“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.
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.
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.
I was sorry to read, via Gary Cokins, of the recent passing of Jeremy Hope, who along with Robin Fraser pioneered the Beyond Budgeting movement and co-founded the Beyond Budgeting Round Table (BBRT). As Cokins summarises:
Their basic message was that the annual budgeting process is so broken and dysfunctional that the best solution is not to reform it but rather to abandon the process altogether. Their solution was to understand the underlying purposes of a budget and apply methods, like driver-based rolling financial forecasts, that fulfill the purposes of a budget.
Having spent a good part of the last twelve years as an enterprise budgeting and planning specialist I have a great deal of sympathy for this view. The underlying purposes of budgets are rarely clarified and distinguished from each other. As I’ve written about before, this leads to much wasteful confusion, both practical and linguistic:
In reality the budget is a hybrid because it serves two main purposes. It sets performance targets (goal setting) and limits the resources available to those pursuing them (planning). Both goal setting and planning are necessarily reliant on forecasts, although these underlying objective estimates are not always made explicit. Updated plans and targets – they are commonly revised within a financial year – are often referred to as “forecasts”.
The enterprise budget is an odd and hybrid beast. Many of its perversities and pathologies are familiar to everyone who’s worked in an organisation: arbitrariness, inflexibility, unresponsiveness to change, incentives to game the system (underplaying revenue potential while overstating costs), encouragement of ‘use it or lose it’ spending, disconnection from strategy. Then there is its being expressed in the language of accounting, which is not the natural language of most businesspeople. Finally, there is the sheer complexity of its enterprise coordination—the annual ‘march of a thousand spreadsheets’. Most of this coordination effort is in fact completely unnecessary. The bulk of any organisation’s expenditures are preordained. They’re either fixed, or circumscribed by its balance sheet. The planning (resource allocation) aspect of budgeting is thus fundamentally a top-down exercise. However, its goal setting aspirations lead to an insistence that budgets be built bottom up, painstakingly, by individual managers. The idea is that this generates ‘buy in’. Typically, however, the bottom-up aggregations never conform to the top-down constraints, so they get overridden during the budget finalisation process.
Despite all of this, the annual budget remains stubbornly embedded in the workings of most organisations—more understandably in government, where it fulfills a legislated purpose, than in the private sector. I attended a seminar with Jeremy Hope in Sydney, from memory in 2004, facilitated by the Institute of Chartered Accountants in Australia (ICAA). I remember asking Hope why it was that adoption of Beyond Budgeting’s principles was relatively rare. It was notable that the practitioners featured in Beyond Budgeting’s case studies (companies such as Toyota and Svenska Handelsbanken) had been using it successfully for decades. If the good news wasn’t new, why such resistance? His answer, in essence, was that the status quo, although widely acknowledged as inefficient, was so familiar that dismantling it was literally unimaginable for most budgeteers. Disrupting it was a long and uphill battle.
Beyond Budgeting is to budgeting as Lean Startup is to entrepreneurship and Analyst First is to Business Analytics. Each movement takes a first principles approach to diagnosing, in order to do away with, a set of wasteful habits of thought and practice which result from convention and are sustained by poor incentives.
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:
Ted Cuzzillo, writing at TDWI and citing Blake Johnson of Stanford, identifies 6 conditions for [or barriers to] the rise of business analysts:
1. The best analysts are skilled in three areas: First, they engage stakeholders and have an eye for business opportunity. Second, they inspire stakeholders’ trust with consistently excellent analysis. Third, “big data” requires skill with data management and software engineering.
This paints a similar but not identical picture to Drew Conway’s Data Scientist Venn Diagram. The key point of difference is that Conway places more weight on mathematical and statistical training, which is not the same thing as “consistently excellent analysis”, but is more important than is often assumed in enabling it.
2. Each analyst’s skills should be about 80 percent in data management and about 20 percent in business and analytics — but Johnson expects that to change over the next five or 10 years as tools make data management easier. Eventually the mix of skills will be the opposite: 20 percent data management and 80 percent business.
I have no strong view on this, but my intuition is that data wrangling will always consume far more time and effort than analysis. Analysis is a feedback loop and a read-write activity. Standardisation and automation continue to consolidate efficiencies but these tend to raise the analytical bar. That said, I’d be happy for future tools to prove me wrong.
3. Gaining a foothold within an organization is best done in small bites with an entrepreneurial approach. Forget trying for a “big bang,” he says. Instead, find a need and fill it quickly, then move on to others. Identify and solve one business problem after another — always making sure to keep your methods scalable.
This agrees with Analyst First’s contention, seconded by others, that the monolithic IT project approach doesn’t work, and that—within an existing organisation—a bottom-up Lean Startup approach is your best bet. The only exceptions to this are analytic-centric online startups and quantitative hedge funds.
4. Location of analysts’ workspace matters. They should work in a cluster for critical mass, which encourages sharing of best practices and support. If they sit within business teams, their work becomes more visible.
This makes sense. Isolated analysts are a problem whether they’re isolated from each other or from management oversight and direction. Generally speaking, senior executives need to be broadened while analysts need to be narrowed. Middle managers need to be skilled up to bridge between the two.
5. It’s an adjustment for everyone — on the business side but especially on the IT side. It means fundamental changes in the way data is organized and managed, and accessed and used, with both new technologies and skill sets.
6. Many IT pros deny access to data based on obsolete knowledge. Johnson reports that many don’t know about modern load-balancing and other technology that make such access safer.
Certainly true. I’ve written before about the data needs of analysts as distinct from traditional business intelligence consumers, and also observed that big data is at once driving up the need for advanced analytics and rendering traditional data warehousing approaches obsolete. But the odd part about the commonly invoked ‘IT vs business’ balance of power is the acceptance of IT as a ‘stakeholder’ as opposed to an enabler. 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.
Cuzzillo closes well aware of both the future possibilities for Business Analytics, and the status quo political realities standing in its way:
You would think that both sides would sign up for the bargain the new middlemen [i.e. analysts] seem to offer. IT would cede control and concentrate on what it does best, managing the back end. Meanwhile, business stakeholders would get insights from these newly empowered, eager specialists. Analysts would be newly ready to answer business questions, conjure up new questions, and offer strategic options.
Analysts would colonize what had been the no-man’s-land between IT and business. Trouble is, the analysts may end up ruining the neighborhood for them. If the strategies Johnson suggests work, IT and business would find a new power growing alongside them. Analysts — simply from the position they would find themselves in, not from any wish to rule the world — would be indispensible, powerful, and well funded.
Who wouldn’t want that?
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All Analytics – The Community for Data Management, Business Intelligence, & Analytics – has invited Analyst First to argue the case for open source software in analytics as part of their Point/Counterpoint series. Our point post, ‘The Case for Commodity & Open-Source Analytics’ is here. The counterpoint post, ‘Downsides Dampen Open-Source Analytics’, by Ajay Ohri, is here. Beth Schultz’s introduction is here.
I encourage our readers to explore the All Analytics site and to comment on the debate (which necessitates free registration).
BBC News reports that ‘black swans’ are busting IT budgets. One in six large IT projects go over budget by an average of 200%, according to a recent Oxford University and McKinsey study, ‘Why Your IT Project May Be Riskier Than You Think’, published in HBR. This comes as no surprise when paired with Gartner’s estimates that 70 to 80% of corporate business intelligence projects fail. It’s interesting from a Business Analytics perspective, both because analytics projects are themselves software dependent—particularly at the operational end—and because project risk analytics are part of the solution.
The study’s authors, Bent Flyvbjerg and Alexander Budzier, describe IT projects as generating “a disproportionate number of black swans”. But one in six is not a rare event—it’s a single roll of the die. Their underlying research shows that decision makers are working with poor initial estimates of probabilities and maintaining them in the face of persistent error. Such widespread failure to readjust projections in response to disconfirmatory data is a signal that accuracy may not be the goal. That is, IT project management straddles the planning and goal setting domains, not the forecasting one. Robin Hanson wrote about the perversities of project planning and management in the Cato Unbound forum on expert forecasting:
Even in business, champions need to assemble supporting political coalitions to create and sustain large projects. As such coalitions are not lightly disbanded, they are reluctant to allow last minute forecast changes to threaten project support. It is often more important to assemble crowds of supporting “yes-men” to signal sufficient support, than it is to get accurate feedback and updates on project success. Also, since project failures are often followed by a search for scapegoats, project managers are reluctant to allow the creation of records showing that respected sources seriously questioned their project.
Often, managers can increase project effort by getting participants to see an intermediate chance of the project making important deadlines—the project is both likely to succeed, and to fail. Accurate estimates of the chances of making deadlines can undermine this impression management. Similarly, overconfident managers who promise more than they can deliver are often preferred, as they push teams harder when they fall behind and deliver more overall.
The primary KPI for large projects, it appears, is simply “completion”. Completion on time, on budget, and sensitive to changes in specification and priority appear to be at best secondary considerations. Flyvbjerg and Budzier cite additional research showing that 67% of companies failed to terminate unsuccessful projects.
But the model failure chronicled by the study runs deeper still. Projects don’t live in political or economic isolation, but planners act as though they do (from the BBC report):
“Black swans often start as purely software issues. But then several things can happen at the same time – economic downturn, financial difficulties – which compound the risk,” explained Prof Flyvbjer
Projects are being approached as though they’re engineering problems when in fact they’re complex systems problems.
The study raised concerns about the adequacy of traditional risk-modelling systems to cope with IT projects, with large-scale computer spending found to be 20 times more likely to spiral out of control than expected.
Size and complexity play critical roles. Flyvbjerg dispells the notion that this is a public sector problem:
“People always thought that the public sector was doing worse in IT than private companies – our findings suggest they’re just as bad.
“We think government IT contracts get more attention, whereas the private sector can hide its details,” he said.
The study’s concluding advice, given both the frequency and magnitude of project failure, is more reality-based risk management:
Any company that is contemplating a large technology project should take a stress test designed to assess its readiness. Leaders should ask themselves two key questions as part of IT black swan management: First, is the company strong enough to absorb the hit if its biggest technology project goes over budget by 400% or more and if only 25% to 50% of the projected benefits are realized? Second, can the company take the hit if 15% of its medium-sized tech projects (not the ones that get all the executive attention but the secondary ones that are often overlooked) exceed cost estimates by 200%? These numbers may seem comfortably improbable, but, as our research shows, they apply with uncomfortable frequency.
Even if their companies pass the stress test, smart managers take other steps to avoid IT black swans. They break big projects down into ones of limited size, complexity, and duration; recognize and make contingency plans to deal with unavoidable risks; and avail themselves of the best possible forecasting techniques—for example, “reference class forecasting,” a method based on the Nobel Prize–winning work of Daniel Kahneman and Amos Tversky. These techniques, which take into account the outcomes of similar projects conducted in other organizations, are now widely used in business, government, and consulting and have become mandatory for big public projects in the UK and Denmark.
In other words, take a more learning-oriented approach to project planning and execution, informed by simulations based on data from similar projects. Completion is by itself a dangerous goal. Risk-adjusted completion looks quite different, and like projects provide a better model than idealised and data free assumptions.
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The Analyst First view is that strategic analytics is in many respects easier than operational analytics. In part, operational analytics is hard because motivating and coordinating humans is hard. For typical operational analytics applications to consistently work end-to-end (e.g. driving up customer acquisition, retention and value via predictive modelling and campaign management) and to be able to prove and articulate their value-add, they require the coordination and cooperation of, at a minimum, people in each of the following organisational functions:
- Data Warehousing and BI
- Call Centre
- Product Management
The dependent set of business processes are difficult to execute. They are inherently brittle due to their many moving parts. But they’re also difficult to coordinate because they’re human-centric processes. The monitoring and performance management needs of humans are demanding and resistant to automation. The maintenance of human capital is far more mercurial and challenging than the maintenance of physical or information capital. This has implications for competition. It decreases the attractiveness of competing on analytics—particularly operational analytics—relative to alternative competitive frontiers.
Google is a competing on analytics business through and through. But its recent purchase of Motorola Mobile, according to many analyses, was about arming it to attack its competitors in the courtroom using its lawyers rather than in the marketplace using its engineers. Motorola Mobile’s thousands of patents provide it with new ammunition in its patent arms race with Apple, Microsoft, and others in the mobile telephony hardware sector. Its move into the political lobbying game was similarly explained five years ago.
What makes lawsuits and lobbying more attractive than analytics—to a company built on analytics? The law and the legislature are like analytics in many respects: complex domains, information based, and the province of highly qualified, experienced and intelligent specialists. However, far less of their complexity is contingent on the effective coordination and performance management of human activities inside an organisation. Operational analytics frequently fails because a business is divided against itself. Lawyers and lobbyists, on the other hand, can represent a large, complex, multinational business as a single, unified entity. The human coordination effort is far simpler and much less fragile.
Like any other competitive processes, lawsuits and lobbying campaigns can be more effective if they’re informed by analytics. But the sort of analytics that can do this will be of a more bespoke, tactical, or strategic nature, and less amenable to standard practices and operationalisation as IT processes.
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Most of the literature on why BI projects fail tends to put some variant of ‘a lack of communication between IT and the business’ near the top of the list of culprits. This implies a relationship between two parties prone to friction. Either IT or ‘the business’ can own the BI initiative, but each needs the other in order for it to progress. In crude terms, the business has information needs; IT has data and the electronic infrastructure required to access it, transform it, and publish it as information. The two parties don’t speak the same language so each has to second-guess the other. Each is also required to generalise the multiple views of its members into a unified position for representation and negotiation purposes. The result is an equilibrium conception of the information needs of business consumers which may only poorly approximate each individual user’s actual needs.
The data needs of analysts are, by comparison, easier to serve than the information needs of business consumers. Analysts want data in closer to its raw form, and are less reliant on others for its transformation into packaged information. There are fewer representation and negotiation steps between demand and fulfilment.
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Industry watchers have been talking up “advanced analytics” for a couple of years now — with no clear indication that the market was ready to follow suit. New market research finds that demand for traditional end-user query, reporting, and analysis technologies continues to outpace demand for advanced analytic technologies.
That’s from Stephen Swoyer at TDWI, commenting on International Data Corp.’s (IDC) recent Worldwide Business Intelligence Tools 2010 study. The study finds that demand for advanced analytics is growing in absolute terms, but that query, reporting and analysis is growing at a faster rate. This supports my contention that query and reporting basics remain a problem for organisations of all sizes.
According to IDC:
“The [advanced analytics market] continues to be dominated by SAS and IBM — which combined hold a 51.4 percent market share — and is therefore more strongly influenced by the performance of just these two vendors,” writes analyst Dan Vesset in the IDC report.
In BI, the post-consolidation megavendors rule the roost:
“The largest of IT companies continue to dominate the BI tools market and to consolidate market share,” writes Vesset, who notes that large IT companies such as IBM Corp., Oracle Corp., and SAP AG — among others — now control more than three-quarters (75.3 percent) of the entire BI market.
It would be interesting to know what proportion of these sales are pure BI or driven primarily by BI requirements, versus BI having been bundled with other software and/or hardware. There is some suggestion in the figures that the shopping cart model accounts for a good deal of the growth. BI grew by 11.4 percent in 2010 but only by 2 percent in 2009. It’s plausible that it took until 2010 for the acquisitions of Hyperion, Business Objects, and Cognos to find their feet in the larger sales machines of Oracle, SAP, and IBM.
Rounding out the market, the “BI-only vendors such as MicroStrategy Inc., SAS, and Information Builders Inc. (IBI) have fortified their markets”, and the emerging pure-play vendors “such as QlikTech International AB, Tableau Software, and Panorama Software have continued to outpace the market, growing at a rate several times that of the BI market as a whole.”
All of this reads consistently with the recent Dresner Advisory Services study.
It needs to be remembered that these measures of market share are fundamentally incomplete. They relate only to the commercial market and exclude commodity and open source software. I’ve pointed out previously that this is an understatement of how much Business Analytics activity is going on inside organisations. It’s only a software view, and even then it ignores a lot of the software actually used by analysts.
In interpreting these commercial trends it’s worth understanding the interrelationship between BI and advanced analytics, as well as the nature of the hype cycle.
I would expect an increase in attempts at advanced analytics to drive up BI. BI and advanced analytics are symbiotic. In consulting, my rule of thumb is that every one part of advanced analytics means five parts of BI. BI provides context. A predictive model that scores customers for their likelihood of future churn isn’t going to be valued unless historical churn and its revenue impact are also being reported. Nor is a better statistical forecast going to be appreciated unless actual time series monitoring and appropriate forecast error measurement are in place.
The persistence of query and reporting needs should also be reconciled with the high failure rate of BI initiatives. Successful BI isn’t easy. Most organisations try it more than once, and the default method of attempting it – not always for good reasons – involves purchasing new software. In this context, the hype that Swoyer mentions plays a role. Query and reporting initiatives which successfully affiliate themselves with emerging technology trends are given new life. Organisations get second and third chances to get BI right – under the moniker of being innovative. This may explain the degree to which the industry buzz around advanced analytics has exceeded market performance. It also explains why enterprise search, mobile platforms, big data, social media, and cloud computing are likely to be invoked in the context of contemporary BI initiatives.
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About usAnalyst First is a new approach to analytics, where tools take a far less important place than the people who perform, manage, request and envision analytics, while analytics is seen as a non-repetitive, exploratory and creative process where the outcome is not known at the start, and only a fraction of efforts are expected to result in success. This is in contrast with a common perception of analytics as IT and process.
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