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.
Continuing with the big data meets big hype theme:
So you want to get into Business Analytics/Big Data/Predictive Analytics.
What areas, skills, tools, data should you focus on first ?
There are three rather big questions that you need to ask yourself:
1. How well do I really understand the problem(s) that I want Analytics to solve, and The roles(s) that Analytics would play ?
2. How well do I understand my data?
3. What data do I actually have, or can get ?
Each question explores a continuum. Together they represent a three dimensional space of possibilities. There is no “magic quadrant” here, each part of the space is a legitimate place to be, with its own solutions, risks and benefits.
Let’s go through them.
1. The range of possibilities looks something like this:
A: having built preliminary offline random forest models and created some prototypes, I want to extend these existing customer acquisition and retention models we have to our intentional markets, and operationalise them for real-time, event based activity, provided this is seem to yield further significant yield. We will need an industrial strength, scalable, and reliable tool, probably a commercial vendor tool, and possibly a Hadoop-based MapReduce solution
B. my CEO just attended a lavish conference where he saw a slide presentation mentioning the Davenport HBR article from 2006 and now he wants us to “get into analytics”.
Most people are somewhere in between. But you get the idea. And there are far too many initiatives that are precisely at B. the ideal vendor customer is precisely at A. Unfortunately, there are not enough A’s around (we call them “Eduacated Buyers”) so some vendors must sell to people who look more like B’s.
Naturally, Analyst First does not advise Bs to get into Big Data, buy expensive vendor tools, or ever believe anyone that there is such a thing as “a solution for getting started in Analytics” especially when said solution is no more than a bunch of software and maybe a few relatively junior technical consultants for a few months.
Indeed, we advise the Bs of this world to invest in learning, exploring and gaining experience, while managing their sponsors’ expectations and growing their personal investment and participation in the new Analytics enterprise (yep, it’s an Enterprise, with all the Lean Startup that entails), and eliciting from said sponsors their real, and realistically achievable needs.
This is a crucial time to invest in smarts, experience, talent, learning and plenty of Lean Startup.
If this approach is not feasible, I do not have high hopes for the future of the function, which will, at best become a showpiece trophy of high tech adding no value, and will more likely be shut down, “restructured” and restarted again, hopefully with a more sensible approach.
And what of the As ?
Speaking to an A recently, indeed one of the best As I know, he noted that his team had kicked some great business goals recently, having implemented a very necessary expensive vendor tool, after trying R and seeing that it was not up to the big data / big crunch job they had to do. He noted that this was necessary, even though he agreed with A1, and that this was not in line with A1′s preference for open source tools.
“not at all”, I replied, “This is exactly A1, you were the quintessential Educated Buyer! A1 is not against vendor tools. We are against people spending money on what they do not understand in the hope of a magic solution. You don’t fall into that category.”
Hopefully, the anonymous A in question will write a more detailed post on this blog, outlining his success story in more detail.
So, our advice to As is… You don’t really need our advice, until you want to do something new again. In which case, chances are you are following A1 principles already, explicitly or not – otherwise how did you get to A in the first place,anyway ?
Most people are somewhere in between, and usually closer to B than to A.
Answering the “what the heck are we going to do?” question involves exploration on a number of axes, including stakeholders needs, own capability, available resources (human and electronic), any impediments or constraints (Hello IT!) and data, the subject of questions 2 and 3. The actual hidden contents of the data, the “gold” of the data “mining” metaphor is a huge exploratory subject in its own right, and must be considered in the context of the others.
This is not a very easy target to hit, and needs defining before that can happen !
So, to all the Bs and almost-B’s out there : invest in learning : invest in your own and your sponsors’. Invest in getting your sponsor invested, supporting and covering you, letting you explore and grow. Invest, above all, in exploration and invest in managing expectations and delivering intermediate ressults to allow all this to happen. Buy your analytics function a chance to grow, learn, explore and breathe free of unreasonable pressures and constraints.
The other two questions will be covered in upcoming posts.
In my experience working for and partnering with software vendors I have never once heard of an organisation buying training from a vendor before deciding whether or not to buy its software. I’d be very keen to hear from readers who know of any examples.
Business Analytics software vendors have Education departments: specialist trainers, classrooms, courseware, certification. Their education programs cover beginner through to advanced level instruction in how to use their software. Few individual courses would run for more than five days. Investors in Business Analytics typically send the software’s users-to-be on training in the early stages of implementation, after the software has been selected and paid for.
This seems an opportunity missed. Training is a wonderful evaluation tool, not just of the software, but also of the team who will be using it, and of the degree to which its intended use has been well formulated.
Most software capabilities are asserted through written responses (RFTs, RFQs, RFIs, etc.) and then demonstrated. Demos are created and performed by experts. This shows the software in its best light and illustrates its possibilities. Worth doing.
Some organisations also run a trial evaluation of the software. This tests the software’s compliance with the IT environment and may enable some cursory analyses to be performed using real data. Also worth doing. However the software is typically operated primarily by the vendor’s consultants in these settings – in part because no one from the business has been trained yet.
My suggestion is that an organisation considering spending money on a vendor’s software should first spend money on that vendor’s training. This would enable a superior evaluation of:
- How easy the software is to use in practice.
- Whether the proposed users of the software are suitable.
- The degree to which expectations of increased productivity have been well calibrated.
- Whether the business problems expected to be tackled using the software have been well framed.
- Whether additional tools are required and/or existing tools are being duplicated.
- Whether the business has the sort of data it’s going to need to run various analyses.
- Whether draft project plans are realistic.
There remain some critical things it wouldn’t be able to test, which also don’t get addressed through sales consultation, written responses, or demos:
- The wisdom of the Business Analytics initiative from the point of view of business value.
- The organisational-political environment in which the software is going to live.
- The organisation’s data quality.
- And of course, what’s going to be found when the data gets analysed.
The only apparent downside of this approach is cost. But if an organisation is confident that it’s going to buy the software in question then it’s going to be paying for training anyway. There is no additional cost. If it’s not confident then this makes even more sense as a hedging strategy. Training is a small cost when compared to licensing fees, implementation services and first year maintenance. Upfront training is a smart way to buy an option on the software.
Related Analyst First posts:
So what is Analyst First all about?
In a nutshell, it is about making analytics cheaper, more relevant and appropriate to business (which can includes government, NGOs and any other folks actually using analytics to do something other than research for its own sake). It is also about presenting a radically different model of analytics to the one currently seen by most of the market.
Does this mean that it is not being done well already? Well… Let’s say that it could be done a whole lot better.
The biggest problem is: most people think that analytics is about software, when it is actually about people.
What does this mean? It means that buying very expensive software that the buyers do not understand and do not have the staff to select appropriately – let alone use – is a lousy way to get going with analytics.
On the other hand, investing in people might just be the right idea. Investing in people does mean getting skilled analysts before software. Hence “Analyst First”.
But this is only the beginning, getting us to the first key principle of Analyst First:
Invest in Smarts: Build The Human Infrastructure First
This means getting highly skilled experts in analytics to advise, demonstrate and trial a range of techniques, mentoring the new team.
It means carefully building an appropriate team of analysts, business experts, communicators and data manipulators (yes, they are different skill sets).
More importantly it means establishing the right channels, expectations and incentives to gently educate executives about what they can ask of analytics, and what they need to provide to make it happen.
This may sound hard enough, but what does the team work with if there is no software? But there is:
Use Free, Commodity and Open Source Tools First
Tools on the desktop, such as MS Excel and Access, are more than enough for most analytics tasks attempted by beginners, or business areas trying out analytics.
If serious power is required, tools like R, Rapidminer, Knime etc will probably do. These tools are free, and industrial-strength enough for most applications. Certainly worth trying first, and perhaps sticking with.
In our experience, commodity and open source tools are good enough 95% of the time – 100% percent of the time for a new analytics unit. In the latter case, the unit is not entirely sure how analytics may be applied in their business, and their first job is to find out, capturing executive support in the process.
This is a big ask, made all the bigger if big $$$ have been spent on software, and a small number of mediocre staff are hired as an afterthought.
On the other hand, commodity and open source tools are a great alternative, allowing the money to be spent on human infrastructure.
Analyst First is not against buying expensive vendor tools, but it is against spending a cent on software until the buyer is an Educated Buyer, having used commodity and open source tools extensively, found their limitations, and seen a specific need for an expensive vendor solution.
Educated Buyers cut their teeth on readily available, inexpensive tools first, and invest their money in people: staff, skills, consultants, mentoring.
The Practice of Analytics: Exploration, Learning, and Making Mistakes
Analytics is not a linear process, like most engineering projects. Its end product is discovery: you cannot determine what will be discovered ahead of time. Thus the outcomes of analytics, and the decisions based on them, cannot be made before the analysis has been carried out.
Further, analytics is inherently exploratory in nature: data is a treacherous beast, and there may be many dead ends. Not all analytics exercises end in brilliant findings, accurate models or actionable insights. Nor should they. Mistakes are how we learn. The trick is to make them quickly, minimise their cost, learn from them and move on.
This organic, exploratory approach fits perfectly with a view that Analytics is an Intelligence activity.
Where it fits less well is with the view that analytics is IT…
Analytics is not IT
While accounting, graphic design, journalism and medicine are not part of the IT function, they all use a heck of a lot of IT.
Analytics done right is no different. While analysts require some very smart software to do their jobs, the software is not the star of the show. A strategic intelligence function does not belong within IT.
The fact that analytics relies on highly sophisticated software should not serve to downplay the far greater role of people.
The exploratory, organic growth, human centric model outlined above is in stark contrast with the IT view of the world, which is all about pre-defined systems, known outcomes and large, top-down specified projects. An intelligence-based, human-centric model has no chance to flourish within such an environment.
Finally, most of the issues concerning IT in the context of analytics apply in the operational deployment of analytics results (eg campaign models), but apply far less in the context of analytics proper: the exploration, description and modelling of data.
Issues around security, real-time effectiveness, transfer bottlenecks etc are not real issues in most analytics contexts, though they are thrown around by IT departments.
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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