This article will also be published as an upcoming Analyst First’s white paper to be released shortly, authors are Eugene Dubossarsky and Jason Widjaja.

Executive Summary

“Daunting as it may seem to rethink top-management roles and responsibilities, failing to do so, given the cross-cutting nature of many data-related opportunities, could well mean jeopardizing top- or bottom-line growth and opening the door to new competitors.”

- McKinsey Quarterly Nov 13, Mobilizing your C-suite for big-data analytics

Global spending on business analytics services is projected to rise from $51.6 billion in 2014 to $89.6 billion in 2018, according to a new forecast from International Data Corp. But how much of this rapidly growing, multi-billion dollar market is actually driving business value? And will the current executive leadership evolve in light of the rapid advancement towards a new business reality? This white paper explores both these questions.

1 Signs analytics is not driving strategic value

The gravitational pull of Big Data is now so strong that even people who haven’t a clue as to what it’s all about report that they’re running Big Data projects.

- Gartner On Big Data: Everyone’s Doing It, No One Knows Why

Analytics work needs to be taken in the context of its drivers within an organisation and the value that it can create, but this is currently not the case.Gartner on Big Data - Top Big Data Challenges 2

Gartner research shows that of the Top Big Data Challenges that organisations are facing, the largest by far is organisations are still struggling how to get value from big data.

And yet the prize is clear – a survey of executives at more than 400 companies around the world conducted by Bain & Company showed that the top 4% of companies who had the right people, tools, data and intentional focus significantly outperformed their peers. These companies are:

  • Twice as likely to be in the top quartile of financial performance within their industries
  • Three times more likely to execute decisions as intended
  • Five times more likely to make decisions faster

The implication that analytics spending is preceding intention or understanding is the starting point of understanding why many analytics functions are operating at a sub-optimal level, and are a long way from realising the substantial value of possessing market leading analytics capabilities. The following are a number of signs that analytics is playing a peripheral role in many organisations.

1.1 Compliance vs. Real Analytics – Does Analytics Drive Competitive Decisions?

Analytics at its best creates value by providing decision support to advise decisions that have a material bearing on the company. These decisions can be both strategic – with analytics acting as the intelligence function of the business – or operational, with analytics adding value at key value-creating decision points within business processes.

However, considerable analytics work is primarily driven by regulatory compliance. This may be necessary, but it is ultimately a second order application of analytics – done not to edge out the competition or create strategic value, but because an organisation is obligated to.

1.2 Vanity vs. Real Analytics – Does Analytics Lead To Coherent Action?

The big data and analytics market will reach $125 billion worldwide in 2015, according to IDC.

- Forbes.com, 6 predictions for the 125 billion big data analytics market in 2015

While analytics budgets continue to expand at a brisk pace, many analytics functions are set up for failure in the absence of executive sponsorship, business adoption, links to organisational strategy and a clear mandate from senior management. Unfortunately, analytics capabilities are often started – even with significant budgets – without supporting structures or well defined business objectives.

This may be ‘vanity’ analytics at work – some executives may be driven more by the need to be seen to be taking action and keeping up with trends by having an analytics function, rather than actually leaning on analytics to drive a defined business outcome. A tell-tale sign that this ‘vanity’ analytics may be in play is the answer to the question “how often does the key sponsor or source of budget actually engage with the analytics?”

2 Four kinds of executives in the decision making and data landscape

The value of analytics can only be realised in context – it is dependent on the analytics function not existing in a vacuum, but being consumed in actual value-creating decisions. A discussion on analytics is incomplete without the related discussion about those who actually make decisions based on the outputs of analytics. Moving to a frame of thinking where analytics is embedded in day to day decisions both strategic and operational is not a trivial thing.

Not all executives are equal, and while it would be a rare senior manager to be unaware of the potential of analytics – anyone with an analytics budget would have been a vendor magnet for years by the time of writing – their actual comfort with data, willingness to engage with analytics and degree of ownership for de

cisions differ. The following diagram illustrates four broad categories of executives which outline the data and decision making landscape.

Four types of executives and their response to analytics

2.1 Type 1: Political Decision Evaders

The first category consists of executives who are primarily self-driven. While politics is an ever-present reality, Type 1 executives shy away from decision making before the fact in order to take credit for good decisions and distribute blame for bad ones, regardless of who actually made them. They encourage or abet a “stakeholder” model of decision making ownership where attribution of responsibility for decisions is unclear. At their core they lean on their savviness in organisational politics as opposed to fully owning and embracing their decision making authority.

A mark of such a decision evader may be they can be heard saying things like the following to avoid the responsibility of making decisions:

  • “I’m just a facilitator”
  • “It’s not my job to decide”

With respect to data, their interaction with data is largely cosmetic – they may invest in it for appearance sake, but not want to actually look at or engage with data.

As such, they will not add value in the more transparent and objective environment that is the hallmark of effective, appropriate and depoliticised analytics. The opposite may be true – the truth is inconvenient, so it does not always suit a political purpose. And effective, objective analytics may unceremoniously uncover their shenanigans, proving to be a threat to their continued existence.

Their talent at politics may allow them to survive, at least in the short term. However, their days are numbered.

2.2 Type 2: Earnest Skeptics

The bulk of ‘traditional’ decision makers, prevalent in large organisations and government are the group of managers who have the most to gain from harnessing the power of analytics. Doing the best they can using regular tools and techniques, when appropriately incentivised these Type 2 executives hold promise in the coming age of data.

They recognise that organisations need to use data, so they can and use data, and prefer to stay with what is comfortable, straightforward and widely used. Their typical arsenal includes an array of spreadsheets, operational reports and financials.

They recognise they cannot afford to not know which parts of the business make money and which do not, and engage with analytics accordingly. However, they may view “big data” as another of a string of fads and are rightfully sceptical of “big data” hype.

They are perhaps curious but unsure of the benefits of analytics, e.g.:

  • What value does analytics deliver?
  • How can they practice analytics appropriately to realise the value in their organisational context?
  • How should their organisation instil that value in a cost effective way?
  • How can they achieve, or start to achieve, that value with their current resources?

2.3 Type 3: Arms-Length Voyeurs

Type 3 executives are marked by their cursory engagement with data and their seemingly mild approach of being ‘willing to let data make decisions’. However, that facade belies possible organisational risks due to the mishandling of analytics. While slightly evolved in terms of their data and analytics maturity relative to Type 1 managers, they have yet to grasp that to leverage data effectively they must be prepared to change their own practices to fit its insights and discipline.

Unfortunately, their passive ownership may be synonymous with ineffective ownership. Investing in analytics is not a passive investment like a bank deposit, where an investor puts money away and expects it to give a return over time. It is rather an active investment more akin to a gym membership – its investors need to actively engage with what they get access to through the investment in order to get value out of it.

They want ‘actionable insights’ that tell them what to do. They think they are engaging with data and its insights, but they may not be due to two problems:

  • By seeing analytics as a tool for actionable insight only, they are refusing to engage with analytics in a meaningful way. The limitations of analytics output need to be understood if they are to be used appropriately.
  • By wanting the decisions they make to be automated, they are fundamentally devaluing the role that they do.

Type 3 executives may benefit from education in examining and synthesise complex information for decision making, or may ultimately opt to pare back their engagement with data.

2.4 Type 4: Engaged Pioneers

Type 4 executives represent the ideal sponsor for analytics and a model of analytics literate leadership who will propel their organisations forward through the intelligent application and embedding of analytics.

Possibly ‘analytically minded’ all along, even before the recent definition of analytics, they are executives whose essential role as true decision makers has not changed, but have been boosted through the additional, vital source of information that is data analytics.

In lifting these executives to fully realise the value of analytics, a significant amount of education and advice in the analytics field may still be required. However, it is even more important that they effectively organise, manage and cultivate their human and electronic analytics resources.

A Type 4 business leader makes intelligent demands of data analysts. Such a leader knows they need to be analytics literate. They also know they need to be educated about analytics in order to use it.

In embracing analytics, the work of a Type 4 executive is not necessarily made easier, but it does become more successful, and more fulfilling in terms of intellectual stimulation and organisational outcomes. By successfully orchestrating a well-functioning analytics capability to meet the ravenous demand for information from an analytically minded organisation, organisations will be able to:

  • Make better decisions than their competitors
  • Navigate existential risks
  • Identify and exploit emerging opportunities

And it is these organisations that will be able to compete successfully and decisively break away away from their peers in the coming future.

Here is a recent presentation, a version of which went to Mark Burnard’s well-organised, catered and attended Big Data in Banking and Finance Meetup Group. Another version was presented to the Australian Superannuation Industry Trustee conference the following day. The Future of Analytics in Finance and Elsewhere, presented here in “director’s cut”/extended format contains much that didn’t make it into either presentation.

These are recent thoughts on what the future of data analytics looks like, what drives that future and how it is already here in many places, niches and organisations.  Parts of this presentation will exapand into future blog posts in their own right. A video will also be forthcoming.

Speaking of videos:  the  long-overdue “Zen of Data Science” presentation, is up on the Presciient videos page (scroll down after “Zen of Predictive Modelling”  to “Zen of Data Science” in 3 parts). These do deserve reposing on the A1 blog as its own article, to be done soon.

 

 

 

 

 

A good way to explain the most extreme yet pervasive issues in the “big data”/”analytics”/”data science” space is with parables. These transport those issues into other domains and thus hopefully help frame the problem.

The parable of “Big Letters”  covers the issue of literacy, the willingness to “get on the bandwagon” without understanding or even being aware of some fundamental concepts, and the role that vendors can play in exploiting and maintaining this state of affairs. Here it is:

In 1439, Johannes Gutenberg first used the printing press in Europe, triggering the age of mass communication. An invention altered society.  This was a time of revolution in the sciences, technology, economics  and arts in Europe.  A “Renaissance”

 The news reached a successful turnip seller in Europe. He saw enough conference presentations and heard from enough colleagues and others that printing was the information technology that was going to change business, and our farmer was determined not to be left behind. As it happens, the turnip seller, like so many at the time was in fact barely numerate and actually illiterate.

The turnip seller, consulted with printing press vendors who informed him of the different model printing press machines he could consider, and  all the additional value he could how deliver. The vendors were not at all perturbed by the turnip seller’s innumeracy, and indeed did not even address it. After all, miracle though printing may be, it was still all about “technical”, “academic” things like reading and writing, and higher level things like “science” and “literature” in the same vein, hardly the domain of important business people like our turnip seller.  

The vendors were very clear in their pitch:  the turnip seller had to own a printing press, but in no way did they say anything about him having to do anything other than own it, follow prescribed “best practices” and, presumably, watch the money roll in. Learn to read ? Not mentioned, not relevant. Instead, the vendors presented some turnip-industry best-practice case studies, at which their presses excelled on a number of metrics. The case studies concerned key business applications, which all the other turnip sellers (competitors!) were getting in on. Could our seller afford to miss out ?

 Accordingly “Turnips – best practice” was printed, mass-produced in multiple languages. The seller watched his new printing team busily doing their jobs, and saw pamphlets, books and journals published. Naturally, he didn’t understand a word of it, but he knew that it wasn’t really his job to understand. What were all those words, letters, pictures for ? He didn’t need to worry. He was too important, “business focused”, “strategic” for that. Reading and writing was for the nerds that worked for him. 

The press’ active life was short before it was long forgotten.  It is unclear who was supposed to read it, or what value it generated. The seller found some value in showing his shiny technological marvel to friends, customers and colleagues, but never really understood what it was for. Soon, hard times befell the seller after a particularly harsh winter, and the printing press collected dust.

Sometimes, that winter, the seller did have time to wonder, what the heck do all those people the vendors cited as “best practitioners” do with their printing presses anyway. 

This parable illustrates that spending money on “Big Letters”, or “Big Data” for that matter, before you understand what it can do, and its actual relevance to your business –  is expensive, comical and a little insane. There is a minimum literacy requirement as well as a need for direct engagement with the technology and the insights it enables.

There is also a cautionary tale about “best practice”, and tool-centric, vendor driven procurement, with illiteracy on one end, and a sales model that isn’t exactly averse to that illiteracy on the other. Finally, consider under what circumstances the printing press vendor might have said “You know, maybe you don’t need a printing press, at least not yet. and I have no idea what you would do with it if you had one. In any case, you should learn to read first”.

 

 

Founder Stephen Samild presented some new ideas on Business Intelligence :

The Data to Decision Landscape.

There may be a more detailed post on the subject by Stephen at a later stage.

Yours truly was interviewed by CeBIT. The topic was broadly : “What the heck is this Data Science Thing Anyway”.

So here is what I said.

“The Zen of Data Science” will be presented a second time, at the Singapore Analyst First group meeting today, headed by Brett Shadbolt who kindly continues to provide premises, refreshments, admin and many other kinds of support to A1, as well as his able leadership of the Singapore and Hong Kong chapters.
The presentation will happen about an hour after typing, so better hurry. Or wait until it presents in your town.

Tagged with:
 

After an overlong absence from the blog, here is some material that should compensate at least in part.

These are three presentations that I have given over the last several months. First of all, a presentation to the Sydney Big Data Group all the way back in April.

The title was : “The Zen of Predictive Modelling”.
A Video of the talk

The slides.

More recently, in June I presented to the Melbourne Users of R Network (MelbURN) on A1 related topics and the role of R in the A1 mix of ideas. Here is how it was billed on the MelbURN meetup page:

Looks like the title got your attention. This presentation is about R’s role, presence and profile in the world outside research and academia. Eugene will describe his experiences as a consultant, trainer, R User Group leader and general industry busybody in promoting the use of R and open source tools for analytics, in Australia and overseas.

He will discuss the political and social environment dominated by the brands of top vendors, and contrast this with a number of measures of R’s actual strong position, its prominence and indeed supremacy as most widely used analytics tool.

Eugene will also present a number of strategies for “selling” R, and pleasantly surprising clients with its power, graphical beauty and ease of implementation and use.

He will also discuss the role of Analyst First in this space.

Eugene may also discuss his new venture “ConnectR”, a community for crowdsourced, crowd funded and business-driven R development. Or he may go in any of a number of tangential directions. He usually does.

The talk pulled no punches, said some frank and less than kind things that needed saying, and promoted much debate during question time.

Slides:
Shocking Confessions

Finally, a presentation from a week ago, presented to the Sydney Data Miners meetup group, titled “The Zen of Data Science”

Slides:

Zen of Data Science

 

Hi Everyone,

I met Eugene last night after connecting through the good people of Melb Uni Computer Engineering.

My career (I’m a 57 YO grey beard) included 10 years in Telstra where I was given a very broad brief to leverage scientific methods and analytical evidence in designing reforms of operations, and convince Execs to make changes. You’ll guess correctly I come from the operational strategy and change side of business. My disciplinary background includes a Ph.D in Management Accounting. I’m sure to stand in awe at the IT and mathematical competencies many of you will possess.

I’m looking forward to meeting many of you in your local chapter meetings. I’m in retirement number 2 – probably temporarily – and would love to use some of that time to learn from and, if I can, assist the good work of Analyst First and its individual members.

Best wishes,

John

“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.

Analyst First is now officially in the US of A with two new chapters, in Washington DC and Dallas-Ft. Worth, both under the leadership of Dr. William Luker Jr.

Anyone interested in joining their local chapter or starting a new one please contact :
a1 ‘at’ analystfirst.com

 

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 Understanding – knowing what to do with analytics, what it takes to get an analytics function running, and how to keep it going.

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.

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