Tag Archives: enterprise analytics

Digital Transformation Dialogues – Part 3 – Bringing an Older Workforce up to Speed and Driving Adoption of Digital Tools

[Here’s more from my ongoing dialogue with transformation expert and friend Scott K Wilder. In the last post, we discussed the role of Millennials in balancing an older workforce. But I wanted a little more detail on how to get an older workforce more digitally aware…]

SW: I probably forgot this one because I am an older guy, but I’m also someone who thinks it’s every marketer’s responsibility to learn digital technology. Before I directly answer the question, let me give you an example. My son is really into drones and wants me to take him to some national parks so he can fly his drone. Before I make a road trip with him, however, I want to master drones, so I hired a drone coach. After all, I am the one who is ultimately responsible for my son’s safety. Working at as a Digital Marketer or Digital Employee requires the same commitment. The only difference, however, is that companies need to play a bit of the parental role and provide a clear path for their employees to learn about technology.

This can be done by paying for courses (Marketo, my former employer, pays for its employees to take courses at Lynda.com). It can be done by making ‘learning certain technologies’ as required for the job. Instead of saying you learn it or you lose it (your job), position this change as an opportunity to skill up — and that the company is investing in the future (in its best asset, its employees).

Companies also should provide career guidance — either for older employees to find other opportunities within their company or with a company’s partner. Training, career guidance are not only great retention tools, but also build loyalty after an employee moves on.

Companies also need to gently require that digital technologies be used in their everyday business practices. If the older person wants to remain part of the company, they will have to hop on the digital bus. And like the Magic School Bus (a book my kid loves), it will be a journey into unknown — with lots of opportunity to learn, a bit of uncertainty and a fun adventure. You know what. Even outside the office, they will feel as if they are on the Magic School Bus because by learning technology, older folks can have a more enriched life. My son Facetimes and Skypes with his Grandma twice a week.

Why should companies do this? Why should they make this investment? Several reasons, such as older workers tend to be loyal, older workers already know ‘your business’. Companies should also build incentive systems — gamify their career development — so they will be motivated to take on the exciting challenge of improving their skills.

Final note: Being Digital is more than just using the internet and Facebook. Companies should also figure out what digital technologies will help these older workers do their job better. If they need to be on social media, teach them Hootsuite. If they need to manage email programs, teach them Autopilot or Exact Target. If they need to collaborate better, be their guide while they learn Slack or HipChat.

GA: There’s a couple of points that I want to particularly call-out there. One is that company’s aren’t taking full advantage of the explosion in high-quality educational courseware that’s available these days. Sure, lots of folks will do this on their own, but not everyone is sufficiently motivated. I’ve always said my number one guiding principal – and the reason transformation is so hard – is that EVERYONE IS FUNDAMENTALLY LAZY. Giving people real incentives and formal guidance on courseware so that it’s part of an employee’s basic career development is really easy to do and I think pays tremendous dividends. If your company hasn’t curated public courseware for specific career-tracks and incentivized your employees to take advantage, you should be kicking your HR team’s butt (just my humble opinion).

I’m also a huge fan of the idea (as you know) that people have to DO stuff. And I’m glad you brought up the technologies because that’s the next (and last) area I wanted to explore. A lot of the digital technologies are fundamentally collaborative. But that can make adoption critical to their success. I know you’ve been living this problem – how do you get a team (and keep my older, non-digital workers in mind) to adopt tools like Slack?

SW: Gary, why are you always asking me the hard questions? I think you ‘re correct in focusing on ‘the team’ vs. ‘the company’ and trying to mandate day 1 that a whole company start using something like Slack.

They key is to start with one group.  Pick a team that seems receptive to taking on new ways of doing things — especially when it comes to digital technology. And within that group, you should also identify a few key digital change agents, early adopters, who are willing to not only try out the new technology, but also be champions for it.

Create a program for these digital champions. It can be rewards focused, but even better,  show them how sharing their knowledge and experience will help them learn a new technology even better and make them more marketable. Intuit, where I spent almost a full decade, has a philosophy called “Learn Teach Learn.” The only way to really learn something is to teach it to others (Intuit has a great learning culture!).

Of course, there is another option. You could see if any group in the company is currently using Slack and make them that group ‘your change agents. At Marketo, it was actually the company’s commuters — employees who took a small shuttle bus that looked like one of those vans old age homes use to transport its frail residents – who started using Slack. They let their fellow workers know if they wanted the bus to wait for them or if they wanted the van to turn around and pick up someone they forgot. My group of commuters called our Slack group, The Purple Lobster.

The Slack group was called the Purple Lobster because that’s what we called the van. We picked purple because that was Marketo’s company color. And lobster because it wasn’t the fastest moving vehicle on Highway 101.

And like a lobster slithering in the sand (sorry about pushing the poetic envelop here) slowly, but surely ,other commuting groups started to using Slack. Eventually, product teams started using it And finally, the CTO and his team made the call to not fight the crowd and force the company to use another tool, like Chatter. Instead, CIO convinced his fellow executives to adopt Slack across the company. It was a brilliant ‘if you can’t beat them, then join them’ strategy.

If you identify a group using Slack, challenge them to go completely cold Turkey. See if they are willing to only use Slack only (no email) for a week or so. At Atlassian, I had my hand Slacked when I tried to send an email to someone with a simple question. They recommended I use their Slack like product, Hipchat. And now, I only have 20 emails in my inbox. How many of you have only 20 emails in your corporate email inbox?

If you are not so lucky to find early adopters, you need to find a group of people who are most like to use the new technology. If there are some older folks on the team, pair them up with the younger wipper snappers. Or provide some training.

The key in all this is not to focus on technology. Instead, treat the change to Slack or any other digital technology as a change management exercise. Focus on adoption — education, onboarding and engagement. None of this should be done in a vacuum. You need someone to shepherd the process. Someone who can be a guide, a teacher, a problem solver and yes, a true change agent.

Other considerations include rewarding people for their efforts and successes. Gamify the process! In doing so, make sure to acknowledge people’s efforts for trying. Don’t make the same mistake most schools make and only pass people for knowing the answer. As Carol Dweck, well known motivational researcher,  points out, children praised for hard work chose problems that promised increased learning (vs. just getting the right answer). This also applies to adults. Really!

The key here is to alter someone’s mindset. Instead of rewarding (just giving them a bonus) or punishing someone (not promoting them) for adopting a new technology, recognize their effort and hard work. The end result will be they might adopt taking on new challenges and succeeding at them. Even if it means learning and using something like Slack.

Finally good old training is important. It always amazes me how many companies introduce a new technology and offer one time training. Usually during a three hour class. If you are licensing a technology like Slack, see if they can conduct monthly webinars to answer questions (if not, you offer it). Also have videos and Q&As available for your staff.

Personally, I would rather be HipChatted vs. Slacked. But technology sometimes like religion. You have to find out what people are most comfortable with. At Marketo, it was Slack. At Salesforce, it is Chatter. For me, I prefer to be Skyped!. How about you?

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…

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!]

A Guided Tour through Digital Analytics (Circa 2016)

I’ve been planning my schedule for the DA Hub in late September and while I find it frustrating (so much interesting stuff!), it’s also enlightening about where digital analytics is right now and where it’s headed. Every conference is a kind of mirror to its industry, of course, but that reflection is often distorted by the needs of the conference – to focus on the cutting-edge, to sell sponsorships, to encourage product adoption, etc.  With DA Hub, the Conference agenda is set by the enterprise practitioners who are leading groups – and it’s what they want to talk about. That makes the conference agenda unusually broad and, it seems to me, uniquely reflective of the state of our industry (at least at the big enterprise level).

So here’s a guided tour of my DA Hub – including what I thought was most interesting, what I choose, and why. At the end I hope that, like Indiana Jones picking the Holy Grail from a murderers row of drinking vessels, I chose wisely.

Session 1 features conversations on Video Tracking, Data Lakes, the Lifecycle of an Analyst, Building Analytics Community, Sexy Dashboards (surely an oxymoron), Innovation, the Agile Enterprise and Personalization. Fortunately, while I’d love to join both Twitch’s June Dershewitz to talk about Data Lakes and Data Swamps or Intuit’s Dylan Lewis for When Harry (Personalization) met Sally (Experimentation), I didn’t have to agonize at all, since I’m scheduled to lead a conversation on Machine Learning in Digital Analtyics. Still, it’s an incredible set of choices and represents just how much breadth there is to digital analytics practice these days.

Session 2 doesn’t make things easier. With topics ranging across Women in Analytics, Personalization, Data Science, IoT, Data Governance, Digital Product Management, Campaign Measurement, Rolling Your Own Technology, and Voice of Customer…Dang. Women in Analytics gets knocked off my list. I’ll eliminate Campaign Measurement even though I’d love to chat with Chip Strieff from Adidas about campaign optimization. I did Tom Bett’s (Financial Times) conversation on rolling your own technology in Europe this year – so I guess I can sacrifice that. Normally I’d cross the data governance session off my list. But not only am I managing some aspects of a data governance process for a client right now, I’ve known Verizon’s Rene Villa for a long time and had some truly fantastic conversations with him. So I’m tempted. On the other hand, retail personalization is of huge interest to me. So talking over personalization with Gautam Madiman from Lowe’s would be a real treat. And did I mention that I’ve become very, very interested in certain forms of IoT tracking? Getting a chance to talk with Vivint’s Brandon Bunker around that would be pretty cool. And, of course, I’ve spent years trying to do more with VoC and hearing Abercrombie & Fitch’s story with Sasha Verbitsky would be sweet. Provisionally, I’m picking IoT. I just don’t get a chance to talk IoT very much and I can’t pass up the opportunity. But personalization might drag me back in.

In the next session I have to choose between Dashboarding (the wretched state of as opposed to the sexiness of), Data Mining Methods, Martech, Next Generation Analytics, Analytics Coaching, Measuring Content Success, Leveraging Tag Management and Using Marketing Couds for Personalization. The choice is a little easier because I did Kyle Keller’s (Vox) conversation on Dashboarding two years ago in Europe. And while that session was probably the most contentious DA Hub group I’ve ever been in (and yes, it was my fault but it was also pretty productive and interesting), I can probably move on. I’m not that involved with tag management these days – a sign that it must be mature – so that’s off my list too. I’m very intrigued by Akhil Anumolu’s (Delta Airlines) session on Can Developers be Marketers? The Emerging Role of MarTech. As a washed-up developer, I still find myself believing that developers are extraordinarily useful people and vastly under-utilized in today’s enterprise. I’m also tempted by my friend David McBride’s session on Next Generation Analytics. Not only because David is one of the most enjoyable people that I’ve ever met to talk with, but because driving analytics forward is, really, my job. But I’m probably going to go with David William’s session on Marketing Clouds. David is brilliant and ASOS is truly cutting edge (they are a giant in the UK and global in reach but not as well known here), and this also happens to be an area where I’m personally involved in steering some client projects. David’s topical focus on single-vendor stacks to deliver personalization is incredibly timely for me.

Next up we have Millennials in the Analytics Workforce, Streaming Video Metrics, Breaking the Analytics Glass Ceiling, Experimentation on Steroids, Data Journalism, Distributed Social Media Platforms, Customer Experience Management, Ethics in Analytics(!), and Customer Segmentation. There are several choices in here that I’d be pretty thrilled with: Dylan’s session on Experimentation, Chip’s session on CEM and, of course, Shari Cleary’s (Viacom) session on Segmentation. After all, segmentation is, like, my favorite thing in the world. But I’m probably going to go with Lynn Lanphier’s (Best Buy) session on Data Journalism. I have more to learn in that space, and it’s an area of analytics I’ve never felt that my practice has delivered on as well as we should.

In the last session, I could choose from more on Customer Experience Management, Driving Analytics to the C-Suite, Optimizing Analytics Career-Oaths, Creating High-Impact Analytics Programs, Building Analytics Teams, Delivering Digital Products, Calculating Analytics Impact, and Moving from Report Monkey to Analytics Advisor. But I don’t get to choose. Because this is where my second session (on driving Enterprise Digital Transformation) resides. I wrote about doing this session in the EU early this summer – it was one of the best conversations around analytics I’ve had the pleasure of being part of. I’m just hoping this session can capture some of that magic. If I didn’t have hosting duties, I think I might gravitate toward Theresa Locklear’s (NFL) conversation on Return on Analytics. When we help our clients create new analytics and digital transformation strategies, we have to help them justify what always amount to significant new expenditures. So much of analytics is exploratory and foundational, however, that we don’t always have great answers about the real return. I’d love to be able to share thoughts on how to think (and talk) about analytics ROI in a more compelling fashion.

All great stuff.

We work in such a fascinating field with so many components to it. We can specialize in data science and analytics method, take care of the fundamental challenges around building data foundations, drive customer communications and personalization, help the enterprise understand and measure it’s performance, optimize relentlessly in and across channels, or try to put all these pieces together and manage the teams and people that come with that. I love that at a Conference like the Hub I get a chance to share knowledge with (very) like-minded folks and participate in conversations where I know I’m truly expert (like segmentation or analytics transformation), areas where I’d like to do better (like Data Journalism), and areas where we’re all pushing the outside of the envelope (IoT and Machine Learning) together. Seems like a wonderful trade-off all the way around.

See you there!
See you there!

https://www.digitalanalyticshub.com/dahub16-us/

 

The State of the Art in Analytics – EU Style

(You spent your vacation how?)

I spent most of the last week at the fourth annual Digital Analytics Hub Conference outside London, talking analytics. And talking. And talking. And while I love talking analytics, thank heavens I had a few opportunities to get away from the sound of my own voice and enjoy the rather more pleasing absence of sounds in the English countryside.

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With X Change no more, the Hub is the best conference going these days in digital analytics (full disclosure – the guys who run it are old friends of mine). It’s an immensely enjoyable opportunity to talk in-depth with serious practitioners about everything from cutting edge analytics to digital transformation to traditional digital analytics concerns around marketing analytics. Some of the biggest, best and most interesting brands in Europe were there: from digital and bricks-and-mortar behemoths to cutting-edge digital pure-plays to a pretty good sampling of the biggest consultancies in and out of the digital world.

As has been true in previous visits, I found the overall state of digital analytics in Europe to be a bit behind the U.S. – especially in terms of team-size and perhaps in data integration. But the leading companies in Europe are as good as anybody.

Here’s a sampling from my conversations:

Machine Learning

I’ve been pushing my team to grow in the machine learning space using libraries like TensorFlow to explore deep learning and see if it has potential for digital. It hasn’t been simple or easy. I’m thinking that people who talk as if you can drop a digital data set into a deep learning system and have magic happen have either:

  1. Never tried it
  2. Been trying to sell it

We’ve been having a hard time getting deep learning systems to out-perform techniques like Random Forests. We have a lot of theories about why that is, including problem selection, certain challenges with our data sets, and the ways we’ve chosen to structure our input. I had some great discussions with hardcore data scientists (and some very bright hacker analysts more in my mold) that gave me some fresh ideas. That’s lucky because I’m presenting some of this work at the upcoming eMetrics in Chicago and I want to have more impressive results to share. I’ve long insisted on the importance of structure to digital analytics and deep learning systems should be able to do a better job parsing that structure into the analysis than tools like random forests. So I’m still hopeful/semi-confident I can get better results.

In broader group discussion, one of the most controversial and interesting discussions focused on the pros-and-cons of black-box learning systems. I was a little surprised that most of the data scientist types were fairly negative on black-box techniques. I have my reservations about them and I see that organizations are often deeply distrustful of analytic results that can’t be transparently explained or which are hidden by a vendor. I get that. But opacity and performance aren’t incompatible. Just try to get an explanation of Google’s AlphaGo! If you can test a system carefully, how important is model transparency?

So what are my reservations? I’m less concerned about the black-boxness of a technique than I am its completeness. When it comes to things like recommendation engines, I think enterprise analysts should be able to consistently beat a turnkey blackbox (or not blackbox) system with appropriate local customization of the inputs and model. But I harbor no bias here. From my perspective it’s useful but not critical to understand the insides of a model provided we’ve been careful testing to make sure that it actually works!

Another huge discussion topic and one that I more in accord with was around the importance of not over-focusing on a single technique. Not only are there many varieties of machine learning – each with some advantages to specific problem types – but there are powerful analytic techniques outside the sphere of machine learning that are used in other disciplines and are completely untried in digital analytics. We have so much to learn and I only wish I had more time with a couple of the folks there to…talk!

New Technology

One of the innovations this year at the Hub was a New Technology Showcase. The showcase was kind of like spending a day with a Silicon Valley VC and getting presentations from the technology companies in their portfolio (which is a darn interesting way to spend a day). I didn’t know most of the companies that presented but there were a couple (Piwik and Snowplow) I’ve heard of. Snowplow, in particular, is a company that’s worth checking out. The Snowplow proposition is pretty simple. Digital data collection should be de-coupled from analysis. You’ve heard that before, right? It’s called Tag Management. But that’s not what Snowplow has in mind at all. They built a very sophisticated open-source data collection stack that’s highly performant and feeds directly into the cloud. The basic collection strategy is simple and modern. You send json objects that pass a schema reference along with the data. The schema references are versioned and updates are handled automatically for both backwardly compatible and incompatible updates. You can pass a full range of strongly-typed data and you can create cross-object contexts for things like visitors. Snowplow has built a whole bunch of simple templates to make it easier for folks used to traditional tagging to create the necessary calls. But you can pass anything to Snowplow – not just Web data. It’s very adaptable for mobile (far more so than traditional digital analytics systems) and really for any kind of data at all. Snowplow supports both real-time and batch – it’s a true lambda architecture. It seems to do a huge amount of the heavy lifting for you when it comes to creating a  modern cloud-based data collection system. And did I mention it’s open-source? Free is a pretty good price. If you’re looking for an independent data collection architecture and are okay with the cloud, you really should give it a look.

Cloud vs. On-Premise

DA Hub’s keynote featured a panel with analytics leaders from companies like Intel, ASOS and the Financial Times. Every participant was running analytics in the cloud (with both AWS and Azure represented though AWS had an unsurprising majority). Except for barriers around InfoSec, it’s unclear to me why ANY company wouldn’t be in the cloud for their analytics.

Rolling your own Technology

We are not sheep
We are not sheep

Here in the States, there’s been widespread adoption of open-source data technologies (Hadoop/Spark) to process and analyze digital data. But while I do see companies that have completely abandoned traditional SaaS analytics tools, it’s pretty rare. Mostly, the companies I see run both a SaaS solution to collect data and (perhaps) satisfy basic reporting needs as well as an open-source data platform. There was more interest in the people I talked to in the EU about a complete swap out including data collection and reporting. I even talked to folks who roll most of the visualization stack themselves with open-source solutions like D3. There are places where D3 is appropriate (you need complete customization of the surrounding interface, for example, or you need widespread but very inexpensive distribution), but I’m very far from convinced that rolling your own visualization solutions with open-source is the way to go. I would have said that same thing about data collection but…see above.

Digital Transformation

I had an exhilarating discussion group centered around digital transformation. There were a ton of heavy hitters in the room – huge enterprises deep into projects of digital transformation, major consultancies, and some legendary industry vets. It was one of the most enjoyable conference experiences I’ve ever had. I swear that we (most of us anyway) could have gone on another 2 hours or more – since we just scratched the surface of the problems. My plan for the session was to cover what defines excellence in digital (what do you have to be able to do digital well), then tackle how a large-enterprise that wants to transform in digital needs to organize itself. Finally, I wanted to cover the change management and process necessary to get from here to there. If you’re reading this post that should sound familiar!

Lane
It’s a long path

Well, we didn’t get to the third item and we didn’t finish the second. That’s no disgrace. These are big topics. But the discussion helped clarify my thinking – especially around organization and the very real challenges in scaling a startup model into something that works for a large enterprise. Much of the blending of teams and capabilities that I’ve been recommending in these posts on digital transformation are lessons I’ve gleaned from seeing digital pure-plays and how they work. But I’ve always been uncomfortably aware that the process of scaling into larger teams creates issues around corporate communications, reporting structures, and career paths that I’m not even close to solving. Not only did this discussion clarify and advance my thinking on the topic, I’m fairly confident that it was of equal service to everyone else. I really wish that same group could have spent the whole day together. A big THANKS to everyone there, you were fantastic!

I plan to write more on this in a subsequent post. And I may drop another post on Hub learnings after I peruse my notes. I’ve only hit on the big stuff – and there were a lot of smaller takeaways worth noting.

See you there!
See you there!

As I mentioned in my last post, the guys who run DA Hub are bringing it to Monterey, CA (first time in the U.S.) this September. Do check it out. It’s worth the trip (and the venue is  pretty special). I think I’m on the hook to reprise that session on digital transformation. And yes, that scares me…you don’t often catch lightning in a bottle twice.

Practical Steps to Building an Analytics Culture

Building an analytics culture in the enterprise is incredibly important. It’s far more important than any single capability, technology or technique. But building culture isn’t easy. You can’t buy it. You can’t proclaim it. You can’t implement it.

There is, of course, a vast literature on building culture in the enterprise. But if the clumsy, heavy-handed, thoroughly useless attempts to “build culture” that I’ve witnessed over the course of my working life are any evidence, that body of literature is nearly useless.

Here’s one thing I know for sure: you don’t build culture by talk. I don’t care whether it’s getting teenagers to practice safe-sex or getting managers to use analytics, preaching virtue doesn’t work, has never worked and will never work. Telling people to be data-driven, proclaiming your commitment to analytics, touting your analytics capabilities: none of this builds analytics culture.

If there’s one thing that every young employee has learned in this era, it’s that fancy talk is cheap and meaningless. People are incredibly sophisticated about language these days. We can sit in front of the TV and recognize in a second whether we’re seeing a commercial or a program. Most of us can tell the difference between a TV show and movie almost at a glance. We can tune out advertising on a Website as effortlessly as we put on our pants. A bunch of glib words aren’t going to fool anyone. You want to know what the reaction is to your carefully crafted, strategic consultancy driven mission statement or that five year “vision” you spent millions on and just rolled out with a cool video at your Sales Conference? Complete indifference.

That’s if you’re lucky…if you didn’t do it really well, you got the eye-roll.

But it isn’t just that people are incredibly sensitive – probably too sensitive – to BS. It’s that even true, sincere, beautifully reasoned words will not build culture. Reading moral philosophy does not create moral students. Not because the words aren’t right or true, but because behaviors are, for the most part, not driven by those types of reasons.

That’s the whole thing about culture.

Culture is lived, not read or spoken. To create it, you have to ingrain it in people’s thinking. If you want a data-driven organization, you have to create good analytic habits. You have to make the organization (and you too) work right.

How do you do that?

You do it by creating certain kinds of process and behaviors that embed analytic thinking. Do enough of that, and you’ll have an analytic culture. I guarantee it. The whole thrust of this recent series of posts is that by changing the way you integrate analytics, voice-of-customer, journey-mapping and experimentation into the enterprise, you can drive better digital decision making. That’s building culture. It’s my big answer to the question of how you build analytics culture.

But I have some small answers as well. Here, in no particular order, are practical ways you can create importantly good analytics habits in the enterprise.

Analytic Reporting

What it is: Changing your enterprise reporting strategy by moving from reports to tools. Analytic models and forecasting allow you to build tools that integrate historical reporting with forecasting and what-if capabilities. Static reporting is replaced by a set of interactive tools that allow users to see how different business strategies actually play-out.

Why it build analytics culture: With analytics reporting, you democratize knowledge not data. It makes all the difference in the world. The analytic models capture your best insight into how a key business works and what levers drive performance. Building this into tools not only operationalizes the knowledge, it creates positive feedback loops to analytics. When the forecast isn’t right, everyone know it and the business is incented to improve its understanding and predictive capabilities. This makes for better culture in analytics consumers and analytics producers.

 

Cadence of Communications

What it is: Setting up regular briefings between analytics and your senior team and decision-makers. This can include review of dashboards but should primarily focus on answers to previous business questions and discussion of new problems.

Why it builds analytics culture: This is actually one of the most important things you can do. It exposes decision-makers to analytics. It makes it easy for decision-makers to ask for new research and exposes them to the relevant techniques. Perhaps even more important, it lets decision-makers drive the analytics agenda, exposes analysts to real business problems, and forces analysts to develop better communication skills.

 

C-Suite Advisor

What it is: Create an Analytics Minister-without-portfolio whose sole job is to advise senior decision-makers on how to use, understand and evaluate the analytics, the data and the decisions they get.

Why it builds analytics culture: Most senior executives are fairly ignorant of the pitfalls in data interpretation and the ins-and-outs of KPIs and experimentation. You can’t send them back to get a modern MBA, but you can give them a trusted advisor with no axe to grind. This not only raises their analytics intelligence, it forces everyone feeding them information to up their game as well. This tactic is also critical because of the next strategy…

 

Walking the Walk

What it is: Senior Leaders can talk tell they are blue in the face about data-driven decision-making. Nobody will care. But let a Senior Leader even once use data or demand data around a decision they are making and the whole organization will take notice.

Why it builds analytics culture: Senior leaders CAN and DO have a profound impact on culture but they do so by their behavior not their words. When the leaders at the top use and demand data for decisions, so will everyone else.

 

Tagging Standards

What it is: A clearly defined set of data collection specifications that ensure that every piece of content on every platform is appropriately tagged to collect a rich set of customer, content, and behavioral data.

Why it builds analytics culture: This ends the debate over whether tags and measurement are optional. They aren’t. This also, interestingly, makes measurement easier. Sometimes, people just need to be told what to do. This is like choosing which side of the road to drive on – it’s far more important that you have a standard that which side of the road you pick. Standards are necessary when an organization needs direction and coordination. Tagging is a perfect example.

 

CMS and Campaign Meta-Data

What it is: The definition of and governance around the creation of campaign and content meta-data. Every piece of content and every campaign element should have detailed, rich meta-data around the audience, tone, approach, contents, and every other element that can be tuned and analyzed.

Why it builds analytics culture: Not only is meta-data the key to digital analytics – providing the meaning that makes content consumption understandable, but rich meta-data definition guides useful thought. These are the categories people will think about when they analyze content and campaign performance. That’s as it should be and by providing these pre-built, populated categorizations, you’ll greatly facilitate good analytics thinking.

 

Rapid VoC

What it is: The technical and organizational capability to rapidly create, deploy and analyze surveys and other voice-of-customer research instruments.

Why it builds analytics culture: This is the best capability I know for training senior decision-makers to use research. It’s so cheap, so easy, so flexible and so understandable that decision-makers will quickly get spoiled. They’ll use it over and over and over. Well – that’s the point. Nothing builds analytics muscle like use and getting this type of capability deeply embedded in the way your senior team thinks and works will truly change the decision-making culture of the enterprise.

 

SPEED and Formal Continuous Improvement Cycles

What it is: The use of a formal methodology for digital improvement. SPEED provides a way to identify the best opportunities for digital improvement, the ways to tackle those opportunities, and the ability to measure the impact of any changes. It’s the equivalent of Six Sigma for digital.

Why it builds analytics culture: Formal methods make it vastly easier for everyone in the organization to understand how to get better. Methods also help define a set of processes that organizations can build their organization around. This makes it easier to grow and scale. For large enterprises, in particular, it’s no surprise that formal methodologies like Six Sigma have been so successful. They make key cultural precepts manifest and attach processes to them so that the organizational inertia is guided in positive directions.

 

Does this seem like an absurdly long list? In truth I’m only about half-way through. But this post is getting LONG. So I’m going to save the rest of my list for next week. Till then, here’s some final thoughts on creating an analytics culture.

The secret to building culture is this: everything you do builds culture. Some things build the wrong kind of culture. Some things the right kind. But you are never not building culture. So if you want to build the right culture to be good at digital and decision-making, there’s no magic elixir, no secret sauce. There is only the discipline of doing things right. Over and over.

That being said, not every action is equal. Some foods are empty of nutrition but empty, too, of harm. Others positively destroy your teeth or your waistline. Still others provide the right kind of fuel. The things I’ve described above are not just a random list of things done right, they are the small to medium things that, done right, have the biggest impacts I’ve seen on building a great digital and analytics culture. They are also targeted to places and decisions which, done poorly, will deeply damage your culture.

I’ll detail some more super-foods for analytics culture in my next post!

 

[Get your copy of Measuring the Digital World – the definitive guide to the discipline of digital analytics – to learn more].