacy that analtyics is IT also helps in throwing analytics acquisition in with the broader IT acquisition stack, with strong influence from the CIO, resulting in unhelpful IT management and practice methods applied to analytics, usually staffed by people chosen for their IT-ish skills, and spending most of their time doing IT-ish things like coding. The analytics is IT fallacy is not helped by those software vendors who are all too happy to perpetuate it, the better to get people to spend money unwisely.
Even more fundamental problems can arise when executives or business owners cannot grasp the difference between “technical” (esoteric detail best left to specialists) and “strategic” (important issues for the executives themselves that cannot and should not be outsourced or delegated). All too often, anything that is not understood, and anything that required painfully rigorous thinking as analytics does, is relegated to the “technical” bucket, even when the issue is actually of utmost strategic importance. Important questions like “what kind of decisions do you want this report to support?” or “are you really asking for a forecast, or it is more like our agreed targets ?” or “what do you want to do with customer segments?” are often met with puzzled, impatient stares and the questioner relegated to the technical bucket along with the questions.
My analogy here is cars, particularly taxis. The construction and repair of a car is clearly technical. What about driving skills ? These a higher order skills, but still, these can be outsourced to a taxi driver. Now consider the situation where the executive climbs into a taxi, and the driver asks “where do you want to go?”. Now imagine an incredulous executive saying “how would I know ? I know nothing about cars. don’t bother me with technical detail. This is something that you should be taking care of. And above all, make sure you make me look good”.
Ridiculous as this analogy sounds, it is a good picture of what happens when the sponsor of analytics suffers a catastrophic failure in understanding. In this case, they “make analytics happen”, but aren’t entirely clear why or how.They put the people and software in place, perhaps with some very vague directives, and expect the ill-defined “analytics thing” to happen, whatever that may be. The failure of understating goes beyond not knowing what the “analytics thing” is, to not realizing that that knowing this could perhaps be useful, let alone vital. Most vital knowledge that the sponsor should have is an “unknown unknown”. The only upside in his case is that the sponsor is happy, confident and unperturbed, unaware that anything should be wrong. If you count that as upside.
Another symptom of a failure in understanding is an eagerness to reach for magic solutions and “best practice”, as promised by certain software vendors and consultants. The belief that analytics is IT helps vendor business models that prey on waste and ingnorance. If an executive, unaware of what they really need, is willing to spend millions on “analytics in a box”, that is just fine with the software company. If an executive wants “analytics best practices” put on place by junior process workers, or predictive modelling offshored to Cheapworkerstan, there is always a vendor ready to collect the money. Such a vendor may be quite indifferent to any debacle of error, waste, stagnation and failure that may emerge years later. Even more likely, the vendor is mot concerned that money would have been better spent on good people, that much difficult data plumbing work is in any case unavoidable and not helped by million dollar software and that free software would have been good enough to begin with. The understanding gap is certainly helped by an incentive gap when it comes to spending money on all the wrong things.
Extending the taxi analogy, failure in understanding often reaches reflexively for “best practice”. Not many people catch taxis asking to be taken to a “best practice” destination. Doing this with analytics is usually just as inappropriate and downright surreal, although it happens much more commonly. It helps that taxi drivers don’t usually encourage this kind of behaviour. Consultants and vendors however are often less shy.
Isolation Failure
The remaining issue to consider is the opposite failure mode. This is the situation where understanding is present, while incentive and empowerment are not. What happens if the sponsor has a very good idea of how to make analytics work, but no real interest in doing so, and no real mandate even if they did ?
Often, the lack of mandate is the very thing driving the lack of incentive. Sometimes there are other agendas – understanding analytics can be precisely the reason to undermine or derail it : after all, analytics makes people accountable, possibly obsolete and forces them to operate in a complex, ever-changing world. Some might think that it is best to kill it, and most likely this is done by the one person that knows that analytics is more than some ill-defined buzzword. Often, killing or derailing something like analytics is far easier than nurturing and growing it, so all it takes is a bit of understanding of what analytics can bring to people’s careers and accountability, along with very little empowerment and all the wrong incentives, arising from being the kind of worker that would not cheer for an analytics-empowered world. It would be naive and false to say that there aren’t such people or roles within organisations, however “negative” this truth may be.
Other than that, what is most likely to happen when understanding in supply but incentive and empowerment are not ? The answer is, usually nothing at all. Lack of incentive need not mean a destructive attitude to analytics, it merely means that the are other priorities, and given no empowerment, there is little bandwidth to meet them. So analytics languishes, if it exists at all. Perhaps a single analyst or small team is hired as an afterthought, their activities uncertain and their morale low. Data acquisition has to be painfully negotiated with IT and other stakeholders on an ongoing basis. IT has a very unhelpful say in what systems, tools, process and skills are in place. The team performs at best a rudimentary ad-hoc BI function, at those rare moments when someone actually cares about what is in the data. Most such reports are generated for compliance and similar external reporting. The one upside is that usually when the is no empowerment or incentive, the team finds itself using open source tools. This is not really an upside, nor anything resembling an Analyst First operation. Such functions can sometimes be found in smaller government agencies or NGO. They are particularly common in QUANGOs. Sometimes they are surprisingly well funded too. Interestingly, these functions can survive for years. These are often the people telling me sob stories at conferences. I usually tell them to get a new job.
The greatest opportunity for analytics is in privately owned, owner-managed organisations where understanding is the one missing element of the Trinity, and great value can be realized once this gap is closed. Even better, this sector is not, as great an opportunity to those software vendors who prey on ignorance, which is just the absence of appropriate understanding.
As a final note, it pays to remember that failure in sponsor understanding is the one easiest to fix, although “easiest” is not the same as “easy”. Perhaps a better word is “feasible”, whereas failures in incentive are impossible to fix, and failures in empowerment practically so as well. A sufficiently incentivised and empowered sponsor can and should educate themselves, and make that education a key part of the creation of the new analytics function. Hopefully, they understand at least enough to prioritise improving their understanding. I have been privileged to assist a number of sponsors in precisely this activity, with very satisfying results. Indeed, the bridging of the sponsor’s understanding gap can and should be the first step of any new analytics function.
This was but the first of three essays, the next one will explore failures in Incentive, which are the most damaging and irreversible of all.
Today’s post is from Klaus Felsche, subject of a recent Analyst First Interview on the creation, development and live deployment of an Analytics function at the Australian Department of Immigration And Citizenship (DIAC).
At a recent Analyst 1st meeting in Canberra, it was suggested that I offer a few thoughts about ‘data quality’ as a thought-piece. Here are some of the thoughts:
There is considerable discussion around ‘data quality’; and so it should be. Unfortunately, ‘quality’ is a qualitative and, at time, emotional, assessment of whether the data we have supports analytics. Some, after a quick look at available data, throw in the towel and abandon any attempts to make the data work.
I am inclined to abandon the word ‘quality’ as it tends to under-value the capabilities of skilled analysts and sophisticated software and leads managers to rash judgments about what is and what is not possible. It is far more constructive to consider whether the given data is able to support our processes with sufficient accuracy to be meaningful. In other words, is there enough value in the data to be fit for service?
The Situation: Data is rarely collected to support analytics
We tend to have data that is collected to support a business process. Even if analytics is considered at the initial design stages, by the time we need to get the data to provide answers, we would generally find that these new questions were probably not anticipated years ago when the project defined the current data structures and processes.
The challenge for analysts is, therefore twofold: firstly the analyst tries to build models that address the issues with the highest degree of accuracy from the available data; secondly the analysts compiles a ‘shopping list’ of data that would enhance the process if it were available. The ‘shopping list’ can be provided to management to ensure it is considered in future redesigns.
There will be times when even the smartest analyst cannot squeeze the answer out of existing data sets or sets outside the organisation that may be available.
While we wait to get more useful data analysts may be able to help the organisation build interim solutions based on what is available (eg intelligence reporting, business intuition and knowledge, etc).
Lessons Learnt
• Avoid the term ‘quality’. It assumes that there is a low or high quality data set. This is not helpful. We should probably focus on how well the data supports the analytics processes. As far as business operations are concerned, if the data processes support the core business functions then there is little wrong with its ‘quality’.
• Start analysis from the ‘data end’ rather than having in mind a business (intuition or anecdotally or experientially – derived) model. While these can be useful, measuring the data against such models and then making a call that the data is ‘not good enough’ in some way is not helpful. Analysts should be given the opportunity to test a range of methods to see what meaning can be drawn from the existing data first – in my experience, analysts have been known to produce pleasant surprises when given the chance and the right tools.
• Ensure that there is a business process in place (or build one) that can feed analysts’ suggestions back into future systems enhancements.
• Many vendors offer an end-to-end solution (everything from data capture to storage to analysis and reporting). Such systems would need to be sufficiently flexible to support changes over time to support changes in data structures, collection processes and tools available to support analytics processes.
• Educate the business areas to create an awareness of the value of data:
o enhanced data sets; and
o business processes that better support analytics (ie convince operational staff or clients entering data that there is value in timeliness, accuracy, completeness, etc).
Technology Spectator recently published an article highlighting the need for big data speed …
This article highlights the communication challenge for accredited A1 professionals.
We all recognise Analytics is about using information better than competitors, so we are: 1. doing things better, and 2. doing better things than competitors/relevant comparators. But like so much of the coverage of our sector, the article focusses solely on Operational Analytics, not the latter area of Strategic Analytics.
Secondly, the article fails to recognise speed is only one part of the equation.
Taking the author’s example of the retail sector, sure real time analytics can detect an early decline in sales for a particular product, controlling for some extraneous factors. But a retailer’s promotional response (who they target and how) doesn’t necessarily require real time analytics (they can apply in real time outputs from models created last week, with little risk of degradation).
The most important questions for shareholders of the retailer require Strategic Analytic capability: how should pricing across the entire product portfolio be optimised?, what products should we be ordering now for next season (or the season after)?, how to optimise the physical network and supply chain? These strategic questions demand the right answer, not necessarily the fastest answer.
Any experienced industry professional gets that making sense of data is our primary role. But clearly interpreting data to the best of our ability flies in the face of throwing away information (e.g. because inconsistencies in the available data makes the task more cognitively complex). No one would advocate storing and processing data which possesses no incremental information value, but information value can be measured, so that shouldn’t be an issue.
Critically this article fails to recognise many of the barriers for Australian companies in effectively using their data relate to data quality, not their data storage and processing capacity.
Finally, there is no explicit recognition of the talent required to use data more effectively than your competitors.
From an A1 perspective we should welcome the growing focus on our sector, but we need to better articulate the more nuanced (and interesting) story of Analytics in an A1 Practice. It would be easy to criticise the journalist for being naive in swallowing the line of vendors and other vested interests, but the responsibility is ours to better explain the reality.
Eugene is totally right that we need to stand with a united voice. From today, NTF with publicly back A1 in all our proposals and marketing collateral. I regret not taking this action sooner.
Continuing with the big data meets big hype theme:
So you want to get into Business Analytics/Big Data/Predictive Analytics.
What areas, skills, tools, data should you focus on first ?
There are three rather big questions that you need to ask yourself:
1. How well do I really understand the problem(s) that I want Analytics to solve, and The roles(s) that Analytics would play ?
2. How well do I understand my data?
3. What data do I actually have, or can get ?
Each question explores a continuum. Together they represent a three dimensional space of possibilities. There is no “magic quadrant” here, each part of the space is a legitimate place to be, with its own solutions, risks and benefits.
Let’s go through them.
1. The range of possibilities looks something like this:
A: having built preliminary offline random forest models and created some prototypes, I want to extend these existing customer acquisition and retention models we have to our intentional markets, and operationalise them for real-time, event based activity, provided this is seem to yield further significant yield. We will need an industrial strength, scalable, and reliable tool, probably a commercial vendor tool, and possibly a Hadoop-based MapReduce solution
B. my CEO just attended a lavish conference where he saw a slide presentation mentioning the Davenport HBR article from 2006 and now he wants us to “get into analytics”.
Most people are somewhere in between. But you get the idea. And there are far too many initiatives that are precisely at B. the ideal vendor customer is precisely at A. Unfortunately, there are not enough A’s around (we call them “Eduacated Buyers”) so some vendors must sell to people who look more like B’s.
Naturally, Analyst First does not advise Bs to get into Big Data, buy expensive vendor tools, or ever believe anyone that there is such a thing as “a solution for getting started in Analytics” especially when said solution is no more than a bunch of software and maybe a few relatively junior technical consultants for a few months.
Indeed, we advise the Bs of this world to invest in learning, exploring and gaining experience, while managing their sponsors’ expectations and growing their personal investment and participation in the new Analytics enterprise (yep, it’s an Enterprise, with all the Lean Startup that entails), and eliciting from said sponsors their real, and realistically achievable needs.
This is a crucial time to invest in smarts, experience, talent, learning and plenty of Lean Startup.
If this approach is not feasible, I do not have high hopes for the future of the function, which will, at best become a showpiece trophy of high tech adding no value, and will more likely be shut down, “restructured” and restarted again, hopefully with a more sensible approach.
And what of the As ?
Speaking to an A recently, indeed one of the best As I know, he noted that his team had kicked some great business goals recently, having implemented a very necessary expensive vendor tool, after trying R and seeing that it was not up to the big data / big crunch job they had to do. He noted that this was necessary, even though he agreed with A1, and that this was not in line with A1′s preference for open source tools.
“not at all”, I replied, “This is exactly A1, you were the quintessential Educated Buyer! A1 is not against vendor tools. We are against people spending money on what they do not understand in the hope of a magic solution. You don’t fall into that category.”
Hopefully, the anonymous A in question will write a more detailed post on this blog, outlining his success story in more detail.
So, our advice to As is… You don’t really need our advice, until you want to do something new again. In which case, chances are you are following A1 principles already, explicitly or not – otherwise how did you get to A in the first place,anyway ?
Most people are somewhere in between, and usually closer to B than to A.
Answering the “what the heck are we going to do?” question involves exploration on a number of axes, including stakeholders needs, own capability, available resources (human and electronic), any impediments or constraints (Hello IT!) and data, the subject of questions 2 and 3. The actual hidden contents of the data, the “gold” of the data “mining” metaphor is a huge exploratory subject in its own right, and must be considered in the context of the others.
This is not a very easy target to hit, and needs defining before that can happen !
So, to all the Bs and almost-B’s out there : invest in learning : invest in your own and your sponsors’. Invest in getting your sponsor invested, supporting and covering you, letting you explore and grow. Invest, above all, in exploration and invest in managing expectations and delivering intermediate ressults to allow all this to happen. Buy your analytics function a chance to grow, learn, explore and breathe free of unreasonable pressures and constraints.
The other two questions will be covered in upcoming posts.
Last week I attended a very interesting IAPA panel discussion in Canberra, organised by Peter O’Hanlon, head of the IAPA ACT chapter. The panel discussion was lively, informative and controversial, exploring as it did the often difficult relationship between Analytics and IT. A1′s very own Stephen Samild was one of five panelists. Peter did a great job of facilitating, and all five panelists made some great points. People in the audience also pitched in with interesting questions and reflections on real-world experience.
The conversation continued to return to a central topic, one that lives in the murky grey area between the two functions, and acts too often as a political football. I speak of the instantiation and deployment of Analytics outputs to IT systems. This essential activity, often referred to as “operational analytics” is the source of much confusion, conflict and business failure. Much of the trouble arises from poor fundamental philosophical distinctions which have arisen historically. These lead to unhelpful naming conventions and political turf demarcations. To explore the issue is to re-examine some fundamental definitions and distinctions. The first task is to ask what do we mean by Analytics. Two possible definitions might be:
- Any electronic manipulation of large amounts of data.
- Any exploratory analysis of data that results in information leading to innovation or insight.
Definition 1 covers both the quest for insight and its deployment and operation in an IT system. Definition 2 covers only the former. Which definition is preferable?
The operational step itself consists of two steps, which is the deployment of an insight (e.g. a predictive model) and the ongoing monitoring of its effectiveness.
Reasons for preferring definition 1, which places both steps within the Analytics realm, include the following:
- “Operational Analytics” has the word “Analytics” in it
- There is data crunching involved. Isn’t that what Analytics is?
- There is model evaluation/monitoring involved. That is stuff only Analytics people do, right?
- Historically, this has been stuff only the Analytics people cared about.
- The software that does all this stuff comes from Analytics providers.
There are however some solid counter-arguments to these:
- Could this just be an unhelpful and confusing historical accident?
- There is plenty of data crunching in payroll, accounts payable and other operational systems that few would think of as Analytics.
- Monitoring and evaluation should be applied to a lot more than just predictive models. In particular, it should be applied to any business process that Analytics would seek to improve. This is Performance Management and Business Intelligence, but hardly Advanced Analytics. While this kind of measurement is often seen as part and parcel of Analytics, there is no reason that the two need go hand in hand. The extent to which they do is an artefact of history, and a reflection of the poor penetration of empiricism and appropriate performance management across business generally.
- Historical accident is no reason to maintain a coupling of what are fundamentally different activities.
Naturally, there may be counter-counter arguments, and I invite readers to raise them in comments.
To argue for the narrower definition of Analytics is to demystify “models”, and to demonstrate that an operationalised predictive model is no different to an operational accounting system. The argument is simple:
- Both deal with potentially large data sets.
- Both apply a range of rules, consisting of if-then-else conditions and arithmetic.
- Both produce outputs to some workflow.
And that is it. The emperor has no clothes where actual models are concerned: a predictive model is little more than a bunch of if-then-else logic and arithmetic. These rules can be read and deployed by IT staff. Indeed, it is not important to know where the rules came from, be it a Support Vector Machine or human defined rules laid down by the CFO.
The magic of Analytics lies in its ability to find the right set of rules. The rules themselves are not that complicated in comparison to the learning algorithms that find them. My favourite analogy here is the needle and haystack problem. A metal detector would be handy here, and is arguably a very sophisticated tool compared to the humble needle. The detector makes sure you end up with the needle and not just hay. Once found, you notice that the needle is a rather simple yet valuable tool, and one that can be put to work sewing. So far, so good. You might also agree that looking for metal and sewing are somewhat different tasks and that the metal detector guy can now go off and look for more needles in some other haystack, or for gold. Putting the needle to work sewing is a completely different skill for someone else.
The broader definition of Analytics creates commonality between sewing and metal detection. The narrower definition accepts that any such commonality is neither necessary nor natural. So historical baggage aside, there may be an argument that insight and innovation generation is the business of Analytics, while the operational deployment of business rules is the province of IT, as might be the ongoing monitoring of the effectiveness of such systems.
There are then counter-arguments to this distinction. These rely on specific definitions of the words “exploratory” and “deploy”. Both are to a large extent a misunderstanding of terms rather than a true disagreement, but they can naturally lead to a preference for the broader definition of Analytics. Political factors also come into play. Again, the counter-arguments are on good footing with respect to history, but may lead to unhelpful category errors.
First of all, the word “exploratory” raises the hackles of many an Analytics manager. This is because analysts are by nature explorers, and rightly so. Unfortunately this can be taken to extremes, and a small but conspicuous minority of analysts are always at the ready to run off into uncharted waters, performing analysis of questionable or nil business value, treating their job like an open ended research project/video game, and perhaps violating a number of principles of science, reason and IT security in the process. While actually rare, this approach to Analytics is memorable enough to give exploration a bad name, especially among people in business not used to scientific inquiry.The good news is that pathological exploratory behavior is a small and manageable problem. It can usually be turned around by more attentive supervision, incentives and leadership.
There is also a cultural issue clouding an appreciation of exploration. Managers accustomed to process, best practice, and clear objectives often have trouble distinguishing dysfunctional exploration from more productive kinds. Further, they may have trouble identifying the successful performance of Analytics in an exploratory context due to the unexpected and seemingly random nature of outputs, as well as the need to interpret, evaluate and implement them before value is realised.
Analytics management based on a conventional, deterministic IT project management model is perhaps more common. Traditional project managers may not perceive exploration as delivering any value, and may share their concerns with others in the business. In this way exploration may earn an undeservedly poor reputation. Again, this understanding is in the minority—a shrinking one—and is being steadily replaced by more appropriate agile and Lean Startup approaches. And, once again, it’s a problem easily rectified by acknowledging the uncertain, exploratory nature of Analytics, and ensuring that the sandpit function is not led by traditional project management approaches, nor incentivised according to deterministic KPIs.
The very rare combination of the two pathologies is a perfect storm and a recipe for failure, but even then not irredeemably so. The management issue is the first one to fix in this case, and the analyst issue will either fix itself, or benefit from new resources.
A related argument is a political one, mindful of the organisational status of a unit that “only” does “exploration” as opposed to something “real”. This is certainly a cultural issue affecting many organisations, but there is no reason to take it as a normative argument for how Analytics should be defined in an ideal organisation. At best, it is an argument for a temporary arrangement that may allow Analytics to prove its true worth to the organisation and hopefully rearrange to a more logical structure at a later stage.
A related issue is one of deployment: the argument that for an insight to be valuable it must be deployed. The usual implication is that only Operational Analytics is of value. This is not an argument against the narrow definition of Analytics. Rather, it suggests that the business of Exploratory Analytics is entirely the creation of business rules to deploy in IT systems. The counter-argument here is not so much disagreement as a broadening of the definition of “deployment”, “data” and “IT”. If by “IT” we mean the brains of senior executives—“data” can be unstructured, graphical or tacit (e.g. verbal), and “deployment” can include sharing insights by word of mouth or PowerPoint slides—then there is actually no argument.
Take a predictive model as an illustration. While the model is a valuable operational rule set when deployed on an IT system and let loose on giga/tera/petabytes of data, it is also a valuable summary of behaviour—indicating key drivers, leading indicators, and interactions from which behaviour can be inferred. Such insights are valuable to executives, but not as business rules. Their “deployment” is largely manual and one-off, often requiring additional explanation and visualisation provided by highly skilled statisticians.
Thus, Analytics is responsible for “deploying”, valuable, complex, unrepeatable strategic insights, while the simple, repeatable ones are relegated to IT. Note also that both sets of “deployables”, strategic and operational, can come from the same predictive model.
This completes an outline of a case for a narrow definition of Analytics, demystifying deployment and leaving it to IT, along with model performance measurement, and leaving Analytics to act as an innovation, insight and strategic intelligence function.
Related Analyst First posts:
The relationship between IT departments and analytics teams has at times been hostile. Why? Both have a strong focus on enabling business and have responsibilities for data and its use. There is an apparently obvious requirement to achieve alignment yet many organisations and departments have struggled to do so. In this panel discussion we will explore the basis for these difficulties, and hear some practical approaches aimed at overcoming them.
That was the subject of an Institute of Analytics Professionals of Australia (IAPA) panel discussion last night in Canberra, facilitated by ACT Chapter Head, Peter O’Hanlon. Alongside me on the panel were:
- Murray Alston – Enterprise and Solution Architect (Australian Customs and Border Protection Service)
- James Horton – Director Solutions and Strategy (EMC Greenplum)
- Warwick Graco – Senior Director Operational Analytics (Australian Taxation Office)
In preparing for the discussion I made the following notes:
For what does Analytics require IT support?
- Provision of analytics infrastructure—hardware and software
- Provision of data
What sort of Analytics are we talking about?
- Finding: Exploratory or “lab” analytics
- Building: Operational analytics, IT instantiations of the outcomes of analytics
Why the hostility?
I see two main causes:
- Misunderstanding: adopting the wrong risk management framework
- Misincentive: adopting a siloed, self-interested risk management framework
Both of these result in suboptimal trade-offs between risks and rewards. From the IT point of view it’s easier to imagine downsides than upsides when it comes to Analytics. Giving analysts access to large volumes of organisational data with powerful tools to ask complex questions raises all sorts of concerns about privacy, data security, network availability, application stability, and so on. These can be largely mitigated through matching the right electronic infrastructure to analytical activities—particularly, recognising that ‘Finding’ activities (innovations) can take place in an off-network Analytics Lab or ‘sandpit environment’ and are separate from ‘Building’ activities (instantiations). Then there are the risks of misinterpreting data, making mistakes in the analysis process, and misinforming decisions. These are worth worrying about, but it’s also important to ask about the unseen upsides. What about the risks of not doing Analytics? There are trade-offs, but I’d prefer the IT department that gives its analysts unfettered access to data and tools in a sandpit environment—with the caveat that it takes no responsibility for misuse or misinterpretation of that data—to the IT department which won’t release data to analysts for fear of blame in case they make a mess.
These problems aren’t unique to Analytics but it is the most pathological case. The problems scale according to the degree of reliance on electronic infrastructure and data, which is why Finance functions recognise many of them. Resolving them—that’s to say, ‘aligning IT and Analytics’—suggests an enterprise risk management function which sits above both functions and can weigh and trade off risks and rewards. Perhaps this is ultimately the CEO’s role.
Where does Analytics belong?
There are two dimensions here:
- Technical, in which sense Analytics sits “between IT and the business”
- Functional, in which Analytics sits “not between IT and the business”
It’s common to infer the functional from the technical: because Analytics uses lots of sophisticated software and analysts are technically savvy, it must be IT. This is a mistake. Analytics is done by analysts, but we never say that Analytics should therefore sit “between HR and the business”. Where it should sit in a functional sense will depend mostly on organisational culture and context. Examples include:
- Business Intelligence / Decision Support
- Intelligence and sensemaking
- Strategy
- Innovation
- Research & Development
- Knowledge Management
- but not IT
Related Analyst First posts:
Yesterday’s post contended that the default ‘IT vs business’ balance of power assumption is an unhelpful one for Business Analytics if left unchallenged:
It’s unquestionably the case that analytics doesn’t happen without software, but that’s just as true of accounting, graphic design, and most other activities conducted in front of computers in today’s workplace. It simply doesn’t follow that IT deserves, so to speak, a seat on the Security Council.
Back in July, on the subject of ‘IT support‘, I argued that:
To IT, “Business Analytics” is just another thing to be risk managed, and in this sense it’s no different to database backup, network security, ERP, desktop management, virtualisation, VOIP, or any other IT-reliant capability.
Managing risks in this context translates into either taking control or decreasing responsibility, and so IT’s support typically feels more like a mix of unnecessary interference and reluctant cooperation. This mostly stems from a genuine divergence in understanding, goals, incentives and defaults, although it’s sometimes experienced as ill will.
One of the simplest and starkest ways to think about this is in terms of a typical Request For Tender document. The most recent RFT I looked through was not unusual. It ran to 95 pages. Of these, only one—a mere 400 of 25,000 words—specified the substance of what was being requested. That is, more than 98% of the document’s content would have applied as equally to the development of an ERP system as to the purchase of a new fleet of motor vehicles. Most of it consisted of legal definitions, constitutional and structural information about the tendering organisation, contractual terms and conditions, and compliance and process information. Many of its sentences were self-evident statements along the lines of: The Contract Manager will be the officer who is responsible for managing the resultant Contract formed under the Deed and specified in the Contract. Responses to it, once received, will be divided up and separately assessed for local compliance by various organisational support functions: Procurement, Legal, Human Resources, IT, Finance, and the Project Management Office.
As a thought experiment, make a mental copy of this RFT document, substituting your own 400-word definition of a business analytics capability for whatever was there before. From the organisation’s point of view, how much has changed? Not much. This is of course an over-abstraction. There is no such person as ‘the organisation’, and as such, no organisational point of view. Nor is word count necessarily the best proxy for substance. But drill down. Consider IT’s point of view. For the purposes of risk management, how different is Business Analytics from any other black box which depends on information technology?
Related Analyst First posts:
Pattern-driven Performance: Should You Start with Tools—or with Talent? That’s one of the questions addressed by Deloitte at Real Analytics:
Companies everywhere are catching onto the wisdom of mining information for patterns of performance. Using a combination of advanced statistical tools and good, old-fashioned experience, they’re discovering and dissecting hidden patterns that can help guide their choices in operations, talent, technology, financial strategy, you name it. For those looking to drive performance in this way, there are two paths forward: start by investing in tools or start by investing in talent.
In Analyst First terms it’s a contest between human and electronic infrastructures. Deloitte frames it as a debate, presenting a set of rhetorical, stylised point / counterpoints to which a panel of its Directors and Principals respond. The case for tools first cites scale, automation, efficiency, transparency, and some degree of insulation from undesirable human subjectivity. It also talks up the scarcity of good people and talks down the difficulty of analytics. The case for talent first argues for the importance of business knowledge, an appreciation of context and nuance, interpretative skill, big picture understanding, the ability to ask the right questions, and the soft skills required to build cross-functional communication, coordination, trust and support networks.
Three out of the four Deloitte contributors prioritise talent over tools; the fourth elects both. As Janet Foutty, National Managing Director, Technology, Deloitte Consulting LLP puts it:
[T]here’s a big problem with the “buy technology first” approach: What if you’re not asking the right questions. I know it might sound strange coming from a person who leads Deloitte’s IT services, but I’m “talent first” all the way.
One of Analyst First’s key principles is our advocation of investing in the human over the electronic infrastructure. Simply recommending “both” is appealing, but the reality is that investment decisions are always taken at some margin at which a trade-off is being made, so “both” is never a real choice. A decision to spend any amount of money on commercial software is always a decision to not spend that money on alternative uses—such as hiring more or superior analysts. In comparing the marginal utility of commercial tools with alternative investments, the following should be added to the case put by the Deloitte panel, which further strengthen it:
- Although some instantiations of analytics are process-based, analytics itself is not a process.
- Most of the analytical tools any organisation will require, especially at the outset of its exploration of analytics, are readily available. Much can be done—and is being done—with the commodity tools already available on analysts’ desktops (e.g. Excel and SQL) and with open source tools such as RapidMiner and R.
- Many different tools exist—commercial, commodity, and open source—and sensibly choosing between them means becoming an educated buyer. This entails leveraging experimentation and experience—either in-house in the form of trial and error, or that of outside help.
- Prominent expenditure on commerical tools—while it may perversely benefit individuals—erects a number of barriers to organisational success.
- Tools without sufficient expertise are not harmless. In fact, they may act as risk multipliers.
This does not mean that the marginal utility of commercial software is always less than the marginal utility of analysts. This is of course possible. However, it is empirically the case far less than outsiders to Business Analytics—and many insiders—intuitively expect.
Related Analyst First posts:
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