Digital Transformation Dialogues – Part 2

(Resuming my dialog with transformation expert – and friend – Scott K Wilder). Scott had touched on the challenge an older workforce presents to digital transformation and the need to embrace Millennial Leaders…)

GA: I see this all the time. In fact, I’d venture to suggest that there might be a pretty strong positive correlation between the average age of your workforce and the perceived need for digital transformation. But this seems really hard to change. Good luck attracting young digital talent to a company that skews older AND is poor at digital. I also see challenges in adaptation. I argued that when you select a digital leader for transformation it’s important – even vital – to get someone who isn’t just experienced in cutting edge digital. They need to have experienced the pain of transformation to be effective in that role. But I see potentially similar problems trying to integrate younger employees into your workforce. I could see where they would just get frustrated. Obviously, though, this has to be done. Thoughts on how to smooth this? And thoughts about making an older workforce more digital in a fairly effective manner?

SW: Having younger individuals in your company is important for a true digital transformation. But don’t just hire them because they are less expensive than the older workers. Hire them because of how comfortable they are with technology and their desire to learn.

To smooth things out, first I would focus on what Millennials want in their career and / or what do they want to get out of their work. They have a tremendous desire to learn. Yes, it’s not just about achieving for them. Reminder: Creating a learning culture is an important way to transforming a company.

Therefore, during an interview process or an onboarding process, I would ask them:

– where do they see themselves in a year (none of this three year or five years stuff)

– what skills do they think they need to learn or acquire (maybe you, the hiring manager, help guide them towards an answer by sharing what skills are required for this)

– how can you (their manager support them)

Reminder: Find out their goals and aspirations before they start working

A big mistake companies make is that they never even consider asking these questions.

I would also look beyond the hiring manager or group. I would find a younger employee a mentor, who is outside the group they work in and who is not part of their of their everyday team (even if it is a cross-functional team). I would find someone who can be a good sounding board for the individual. In fact, I would have the person interview 2-3 potential guides or mentors. Let them feel like they are part of the process. Reminder: Assign them a mentor and Don’t just assign everything to a Millennial. (OK, that’s two reminders)

Establish toll-gates or check-ins with the younger employee. Part of creating a learning culture is to have an open and continuous feedback loop. Reminder: Check in with your younger employees even if there’s a manager that separates you and them.!

Younger people today are passionate about causes. So figure out if there’s a way to tie your digital transformation to a higher cause (or even calling). If you are T-Mobile, for example, can you use your technology to help people in less developered countries get better access to telecommunications (Maybe be part of Google or Facebooks’s Internet Satellite projects). Reminder: Define and share your cause!

And somewhat related to the ‘cause’ calling, make sure your company has a clear mission. People, in general, respond better when they know where the company’s True North lies — what the company is trying to accomplish. Final Reminder: If you really want to smooth things out and integrate younger employees into your digital transformation, make them a part of the journey from the beginning.

GA: This is great stuff. I’ve always been a little skeptical of generational theories – but there really are some noticeable differences with Millennials. It’s also, I think, a matter of our times. We talk about Millennials, for example, being passionate about causes – and I’ve certainly seen that. In general, though, I think it’s true more generally these days – not necessarily that people are more passionate about their causes  – but that they are more willing to cross work with other things and are less determined to have a work life and a non-work life which never shall meet. When you can get people to bring that extra passion to their work it’s a pretty big win.

But you dodged one aspect of my question (or at least sinned by omission) – what about getting older works more attuned to digital? In some ways, I think that’s a more important and interesting problem…

The Road Goes Ever On

“It’s a dangerous business,” says Bilbo Baggins, “going out your front door. You step onto the road, and if you don’t keep your feet, there’s no knowing where you might be swept off to.” Eighteen years ago I stepped outside my comfortable door and was swept out into a digital world that I – like everyone else – knew very little about. There were dragons in that wilderness, as there always are. Some we slew and some we ran away from. Some are out there still.

But though a road may go on and on, a person sometimes finds another path. In the last few months I’ve felt rather like young Mr. Baggins, visited by dwarfs and a wizard, and confronted with a map of a great unexplored wasteland, a forbiddingly guarded, lonely mountain, and a vast treasure.

It’s not so easy to give up adventuring.

Eighteen years ago when I first started thinking seriously about digital analytics, we were the poor step-child of analytics. Web analytics (as we called it back then) was pathetic. To call it analytics was a misnomer – the right word being some polyglot mash-up of hubris, false-advertising and ignorance. Perhaps “faligris” was the word we needed but didn’t have. We looked with envy at the sophisticated analytics done for mass media, retail, and direct mail.

Seriously.

My how times have changed.

I’m not going to go all Pollyanna on you. There’s still a lot not to like about the way we do digital analytics. But here’s the thing – I’m not sure there’s a field that does it better. Without really realizing it, digital has spawned a discipline of continuous improvement that includes a fairly sophisticated view of dashboarding and reporting, interesting segmentation, a decent set of techniques for specific analysis problems, and – probably most impressive – a real commitment to experimentation. Sure, most companies get a lot of this wrong. My extended discussion of the perils and problems of digital transformation isn’t (really!) just grumpiness. But the companies that do it well are truly outstanding. And even in the flawed general practice there is much to like.

The best of digital analytics these days has nothing to be ashamed of and much to be proud of.

That’s why, on the map of the digital analytics world, there are more gardens than wasteland, more cultivated field than dangerous mountain. Digital Analytics is well past the “trough of despair” in the hype-cycle – delivering tremendous value on a consistent basis. That’s a great place to be, but I’m looking for a little more adventure.

A little backstory may be in order here. Not too long ago, one of my larger clients asked us to take a look at a solution they’d tried to measure their customer’s IN STORE journeys. Their vendor wired up a sample store with a network of cameras to detect and measure in-store customer paths. It looked a lot like digital analytics. Or should I say it looked a lot like web analytics? Because in almost every respect, it reminded me of the measurement capture, reporting and analysis we did back in 1997. The data capture was expensive and broke frequently. The data was captured at the wrong level of granularity and there was no detail feed available. The reporting was right there with Webtrends 1.0. The analysis – literally – became a standing joke with our client.

It was pathetic.

Well, even from this mess, there was interesting information to be salvaged. You COULD do better reporting even on the sadly broken data being collected. But it got me thinking. Because this was a “leader” in the field.

So naturally, I checked out the rest of the field. What I found were the same type of engineering heavy, analysis tone-deaf companies that I remembered back in the old days of the web before people like Omniture and Google figured out how to do this kind of thing right. I found technology solutions desperately looking for actual business problems. I found expensive implementations that still managed to miss the really important data. I found engineers not analysts.

I found opportunity.

Because this data and these systems are very much like digital analytics. The lessons we’ve learned there about collection, KPIs, reporting, segmentation, analysis and testing all feel fresh, important and maybe even revolutionary. And this time, there’s a chance to provide an end-to-end solution that combines technology with the kind of reporting and analytics I’ve always dreamed about. There’s a chance to be the Omniture AND Semphonic of a really cool space.

I just couldn’t resist.

So I’m going to be leaving EY and, for that matter, digital analytics. I’ll miss both keenly. These past years in digital analytics have been the best and most rewarding of my professional life. I’m proud of the work I’ve done. Proud of the work we’ve done together. Proud of the discipline we’ve created. But I want to take that work and build something new from the ground up.

I’m going to build a startup dedicated to bringing the best of digital discipline and measurement to the physical world. Helping stores, malls, stadiums, banks, hospitals and who knows what else understand how to use customer behavior to actually optimize customer experience.

I want to make the best experiences in the real world every bit as seamless, personalized, and optimized as the best experiences in the digital world already are.

It’s a dangerous world out there in physical retail. They’re struggling and they don’t really know how to get better. If there really was a map, I’m pretty sure it would have a big X with “there be dragons” printed right above.

I can’t wait.

Welcome to Digital Mortar!

 

 

Digital Transformation Dialogs

I’m going to wrap up this extended series on digital transformation with a back-and-forth dialog with an old friend of mine. I’ve known Scott K. Wilder since the early days of Web Analytics. He’s been an industry leader helping companies build communities, adapt to an increasingly social world, and drive digital transformation. In some of this current work, Scott has been working with companies to adopt collaborative working suites for their customers, partners and employees – which I think is a huge part of internal digital transformation. So I thought a conversation on the pitfalls and challenges might be interesting and useful.

GA: We all see these hype-cycle trends and right now there’s a lot of interest in digital transformation at the enterprise level. I think that’s driven by the fact that most large enterprises have tried pretty seriously for a while now to get better at digital and are frustrated with the results. Do you agree?

SW: Good question.

When you read white papers about the latest trends in the enterprise space, most of them highlight the importance of each company being digital transformed. This usually means leaving a legacy approach or operation and instead leveraging a new approach or business model that embraces technology.

Unfortunately, most companies fail when they undertake this endeavor. Sometimes they fail because just pay lip service to this initiative, never do anything beyond placing the goal of ‘going digital’ on a powerpoint slide they give a company All-Hands (I have witnessed this first hand). And sometimes, they just test out bunch of different programs without thinking through desired outcomes. (They throw a lot of virtual stuff against the internet wall hoping that something sticks).

Undergoing a Digital Transformation means many things to many people. It can imply focusing more on the customer. Or it can mean enabling employees collaborate better together. At the end of the day, however, a company needs to first focus on one simple end state. One change in behavior! Rather than trying to boil the whole ocean at once and try to do implement massive digital transformation across an organization, it’s better to start with a  simple project, try to leverage technology to accomplish a desired outcome, learn from the experience and then share the success with other parts of the organization

Start first with a relatively simple goal. And if you really want to change an organization, see if you can get employees volunteer to be your soldiers in arms and then closely work with them to define what digital success looks like. It could be as something getting employees to digitalize their interaction with each other more  or leveraging technology to improve a VOC process. Whatever it is. Start with one project.

Here’s one approach. Once the goal is to define, then ask for volunteers to work on figuring out how to achieve the desired outcome. No digital program or initiative is going to be successful without employee buy – in and involvement, so it behooves CEOs to find a bunch of enthusiastic volunteers to figure out the ‘how’ (If you remember you calculus Y = (x)x Senior managers can decide on the Y, and then let their team figure out the X or inputs.

Digital Transformations often fail because:

  • Executives often decide their company goals and then impose their approach on the employees. Digital Transformation initiatives also fail because CEOs want to change whole culture overnight. Unfortunately, however, they often forget Rome was not built in day. Even though a true Digital Transformation is often a journey, it is also important to start simple. Very simple!
  • There’s no buy in at the mid-level ranks in the company
  • There’s no True North or desired goal
  • There’s too much attention on the technology and not the cultural impact.

I have read articles that tell you true cultural change can only happen if you eliminate political infighting, distribute your decision making, etc. While all of that is important, it will require gutting your organization, laying off a lot of people and hand-picking new hires if you want to change things quickly.

To truly change a culture, however start simple. Pick a goal. Ask for employees to volunteer to work on it (take other work off their plate so they don’t have to work after house). Ask them to to involve leveraging digital technologies. Give the team room to succeed or fail.  Most importantly, be their guide along the way.

Once this small team completes their project, celebrate their success in front of others in the company. Have them highlight how they leveraged technology.

Once this group is successful, anoint each team member to be a digital transformation ambassador and have them then move into other groups of the organization and share their learnings, experiences, etc.

GA: I’m a big believer in the idea that to change culture you have to change behavior – that means doing things not talking about them. I like the idea of a targeted approach – huge organizational changes are obviously incredibly risky. That being said, I feel like most of what you’ve talked about could be applied to any kind of transformation project – digital or otherwise. I’m not disagreeing with that, but I’m curious if you agree that digital presents some unique challenges to the large enterprise. And if you do agree, what are those challenges and do they change/drive any aspects of a transformation strategy?

SW: There are definitely challenges in driving any type of transformative change in an enterprise environment. Here’s a list of challenges preventing a smooth adoption of digital technologies or hindering the ability to digitally transform an organization

As they say. It’s hard to teach an old dog new tricks. Companies get stuck in their old ways of doing things. For example, even though companies are testing the waters with Slack and Hipchat, two great collaborative platforms, few have made any progress in being weaned (a bit) off of email. For example, we all complain about email but refuse to reduce how often we use it). Part of the problem is the result is that those individuals, who are tasked with driving change in the organization actually tend to be the biggest resisters to change. The IT department, who I will pick on here, usually are decision makers and keepers of the digital platform budgets do not want to try something new. (Marketing is slowly getting more say here, but most marketing leads don’t understand new technologies). So IT and even Marketing wait as long as possible to make a decision about adopting newer collaborative technologies, such as Slack or Hipchat. And while they are doing an elaborate evaluation process, today’s tech savvy staff often just jumps in and starts using the latest and greatest technologies. They don’t ask for permission first. This was the case at Marketo with Slack. First, a small group of employees starting using it and soon others jumped in. There was resistance at the highest parts of the company. Eventually, IT, however had no choice and how to follow the wisdom of the crowd. Survey Monkey also started out this way. There are other challenges as well. Solution: Companies need do a better job at knowing understanding what tools their teams want to use and why they want to use them. If the troops are using Google Docs, for example, management needs to embrace this and not try and force their way (in this case, the Microsoft Office 365 way) down the throats of their employees. If there are security concerns, figure out a solution.

GA: I’ll just note that in many ways this reflects my discussion of a Reverse Hierarchy of Understanding in organizations

…What else?

SW:   Data and Privacy Issues: Companies, rightly so, are always concerned about data leakage, data security and privacy issues. Enterprises, especially the public ones and the ones in important transaction industries like Finance or Health Care, have to be sensitive to how data is shared within an organization. Solution: If an organization wants to adopt a newer technology, management needs to do more research in how other companies adopt newer technology while protecting their company secrets. Few companies develop breakthrough technologies and systems that they are the first to try something new. Probably someone has already created a similar service or implemented a similar technology. They have probably already dealt with similar issues. I am not saying just copy what they did but rather learn from their mistakes. Or what they did well.

An older workforce: A third challenge is that many enterprises attract an older workforce and/or are not sure how to integrate millennials into their organization. As I pointed out in my book, Millennial Leaders, it’s important to embrace a younger workforce and place these individuals on teams where they can help advise key decision makers. Younger employees are more likely to adopt new approaches, new technologies and new ways of doing things. Solution: Bring millennials into digital related conversations sooner than later. While decision making can still be top down, it’s important to give these younger folks a voice.

GA: Okay – I know you have more thoughts on this but I’m going to stop right there because I know you’re an expert on this Millennial stuff and I want to delve into it a bit. But that’s probably a discussion for Post #2…

How to Drive Digital Transformation when You’re Not a Digital Expert : Addressing the Reverse Hierarchy of Understanding

In my last post I described some of the biggest challenges to a traditional enterprise trying to drive digital transformation. This isn’t just the usual “this stuff is hard” blather – there are real hurdles for the traditional large enterprise trying to do digital well. The pace of change and frictionless competition drive organizations used to winning through “weight of metal” not agility, crazy. The need for customer-centricity penalizes organizations setup in careful siloes. And these very real hurdles are exacerbated by the way digital often creates poor decision-making in otherwise skilled organizations because of what I termed the reverse hierarchy of understanding.

The reverse hierarchy of understanding is a pretty simple concept. Organizations work best when the most senior folks know the most about the business. When, in other words, knowledge and seniority track. For the most part (and despite a penchant for folks lower down in the organization to always think otherwise), I think they do track rather well in most companies. That, at least, has been my fairly consistent experience.

There are, of course, many pockets of specialized knowledge in a large company where knowledge and seniority don’t track. The CFO may not be able to drive TM1. The CTO probably doesn’t know Swift. That’s not a problem. However, when something is both strategic and core to the business, it’s critical that knowledge and seniority track appropriately. If they don’t, then it’s hard for the enterprise to make good decisions. The people who are usually empowered to make decisions aren’t as qualified as they typically are, and the folks who have the specific knowledge probably don’t have either the strategic skills or business understanding to fill-in. And, of course, they probably don’t have the power either.

Digital can create exactly this inversion in the appropriate hierarchy of decision-making in the traditional enterprise, and it does so at many levels in the organization. Digital has become strategic and core far more rapidly than most large organizations can adapt, creating reverse hierarchies of understanding that can cripple efforts to do digital better.

So if you want to transform a traditional business and you know your organization has a reverse hierarchy of understanding (or maybe just a complete lack of understanding at every level), what do you do?

There’s not one answer of course. No magic key to unlocking the secret to digital transformation. And I’ve written plenty of stuff previously on ways to do digital better – all of which still applies. But here are some strategies that I think might help – strategies geared toward tackling the specific problem created by reverse hierarchies of understanding.

 

Incubation

I’m sensitive to the many draw-backs to incubating digital inside a larger organization. If incubation succeeds, then it creates long-term integration challenges. It potentially retards the growth of digital expertise in the main business and it may even cannibalize what digital knowledge there is in the organization. These are all real negatives. Despite that, I’ve seen incubation work fairly effectively as a strategy. Incubation creates a protected pocket in the organization that can be staffed and setup in a way that creates the desired knowledge hierarchy through most levels.  Would I always recommend incubation? Absolutely not. In many organizations, years of at least partial learning and transfusions of outside talent have created enough digital savvy so that incubation is unnecessary and probably undesirable. If digital knowledge in your organization is still nascent and particularly if you have layers of management still skeptical or negative to digital, then incubation is a strategy to consider.

 

Transfusion

And speaking of talent transfusions, the role of appropriate hiring in effectively transforming the organization can hardly be overstated. The best, simplest and most impactful way to address the reverse hierarchy of understanding is to…fix the problem. And the easiest way to fix the problem is by hiring folks with deep digital understanding at multiple levels of the organization. In some cases, of course, this means hiring someone to run digital. If you’re a traditional enterprise looking to hire a chief digital officer, the natural place to look is to organization’s that are great in digital – especially the companies that dominate the Web and that we all, rightly, admire. I tell my clients that’s a mistake. It’s not that those folks aren’t really good at digital; they are. What they aren’t good at is digital transformation. If you’ve grown up managing digital platforms and marketing for a digital pure-play, chances are you’re going to be massively frustrated trying to change a traditional enterprise. To drive transformation, you have to be a great coach. That isn’t at all the same as being a great player. In fact, not only isn’t it the same, it’s negatively correlated. The best coaches are almost NEVER the best players.

Getting the right person to lead digital isn’t the place where most organizations go wrong though. If you’re committed to digital transformation, you need to look for digital savvy in every hiring decision that is at all related to your digital enterprise. You need digital savvy in HR, in accounting, analytics, in customer, in supply chain, in branding and corporate communication. Etc. Etc. This is the long game, but it’s ultimately the most important game you’ll play in digital transformation – especially when you’re trying to drive transformation outside of massive disruption. In my last post, I mentioned FDR’s many efforts to prepare the U.S. for WWII before there was any political consensus for war. Every leader is constrained by the realities on the ground. Great leaders find ways to at least lay the essential groundwork for transformation BEFORE – not after – disaster strikes. You need to make sure that digital savvy becomes a basic qualifier for a wide range of positions in your organization.

 

Analytics

Dare I say that analytics has the potential to play a decisive role in solving the reverse hierarchy of understanding? Well, at the very least, it can be a powerful tool. In a normal hierarchy of understanding, seniority comes pre-loaded with better intuitions. Intuitions born of both experience and selection. And those intuitions, naturally, drive to better decisions. It’s darn hard to replace those intuitions, but analytics is a great leveler. A good analyst may not be quite the decision-maker that an experienced expert is – but at the very least a good analyst equipped with relevant data will come much closer to that level of competent decisioning than would otherwise be possible.

Thankfully, this works both ways. Where senior decision-makers can’t rely on their experience and knowledge, they, too, benefit from analytics to close the gap. An executive willing to look at analytics and learn may not be quite in the league of an experienced digital expert, but they can come surprisingly close.

This works all up and down the organization.

So how do you get your team using analytics? I addressed this in depth in a series of posts on building analytic culture. Read this and this. It’s good stuff. But here’s a simple management technique that can help drive your whole team to start using analytics. Every time there’s an argument over something, instead of voicing an opinion, ask for the numbers. If your team is debating whether to deliver Feature X or Feature Y in digital, ask questions like “What do our customers say is more important?” or “Which do high-value customers say they’ll use more?”

Ask questions about what gets used more. About whether people like an experience. About whether people who do something are actually more likely to convert. If you keep asking questions, eventually people are going to start getting used to thinking this way and will start asking (and answering) the questions themselves.

Way back in the early days of Semphonic, I often had junior programmers ask me how to do some coding task. At the time, I was still a pretty solid programmer with years of experience writing commercial software in C++. But since I wasn’t actively programming and my memory tends to be a bit short-term, I almost never just knew the answer. Instead, I’d ask Google. Almost always, I could find some code that solved the problem with only a few minutes’ search. Usually, we’d do this together staring at my screen. Eventually, they got the message and bypassed me by looking for code directly on Google.

That’s a win.

Nowadays, programmers do this automatically. But back in the aughts, I had to teach programmers that the easiest way to solve most coding problems is to find examples on Google. In ten years, looking at digital analytics and voice of customer will be second-nature throughout your organization.  But for right now, if you can make your team do the analytics work to answer the types of questions I’ve outlined above, you’ll have dramatically raised the level of digital sophistication in your organization. This isn’t as foreign to most good enterprise leaders as I used to think. Sure, folks at the top of most companies are used to offering their opinions. But they’re also pretty experienced at having to make decisions in areas where they aren’t that expert and they know that asking questions is a powerful tool for pushing people to demonstrate (or arrive at) understanding. The key is knowing the right questions to ask. In digital, that usually means asking customer-focused questions like the one’s I enumerated above.

 

Consulting

I’m probably too deeply involved in the sausage-making to give good advice on how organizations should use consulting to drive transformation. But here’s a few pointers that I think are worth bearing in mind. Consulting is a tempting way to solve a reverse hierarchy of understanding. You can bring in hired guns to build a digital strategy or drive specific digital initiatives. And if you’re lucky or choose wisely, there’s no reason why consultants can’t provide real benefits – helping speed up digital initiatives and supplement your organizational expertise. I genuinely believe we do this on a pretty consistent basis. Nevertheless, consultants don’t fix the problems created by a reverse hierarchy of understanding; they are, at best, a band aid. Not only is it too expensive to pay consultants to make your decisions on a continuing basis, it just doesn’t work very well. There are so many reasons why it doesn’t work well that I can attempt only a very partial enumeration: outside of a specific project, your consultant’s KPIs are almost never well aligned with your KPIs (we’re measured by how much stuff we sell), it’s difficult to integrate consultants into a chain of command and often damaging if you try too hard to do so, consultants can become a crutch for weaker managers, and consultants rarely understand your business well enough to make detailed tactical decisions.

Don’t get me wrong. Building talent internally takes time and there aren’t many traditional enterprises where I wouldn’t honestly recommend the thoughtful use of consulting services to help drive digital transformation. Just don’t lose sight of the fact that most of the work is always going to be yours.

 

That last sentence probably rings true across every kind of problem! And while digital transformation is legitimately hard and some of the challenges digital presents ARE different, it’s good to keep in mind that in many respects it is just another problem.

I’ve never believed in one “right” organization, and when it comes to digital transformation there are strong arguments both for and against incubation. I think a decision around incubation ultimately comes down to whether digital needs protection or just expertise. If the former, incubation is probably necessary. If the latter, it may not be. Similarly, we’re all used to the idea that if we need new expertise in an organization we probably have to hire it. But digital introduces two twists. First, the best candidate to lead a digital transformation isn’t necessarily the best digital candidate. Second, real digital transformation doesn’t just come from having a leader or a digital organization. You should bake digital qualifications into hiring at almost every level of your organization. It’s the long game, but it will make a huge difference. And when it comes to leveling the playing field when faced with a reverse hierarchy of knowledge, remember that analytics is your friend. Teaching the organization to use analytics doesn’t require you to be an analytics wizard. It mostly demands that you ask the right questions. Over and over. Finally, and this really is no different in digital transformation than anywhere else, consulting is kind of like a cold medicine – it fixes symptoms but it doesn’t cure the disease. That doesn’t mean I don’t want my bottle of Nyquil handy when I have a cold! It just means I know I won’t wake up all better. The mere fact of a reverse hierarchy of understanding can make over-reliance on consulting a temptation. When you’re used to knowing better than everyone, it’s kind of scary when you don’t. Make sure your digital strategy includes thought about the way to use and not abuse your consulting partners (and no, don’t expect that to come from even the best consultants).

Keep these four lessons in mind, and you’re at least half-way to a real strategy for transformation.

Join me for what I hope will be a really challenging webinar (hosted by the 4A’s) on improving customer experience with analytics. Feb. 15th at 1pm EST.

What You Will Learn

  • How behavioral segmentation creates a framework for continuous improvement
  • How you can most effectively use VoC to enhance a segmentation framework
  • How you can get around the common limitations of in-line VoC, such as sample bias and survey fatigue
  • What changes in the organization are required to really operationalize this type of process

Digital Transformation and the Reverse Hierarchy of Understanding

Why is it so hard for the traditional enterprise to do digital well? That’s the question that lurks at the heart of every digital transformation discussion. After all, there’s plenty of evidence that digital can be done well. No one looks at the myriad FinTech, social, and ecommerce companies that are born digital and says “Why can’t they do digital well?” When digital is in your DNA it seems perfectly manageable. Of course, mastering any complex and competitive field is going to be a challenge. But for companies born into digital, doing it well is just the age-old challenge of doing ANY business well. For most traditional enterprises, however, digital has been consistently hard.

So what is it that makes digital a particular challenge for the traditional enterprise?

That was the topic of my last conversational session at the Digital Analytics Hub this past week in Monterey (and if you didn’t go…well, sucks for you…great conference). And with a group that included analytics leaders in the traditional enterprise across almost every major industry and a couple of new tech and digital pure plays, we had the right people in the room to answer the question. What follows is, for the most part, a distillation of a discussion that was deep, probing, consistently engaging, and – believe it or not – pretty darn enlightening. Everything, in short, that a conversation is supposed to be but, like digital transformation itself, rarely succeeds in being.

There are some factors that make digital a peculiar challenge for everyone – from startup to omni-channel giant. These aren’t necessarily peculiar to the large traditional enterprise.

Digital changes fast. The speed of change in digital greatly exceeds that in most other fields. It’s not that digital is entirely unique here. Digital isn’t the only discipline where, as one participant put it, organizations have to operate in chaos. But digital is at the upper-end of the curve when it comes to pace of change and that constant chaos means that organizations will have to work hard not just to get good at digital, but to stay good at digital.

The speed of change in digital is a contributing factor to and a consequence of the frictionless nature of digital competition and the resulting tendency toward natural monopoly. I recently wrote a detailed explanation of this phenomenon, beginning with the surprising tendency of digital verticals to tend toward monopoly. Why is it that many online verticals are dominated by a single company – even in places like retail that have traditionally resisted monopolization in the physical world? The answer seems to be that in a world with little or no friction, even small advantages can become decisive. The physical world, on the other hand, provides enough inherent friction that gas stations on opposite sides of the street can charge differently for an identical product and still survive.

This absence of friction means that every single digital property is competing against a set of competitors that is at least national in scope and sometimes global. Local markets and the protection they provide for a business to start, learn and grow are much harder to find and protect in the digital world.

That’s a big problem for businesses trying to learn to do digital well.

However, it’s not quite true that it’s an equal problem for every kind of company. In that article on digital monopoly, I argued for the importance of segmentation in combating the tendency toward frictionless monopoly. If you can find a small group of customers that you can serve better by customizing your digital efforts to their particular needs and interests, you may be able to carve out that protected niche that makes it possible to learn and grow.

Big enterprise – by its very (big) nature – loses that opportunity. Most big brands have to try an appeal to broad audience segments in digital. That means they often lack the opportunity to evolve organically in the digital world.

Still, the challenges posed by a frictionless, high-chaos environment are almost as daunting to a digital startup as they are to a traditional enterprise. The third big challenge – the demand in digital for customer centricity – is a little bit different.

Digital environments put a huge premium on the ability to understand who a customer is and provide them a personalized experience across multiple touches. It’s personalization that drives competitive advantage in digital and the deeper and wider you can extend that personalization, the better. Almost every traditional enterprise is setup to silo each aspect of the customer journey. Call-Center owns one silo. Store another. Digital a third. That just doesn’t work very well.

Omni-channel enterprises not only have a harder challenge (more types of touches to handle and integrate), they are almost always setup in a fashion that makes it difficult to provide a consistent customer experience.

Customer-centricity, frictionless competition and rapidity of change are the high-level, big picture challenges that make digital hard for everyone and, in some respects, particularly hard for the large, traditional enterprise.

These top-level challenges result, inevitably, in a set of more tactical problems many of which are specific to the large traditional enterprise that wasn’t created specifically to address them. Looming large among these is the need to develop cross-functional teams (engineers, creative, analytics, etc.) that work together to drive continuous improvement over time. Rapidity of change, frictionless competition and the need for cross-silo customer-centricity make it impossible to compete using a traditional project mentality with large, one-time waterfall developments. That methodology simply doesn’t work.

Large, traditional enterprise is also plagued by IT and Marketing conflicts and Brand departments that are extremely resistant to change and unwilling to submit to measurement discipline. This is all pretty familiar territory and material that I’ve explored before.

Adapting to an environment where IT and Marketing HAVE to work together is hard. A world where traditional budgeting doesn’t work requires fundamental change in organizational process. A system where continuous improvement is essential and where you can’t silo customer data, customer experience or customer thinking is simply foreign to most large enterprises.

This stuff is hard because big organizations are hard to change. To get the change you want, a burning platform may be essential. And, in fact, in our group the teams that had most successfully navigated large enterprise transformation came from places that had been massively disrupted.

No good leader wants to accept that. If you lead a large enterprise, you don’t want to have to wait till your company’s very existence is threatened to drive digital transformation. That sucks.

So the real trick is finding ways to drive change BEFORE massive disruption makes it a question of survival.

And here, a principle I’ve been thinking about and discussed for the first time at the DA Hub enjoyed considerable interest. I call it the reverse hierarchy of understanding.

Organizations work best when an organization’s management hierarchy generally matches to its knowledge hierarchy. And believe it or not, my general experience is that that’s actually the case most of the time. We’re all used to specialized pieces of knowledge and specific expertise existing exclusively deep down in the organization. A financial planner may have deep knowledge of TM1 that the CFO lacks. But I’ve met a fair number of CFO’s and a fair number of financial planners and I can tell you there is usually a world (or perhaps two decades) of difference in their understanding of the business and its financial imperatives.

When that hierarchy doesn’t hold, it’s hard for a business to function effectively. When privates know more than sergeants, and sergeants know more than lieutenants and lieutenants know more than generals, the results aren’t pretty. Tactics and strategy get confused. The rank and file lose faith in their leaders. Leaders – and this may be even worse – tend to lose faith in themselves.

The thing about digital is that it does sometimes create a true reverse hierarchy of understanding in the large traditional enterprise. This doesn’t matter very much when digital is peripheral to the organization. Reverse hierarchies exist in all sorts of peripheral areas of the business and they don’t spell doom. But if digital become core to the organization, allowing a reverse hierarchy to persist is disastrous.

And here’s where digital transformation is incredibly tricky for the large traditional enterprise. You can’t invert the organization. Not only is it impossible, it’s stupid. Large traditional organizations can’t simply abandon what they are – which means that they have to figure out how to work with two separate knowledge hierarchies while they transform.

So the trick with digital transformation is building a digital knowledge hierarchy and finding ways to incorporate it in the existing management hierarchy of the business. It’s also where great leadership makes an enormous difference. Because most companies wait too long to begin that process – ultimately relying on a burning platform to drive the essential change. But while it’s hard to effect complete transformation without the pressure of massive disruption, it’s eminently possible to prepare for transformation by nurturing a digital knowledge hierarchy.

Think of it like FDR building out the U.S. military prior to WWII. He couldn’t fight the war, but he could prepare for it. We tend to define great leaders by what they do in crisis. But effecting change in crisis is relatively easy. The really great leaders have the vision to prepare for change before the onset of crisis.

So how can leadership address a reverse hierarchy of understanding in digital – especially since they are part of the problem? That’s the topic for my next post.

 

[A final thanks to all the great participants in my Digital Analytics Hub Conference session on this topic. You guys were brilliant and I hope this post does at least small justice to the conversation!]

Burning Down the House

Nowhere is the challenge of getting people to understand how to use data better illustrated than the methodology wars being fought in the discipline of Psychology. If you haven’t heard of the methodology wars be assured that the battlefields – studies in psychological research – are being fought over like blocks of Stalingrad; and like that famous battle, not much is left standing in the aftermath.

I’m not sure exactly how the methodology wars started. Somehow, somewhere, someone decided to actually re-test a “classic” study in psychology. A study that’s been accepted into the core of the discipline – that established somebody’s reputation, made somebody a career. Only it didn’t replicate. They re-did the experiment as carefully as they could and it didn’t show the same result. Didn’t, usually, show any result at all. Increase the sample size to fix the problem and the signal becomes even clearer. Alas, the signal always seems to be that there is no signal.

Pretty soon people started calling into question nearly every Psych study done over the last fifty years and testing them. And many – and I mean many – have failed.

Slate’s article (and it’s really good – giving a great overview of the issue – so give it a read) recounts the latest block to burn down in psychology’s methodology wars. The research in question centered around the idea that our facial states feedback into our emotions. If we smile (even inadvertently) we will feel happier.

It’s an interesting idea – intuitively plausible – and apparently widely supported by a huge variety of studies in the field. It’s an idea which strikes me as perfectly reasonable and in which I have zero vested interested one way or another.

But when it was submitted to rigorous simultaneous validation in a number of different labs, it failed. Completely.

The original test involved 32 participants and a change in average subjective scoring between the two groups of 4.4 to 5.5. That means each group had sixteen participants. The improved second test had 92 total participants and showed a scoring difference of 4.3 to 51.

That’s a pretty small sample.

Especially for something that became received wisdom. A classic.

So how would people react if it turned out to be wrong?

Well, the Slate article answers that question pretty definitively. Because when the multi-lab tests came back, here’s what happened. Seventeen different labs replicated the experiment with nearly 2000 subjects. In half the participating labs, participants who smiled recorded a slightly higher average on the resulting happiness test (but much lower than in the original experiment). In the other half, it went the other way.

Net, net, there was no correlation at all. Zero.

Okay, so far you have just another sad story of a small sample size failure.

That’s not what really attracted my attention. Nope. What really made me laugh in utter disbelief was the comment of the “scientist” who had done the original research. Here it is, and I quote in full lest you think I’m about to exaggerate:

“Fritz Strack has no regrets about the RRR, but then again, he doesn’t take its findings all that seriously. “I don’t see what we’ve learned,” he said.

Two years ago, while the replication of his work was underway, Strack wrote a takedown of the skeptics’ project with the social psychologist Wolfgang Stroebe. Their piece, called “The Alleged Crisis and the Illusion of Exact Replication,” argued that efforts like the RRR reflect an “epistemological misunderstanding,” since it’s impossible to make a perfect copy of an old experiment. People change, times change, and cultures change, they said. No social psychologist ever steps in the same river twice. Even if a study could be reproduced, they added, a negative result wouldn’t be that interesting, because it wouldn’t explain why the replication didn’t work.

So when Strack looks at the recent data he sees not a total failure but a set of mixed results. Nine labs found the pen-in-mouth effect going in the right direction. Eight labs found the opposite. Instead of averaging these together to get a zero effect, why not try to figure out how the two groups might have differed? Maybe there’s a reason why half the labs could not elicit the effect.

[Bolding is mine]

So here’s a “scientist” who, despite presumably being familiar with the extensive literature on statistics and the methodology wars, somehow believes that because half the labs reported a number slightly above average the key thing to look at is why the other half didn’t. Apparently, the only thing that would satisfy him is if all the labs reported an exactly opposite result. Which, presumably, would result in a new classic paper that frowning makes you happier!

Ring, Ring!

Clue Phone.

It’s random variation calling for you, Dr. Strack!

Cause here’s the thing…you’d expect about half the labs to show a positive result when there is no correlation. If some labs didn’t report a positive result then the correlation would pretty much have to be negative, right?

This isn’t, as he appears to believe, half-corroboration. It’s the way every null result ever found actually looks out here in the real world. I’d advise him to try flipping a coin 100 times, repeatedly, and see how often it comes out 50 heads and 50 tails. He might be surprised to learn that about the half the time this test will yield more heads flips than tail flips. This does not mean that heads is more likely than tails and it does not suggest that researchers should focus on why some trials yielded more heads and other trials yielded more tails.

 

Okay, I get it. You published a study. You made a career out of it. It’s embarrassing that it turns out to be wrong. But it’s hard to know in this case which is the worse response – intellectual dishonesty or sheer stupidity. Frankly, I think the latter. Because I don’t care how dishonest you are, some explanations should be too embarrassing to try on for size. And the idea that the right interpretation of these results would be to look for why some labs had slightly different results than others clearly belongs in that category.

I find the defense based on the difficulties of true replication more respectable. And yet, what are we to make of an experiment so delicate that it can’t be replicated AT ALL even with the most careful controls? How important can any inference we make from such an experiment plausibly be? By definition, it could only fit the most narrow range of cases imaginable. And the idea that replication of an experiment doesn’t matter seems…you know…a tad unscientific.

From my perspective, it isn’t the original study that illustrates the extraordinary problem we have getting people to use data well. Yes, over-reliance on small sample sizes is all too common and all too easy. That’s unfortunate, not shameful. But the deeper problem is that even when data is used well, a lethal combination of self-interest and a near total lack of understanding of basic statistics make it all too possible for people to ignore the data whenever they wish.

As Simon and Garfunkel plaintively observed, “A man hears what he wants to hear, and disregards the rest”.

If it wasn’t so sad, it would be funny.

Dammit. It is funny.

For it’s easy to see that in this version of the psych methodology wars, the defenders have their own unique version of a foxhole – with posterior high in the air and head firmly planted in the sand.

[Getting close to the Digital Analytics Hub. If you love talking analytics, check it out. Would be great to see you there!]