Tag Archives: data science

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.

IMG_3757

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.

Digital Transformation in the Enterprise – Creating Continuous Improvement

I’m writing this post as I fly to London for the Digital Analytics Hub. The Hub is in its fourth year now (two in Berlin and two in London) and I’ve managed to make it every time. Of course, doing these Conference/Vacations is a bit of a mixed blessing. I really enjoyed my time in Italy but that was more vacation than Conference. The Hub is more Conference than vacation – it’s filled with Europe’s top analytics practitioners in deep conversation on analytics. In fact, it’s my favorite analytics conference going right now. And here’s the good news, it’s coming to the States in September! So I have one more of these analytics vacations on my calendar and that should be the best one of all. If you’re looking for the ultimate analytics experience – an immersion in deep conversation with the some of the best analytics practitioners around – you should check it out.

I’ve got three topics I’m bringing to the Hub. Machine Learning for digital analytics, digital analytics forecasting and, of course, the topic at hand today, enterprise digital transformation.

In my last post, I described five initiatives that lay the foundation for analytics driven digital transformation. Those projects focus on data collection, journey mapping, behavioral segmentation, enterprise Voice of Customer (VoC) and unified marketing measurement. Together, these five initiatives provide a way to think about digital from a customer perspective. The data piece is focused on making sure that data collection to support personalization and segmentation is in place. The Journey mapping and the behavioral segmentation provide the customer context for every digital touchpoint – why it exists and what it’s supposed to do. The VoC system provides a window into who customers want and need and how they make decisions at every touchpoint. Finally, the marketing framework ensures that digital spend is optimized on an apples-to-apples basis and is focused on the right customers and actions to drive the business.

In a way, these projects are all designed to help the enterprise think and talk intelligently about the digital business. The data collection piece is designed to get organizations thinking about personalization cues in the digital experience. Journey mapping is designed to expand and frame customer experience and place customer thinking at the center of the digital strategy. Two-tiered segmentation serves to get people talking about digital success in terms of customer’s and their intent. Instead of asking questions like whether a Website is successful, it gets people thinking about whether the Website is successful for a certain type of customer with a specific journey intent. That’s a much better way to think. Similarly, the VoC system is all about getting people to focus on customer and to realize that analytics can serve decision-making on an ongoing basis. The marketing framework is all about making sure that campaigns and creative are measured to real business goals – set within the customer journey and the behavioral segmentation.

The foundational elements are also designed to help integrate analytics into different parts of the digital business. The data collection piece is targeted toward direct response optimization. Journey mapping is designed to help weld strategic decisions to line manager responsibilities. Behavioral segmentation is focused on line and product managers needing tactical experience optimization. VoC is targeted toward strategic thinking and decision-making, and, of course, the marketing framework is designed to support the campaign and creative teams.

If a way to think and talk intelligently about the digital enterprise and its operations is the first step, what comes next?

All five of the initiatives that I’ve slated into the next phase are about one thing – creating a discipline of continuous improvement in the enterprise. That discipline can’t be built on top of thin air – it only works if your foundation (data, metrics, framework) supports optimization. Once it does, however, the focus should be on taking advantage of that to create continuous improvement.

The first step is massive experimentation via an analytics driven testing plan. This is partly about doing lots of experiments, yes. But even more important is that the experimentation be done as part of an overall optimization plan with tests targeted by behavioral and VoC analytics to specific experiences where the opportunity for improvement is highest. If all you’re thinking about is how many experiments you run, you’re not doing it right. Every type of customer and every part of their journey should have tests targeted toward its improvement.

Similarly on the marketing side, phase II is about optimizing against the unified measurement framework with both mix and control group testing. Mix is a top-down approach that works against your overall spending – regardless of channel type or individual measurement. Control group testing is nothing more than experimentation in the marketing world. Control groups have been a key part of marketing since the early direct response days. They’re easier to implement and more accurate in establishing true lift and incrementality than mathematical attribution solutions.

The drive toward continuous improvement doesn’t end there, however. I’m a big fan for tool-based reporting as a key part of the second phase of analytics driven transformation. The idea behind tool-based reporting is simple but profound. Instead of reports as static, historical tools to describe what happened, the idea is that reports contain embedded predictive models that transform them into tools that can be used to understand the levers of the business and test what might happen based on different business strategies. Building tool-based reports for marketing, for product launch, for conversion funnels and for other key digital systems is deeply transformative. I describe this as shift in the organization from democratizing data to democratizing knowledge. Knowledge is better. But the advantages to tool-based reporting run even deeper. The models embedded in these reports are your best analytic thinking about how the business works. And guess what? They’ll be wrong a lot of the time and that’s a good thing. It’s a good thing because by making analytically thinking about how the business works explicit, you’ve created feedback mechanisms in the organization. When things don’t work out the way the model predicts, your analysts will hear about it and have to figure out why and how to do better. That drives continuous improvement in analytics.

A fourth key part of creating the agile enterprise – at least for sites without direct ecommerce – is value-based optimization. One of the great sins in digital measurement is leaving gaps in your ability to measure customers across their journey. I call this “closing measurement loops”. If you’re digital properties are lead generating or brand focused or informational or designed to drive off-channel or off-property (to Amazon or to a Call-Center), it’s much harder to measure whether or not they’re successful. You can measure proxies like content consumption or site satisfaction, but unless these proxies actually track to real outcomes, you’re just fooling yourself. This is important. To be good at digital and to use measurement effectively, every important measurement gap needs to be closed. There’s no one tool or method for closing measurement gaps, instead, a whole lot of different techniques with a bunch of sweat is required. Some of the most common methods for closing measurement gaps include re-survey, panels, device binding and dynamic 800 numbers.

Lastly, a key part of this whole phase is training the organization to think in terms of continuous improvement. That doesn’t happen magically and while all of the initiatives described here support that transformation, they aren’t, by themselves, enough. In my two posts on building analytics culture, I laid out a fairly straightforward vision of culture. The basic idea is that you build analytics culture my using data and analytics. Not by talking about how important data is or how people should behave. In the beginning was the deed.

Creating a constant cadence of analytics-based briefings and discussions forces the organization to think analytically. It forces analysts to understand the questions that are meaningful to the business. It forces decision-makers to reckon with data and lets them experience the power of being able to ask questions and get real answers. Just the imperative of having to say something interesting is good discipline for driving continuous improvement.

foundational transformation Step 2

That’s phase two of enterprise digital transformation. It’s all about baking continuous improvement into the organization and building on top of each element of the foundation the never ending process of getting better.

 

You might think that’s pretty much all there is to the analytics side of the digital transformation equation. Not so. In my next post, I’ll cover the next phase of analytics transformation – driving big analytics wins. So far, most of what I’ve covered is valid for any enterprise in any industry. But in the next phase, initiatives tend to be quite different depending on your industry and business model.

See you after the Hub!

Space 2.0

The New Frontier of Commercial Satellite Imagery for Business

One of my last speaking gigs of the spring season was, for me, both the least typical and one of the most interesting. Space 2.0 was a brief glimpse into a world that is both exotic and fascinating. It’s a gathering of high-tech, high-science companies driving commercialization of space.

Great stuff, but what the heck did they want with me?

Well, one of the many new frontiers in the space industry is the commercialization of geo-spatial data. For years now, the primary consumer of satellite data has been the government. But the uses for satellite imagery are hardly limited to intel and defense. For the array of Space startups and aggressive tech companies, intel and defense are relatively mature markets – slow moving and difficult to crack if you’re not an established player. You ever tried selling to the government? It’s not easy.

So the big opportunity is finding ways to open up the information potential in geo-spatial data and satellite imagery to the commercial marketplace. Now I may not know HyperSpectral from IR but I do see a lot of the challenges that companies face both provisioning and using big data. So I guess I was their doom-and-gloom guy – in my usual role of explaining why everything always turns out to be harder than we expect when it comes to using or selling big data.

For me, though, attending Space 2.0 was more about learning that educating. I’ve never had an opportunity to really delve into this kind of data and hearing (and seeing) some of what is available is fascinating.

Let’s start with what’s available (and keep in mind you’re not hearing an expert view here – just a fanboy with a day’s exposure). Most commercial capture is visual (other bands are available and used primarily for environmental and weather related research). Reliance on visual spectrum has implications that are probably second-nature to folks in the industry but take some thought if you’re outside it. Once speaker described their industry as “outside” and “daytime” focused. It’s also very weather dependent. Europe, with its abundant cloudiness, is much more challenging than the much of the U.S. (though I suppose Portland and Seattle must be no picnic).

Images are either panchromatic (black and white), multi-spectral (like the RGB we’re used to but with an IR band as well and sometimes additional bands) or hyperspectral (lots of narrow bands on the spectrum). Perhaps even more important than color, though, is resolution. As you’d probably expect, black and white images tend to have the highest resolution – down to something like a 30-40cm square. Color and multi-band images might be more in the meter range but the newest generation take the resolution down to the 40-50cm range in full color. That’s pretty fine grained.

How fine-grained? Well, with a top-down 40cm square per pixel it’s not terribly useful for things like people. But here’s an example that one of the speakers gave in how they are using the data. They pick selected restaurant locations (Chipotle was the example) and count cars in the parking lot during the day. They then compare this data to previous periods to create estimates of how the location is doing. They can also compare competitor locations (e.g. Panera) to see if the trends are brand specific or consistent.

Now, if you’re Chipotle, this data isn’t all that interesting. There are easy ways to measure your business than trying to count cars in satellite images. But if you’re a Fund Manager looking to buy or sell Chipotle stock in advance of earnings reports, this type of intelligence is extremely valuable. You have hard-data on how a restaurant or store is performing before everyone else. That’s the type of data that traders live for.

Of course, that’s not the only way to get that information. You may have heard about the recent FourSquare prediction targeted to exactly the same problem. Foursquare was able to predict Chipotle’s sales decline almost to the percentage point. As one of the day’s panelist’s remarked, there are always other options and the key to market success is being cheaper, faster, easier, and more accurate than alternative mechanisms.

You can see how using Foursquare data for this kind of problem might be better than commercial satellite. You don’t have weather limitations, the data is easier to process, it covers walk-in and auto traffic, and it covers a 24hr time band. But you can also see plenty of situations where satellite imagery might have advantages too. After all, it’s easily available, relatively inexpensive, has no sampling bias, has deep historical data and is global in reach.

So how easy is satellite data to use?

I think the answer is a big “it depends”. This is, first of all, big data. Those multi and hyper band images at hi-res are really, really big. And while the providers have made it quite easy to find what you want and get it, it didn’t seem to me that they had done much to solve the real big data analytics problem.

I’ve described what I think the real big data problem is before (you can check out this video if you want a big data primer). Big data analytics is hard because it requires finding patterns in the data and our traditional analytics tools aren’t good at that. This need for pattern recognition is true in my particular field (digital analytics), but it’s even more obviously true when it comes to big data applications like facial recognition, image processing, and text analytics.

On the plus side, unlike digital analytics, the need for image (and linguistic) processing is well understood and relatively well-developed. There are a lot of tools and libraries you can use to make the job easier. It’s also a space where deep-learning has been consistently successful so that libraries from companies like Microsoft and Google are available that provide high-quality deep-learning tools – often tailor made for processing image data – for free.

It’s still not easy. What’s more, the way you process these images is highly likely to be dependent on your business application. Counting cars is different than understanding crop growth which is different than understanding storm damage. My guess is that market providers of this data are going to have to develop very industry-specific solutions if they want to make the data reasonably usable.

That doesn’t necessarily mean that they’ll have to provide full on applications. The critical enabler is providing the ability to extract the business-specific patterns in the data – things like identifying cars. In effect, solving the hard part of the pattern recognition problem so that end-users can focus on solving the business interpretation problem.

Being at Space 2.0 reminded me a lot of going to a big data conference. There’s a lot of technologies (some of them amazingly cool) in search of killer business applications. In this industry, particularly, the companies are incredibly sophisticated technically. And it’s not that there aren’t real applications. Intelligence, environment and agriculture are mature and profitable markets with extensive use of commercial satellite imagery. The golden goose, though, is opening up new opportunities in other areas. Do those opportunities exist? I’m sure they do. For most of us, though, we aren’t thinking satellite imagery to solve our problems. And if we do think satellite, we’re likely intimidated by difficulty of solving the big data problem inherent in getting value from the imagery for almost any new business application.

That’s why, as I described it to the audience there, I suspect that progress with the use and adoption of commercial satellite imagery will seem quite fast to those of us on the outside – but agonizingly slow to the people in the industry.

Big Data Forecasting

Forecasting is a foundational activity in analytics and is a fundamental part of everyone’s personal mental calculus. At the simplest level, we live and work constantly using the most basic forecasting assumption – that everything will stay the same. And even though people will throw around aphorisms of the “one constant is change” sort, the assumption that things will stay largely the same is far more often true. The keyword in that sentence, though, is “largely”. Because if things mostly do stay the same, they almost never stay exactly the same. Hence the art and science of forecasting lies in figuring out what will change.

Slide 1 ForecastingBigData
Click here for the 15 minute Video Presentation on Forecasting & Big Data

There are two macro approaches to forecasting: trending and modelling. With trending, we forecast future measurements by projecting trends of past measurements. And because so many trends have significant variation and cyclical behaviors (seasonal, time-of-day, business, geological), trending techniques often incorporate smoothing.

Though trending can often create very reliable forecasts, particularly when smoothed to reduce variation and cycles, there’s one thing it doesn’t do well – it doesn’t handle significant changes to the system dynamics.

When things change, trends can be broken (or accelerated). When you have significant change (or the likelihood of significant change) in a system, then modelling is often a better and more reliable technique for forecasting. Modelling a system is designed to capture an understanding of the true system dynamics.

Suppose our sales have declined for the past 14 months. In a trend, the expectation will be that sales will decline in the 15 month. But if we decide to cut our prices or dramatically increase our marketing budget, that trend may not continue. A model could capture the impact of price or marketing on sales and potentially generate a much better prediction when one of the key system drivers is changed.

This weekend, I added a third video to my series on big data – discussion of the changes to forecasting methodology when using big data.

[I’ve been working this year to build a legitimate YouTube channel on digital analytics. I love doing the videos (webinar’s really since they are just slide-shows with a voice-over), but they are a lot of work. I think they add something that’s different from either a blog or a Powerpoint and I’m definitely hoping to keep knocking them out. So far, I have three video series’ going: one on measuring the digital world, one on digital transformation in the enterprise, and one on big data.]

The new video is a redux of a couple recent speaking gigs – one on big data and predictive analytics and one on big data and forecasting. The video focuses more on the forecasting side of things and it explains how big data concepts impact forecasting – particularly from a modelling perspective.

Like each of my big data videos, it begins with a discussion of what big data is. If you’ve watched (or watch) either of the first two videos in the series (Big Data Beyond the Hype or Big Data and SQL), you don’t need to watch me reprise my definition of big data in the first half of Big Data and Forecasting. Just skip the first eight minutes. If you haven’t, I’d actually encourage you to check out one of those videos first as they provide a deeper dive into the definition of big data and why getting the right definition matters.

In the second half of the video, I walk through how “real” big data impacts forecasting and predictive problems. The video lays out three common big data forecasting scenarios: integrating textual data into prediction and forecasting systems, building forecasts at the individual level and then aggregating those predictions, and pattern-matching IoT and similar types of data sources as a prelude to analysis.

Each of these is interesting in its own right, though I think only the middle case truly adds anything to the discipline of forecasting. Text and IoT type analytics are genuine big data problems that involve significant pattern-matching and that challenge traditional IT and statistical paradigms. But neither really generate new forecasting techniques.

However, building forecasts from individual patterns is a fairly fundamental change in the way forecasts get built. Instead of applying smoothing techniques for building models against aggregated data, big data approaches use individual patterns to generate a forecast for each record (customer/account/etc.). These forecasts can then be added up (or treated probabilistically) to generate macro-forecasts or forecasting ranges.

If you’ve got an interest in big data and forecasting problems, give it a listen. The full video is about 16 minutes split into two pretty equal halves (big data definition, big data forecasting).

The Agile Organization

I’ve been meandering through an extended series on digital transformation: why it’s hard, where things go wrong, and what you need to be able to do to be successful. In this post, I intend to summarize some of that thinking and describe how the large enterprise should organize itself to be good at digital.

Throughout this series, I’ve emphasized the importance of being able to make good decisions in the digital realm. That is, of course, the function of analytics and its my own special concerns when it comes to digital. But there are people who will point out  that decision-making is not the be all and end all of digital excellence. They might suggest that being able to execute is important too.

If you’re a football fan, it’s easy to see the dramatic difference between Peyton Manning – possibly the finest on-field decision-maker in the history of the game – with a good arm and without. It’s one thing to know where to throw the ball on any given play, quite another to be able to get it there accurately. If that wasn’t the case, it’s probably true that many of my readers would be making millions in the NFL!

On the other hand, this divide between decision-making and execution tends to break down if you extend your view to the entire organization. If the GM is doing the job properly, then the decision about which quarterbacks to draft or sign will appropriately balance their physical and decision-making skills. That’s part of what’s involved in good GM decisioning. Meanwhile, the coach has an identical responsibility on a day-to-day basis. A foot injury may limit Peyton to the point where his backup becomes a better option. Then it may heal and the pendulum swings back. The organization makes a series of decisions and if it can make all of those decisions well, then it’s hard to see how execution doesn’t follow along.

If, as an organization, I can make good decisions about the strategy for digital, the technology to run it on, the agencies to build it, the people to optimize it, the way to organize it, and the tactics to drive it, then everything is likely to be pretty good.

Unfortunately, it’s simply not the case that the analytics, organization and capabilities necessary to make good decisions across all these areas are remotely similar. To return to my football analogy, it’s clear that very few organizations are setup to make good decisions in every aspect of their operations. Some organizations excel at particular functions (like game-planning) but are very poor at drafting. Indeed, sometimes success in one-area breeds disaster in another. When a coach like Chip Kelly becomes very successful in his role, there is a tendency for the organization to expand that role so that the coach has increasing control over personnel. This almost always works badly in practice. Even knowing it will work badly doesn’t prevent the problem. Since the coach is so important, it may be that an organization will cede much control over personnel to a successful coach even when everyone (except the coach) believes it’s a bad idea.

If you don’t think similar situations arise constantly in corporate America, you aren’t paying attention.

In my posts in this series, I’ve mapped out the capabilities necessary to give decision-makers the information and capabilities they need to make good decisions about digital experiences. I haven’t touched on (and don’t really intend to touch on) broader themes like deciding who the right people to hire are or what kind of measurement, analysis or knowledge is necessary to make those sorts of meta-decisions.

There are two respects, however, in which I have tried to address at least some of these meta-concerns about execution. First, I’ve described why it is and how it comes to pass that most enterprises don’t use analytics to support strategic decision-making. This seems like a clear miss and a place where thoughtful implementation of good measurement, particularly voice-of-customer measurement of the type I’ve described, should yield high returns.

Second, I took a stab at describing how organizations can think about and work toward building an analytics culture. In these two posts, I argue that most attempts at culture-building approach the problem backwards. The most common culture-building activities in the enterprise are all about “talk”. We talk about diversity. We talk about ethics. We talk about being data-driven in our decision-making. I don’t think this talk adds up to much. I suggest that culture is formed far more through habit than talk; that if an organization wants to build an analytics culture, it needs to find ways to “do” analytics. The word may proceed the deed, but it is only through the force of the deed (good habits) that the word becomes character/culture. This may seem somewhat obvious – no, it is obvious – but people somehow manage to miss the obvious far too often. Those posts don’t just formulate the obvious, they also suggest a set of activities that are particularly efficacious in creating good enterprise habits of decision-making. If you care about enterprise culture and you haven’t already done so, give them a read.

For some folks, however, all these analytics actions miss the key questions. They don’t want to know what the organization should do. They want to know how the organization should work. Who owns digital? Who owns analytics? What lives in a central organization? What lives in a business unit? Is digital a capability or a department?

In the context of the small company, most of these questions aren’t terribly important. In the large enterprise, they mean a lot. But acknowledging that they mean a lot isn’t to suggest that I can answer them – or at least most of them.

I’m skeptical that there is an answer for most of these questions. At least in the abstract, I doubt there is one right organization for digital or one right degree of centralization. I’ve had many conversations with wise folks who recognize that their organizations seem to be in constant motion – swinging like an enormous pendulum between extremes of centralization followed by extremes of decentralization.

Even this peripatetic motion – which can look so irrational from the inside – may make sense. If we assume that centralization and decentralization have distinct advantages, then not only might it be true that changing circumstances might drive a change in the optimal configuration, but it might even be true that swinging the organization from one pole to the other might help capture the benefits of each.

That seems unlikely, but you never know. There is sometimes more logic in the seemingly irrational movements of the crowd than we might first imagine.

Most questions about digital organization are deeply historical. They depend on what type of company you are, in what of market, with what culture and what strategic imperatives. All of which is, of course, Management 101. Obvious stuff that hardly needs to be stated.

However, there are some aspects of digital about which I am willing to be more directive. First, that some balance between centralization and decentralization is essential in analytics. The imperative for centralization is driven by these factors: the need for comparative metrics of success around digital, the need for consistent data collection, the imperatives of the latest generation of highly-complex IT systems, and the need/desire to address customers across the full spectrum of their engagement with the enterprise. Of these, the first and the last are primary. If you don’t need those two, then you may not care about consistent data collection or centralized data systems (this last is debatable).

On the other hand, there are powerful reasons for decentralization of which the biggest is simply that analytics is best done as close to the decision-making as possible. Before the advent of Hadoop, I would have suggested that the vast majority of analytics resources in the digital space be decentralized. Hadoop makes that much harder. The skills are much rarer, the demands for control and governance much higher, and the need for cross-domain expertise much greater in this new world.

That will change. As the open-source analytics stack matures and the market over-rewards skilled practitioners – drawing in more folks, it will become much easier to decentralize again. This isn’t the first time we’ve been down the IT path that goes from centralization to gradual diffusion as technologies become cheaper, easier, and better supported.

At an even more fundamental level than the question of centralization lives the location and nature of digital. Is digital treated as a thing? Is it part of Marketing? Or Operations? Or does each thing have a digital component?

I know I should have more of an opinion about this, but I’m afraid that the right answers seem to me, once again, to be local and historical. In a digital pure-play, to even speak of digital as a thing seems absurd. It’s the core of the company. In a gas company, on the other hand, digital might best be viewed as a customer service channel. In a manufacturer, digital might be a sub-function of brand marketing or, depending on the nature of the digital investment and its importance to the company, a unit unto-itself.

Obviously, one of the huge disadvantages to thinking of digital as a unit unto-itself is how it can then interact correctly with the non-digital functions that share the same purpose. If you have digital customer servicing and non-digital customer servicing, does it really make sense to have one in a digital department and the other as a customer-service department?

There is a case, however, for incubating digital capabilities within a small compact, standalone entity that can protect and nourish the digital investment with a distinct culture and resourcing model. I get that. Ultimately, though, it seems to me that unless digital OWNS an entire function, separating that function across digital and non-digital lines is arbitrary and likely to be ineffective in an omni-channel world.

But here’s the flip side. If you have a single digital property and it shares marketing and customer support functions, how do you allocate real-estate and who gets to determine key things like site structure? I’ve seen organizations where everything but the homepage is owned by somebody and the home page is like Oliver Twist. “Home page for sale, does anybody want one?”

That’s not optimal.

So the more overlap there needs to be between the functions and your digital properties, the more incentive you have to build a purely digital organization.

No matter what structure you pick, there are some trade-offs you’re going to have to live with. That’s part of why there is no magic answer to the right organization.

But far more important than the precise balance you strike around centralization or even where you put digital is the way you organize the core capabilities that belong to digital. Here, the vast majority of enterprises organize along the same general lines. Digital comprises some rough set of capabilities including:

  • IT
  • Creative
  • Marketing
  • Customer
  • UX
  • Analytics
  • Testing
  • VoC

In almost every company I work with, each of these capabilities is instantiated as a separate team. In most organizations, the IT folks are in a completely different reporting structure all the way up. There is no unification till you hit the C-Suite. Often, Marketing and Creative are unified. In some organizations, all of the research functions are unified (VoC, analytics) – sometimes under Customer, sometimes not. UX and Testing can wind up almost anywhere. They typically live under the Marketing department, but they can also live under a Research or Customer function.

None of this, to me, makes any sense.

To do digital well requires a deep integration of these capabilities. What’s more, it requires that these teams work together on a consistent basis. That’s not the way it’s mostly done.

Almost every enterprise I see not only siloes these capabilities, but puts in place budgetary processes that fund each digital asset as a one-time investment and which requires pass-offs between teams.

That’s probably not entirely clear so let me give some concrete examples.

You want to launch a new website. You hire an agency to design the Website. Then your internal IT team builds it. Now the agency goes away. The folks who designed the website no longer have anything to do with it. What’s more, the folks who built it get rotated onto the next project. Sometimes, that’s all that happens. The website just sits there – unimproved. Sometimes the measurement team will now pick it up. Keep in mind that the measurement team almost never had anything to do with the design of the site in the first place. They are just there to report on it. Still, they measure it and if they find some problem, who do they give it to?

Well, maybe they pass it on to the UX team or the testing team. Those teams, neither of which have ever worked with the website or had anything to do with its design are now responsible for implementing changes on it. And, of course, they will be working with developers who had nothing to do with building it.

Meanwhile, on an entirely separate track, the customer team may be designing a broader experience that involves that website. They enlist the VoC team to survey the site’s users and find out what they don’t like about it. Neither team (of course) had anything to do with designing or building the site.

If they come to some conclusion about what they want the site to do, they work with another(!) team of developers to implement their changes. That these changes may be at cross-purposes to the UX team’s changes or the original design intent is neither here nor there.

Does any of this make sense?

If you take continuous improvement to heart (and you should because it is the key to digital excellence), you need to realize that almost everything about the way your digital organization functions is wrong. You budget wrong and you organize wrong.

[Check out my relatively short (20 min) video on digital transformation and analytics organization – it’s the perfect medium for distributing this message through your enterprise!]

Here’s my simple rule about building digital assets. If it’s worth doing, it’s worth improving. Nothing you build will ever be right the first time. Accept that. Embrace it. That means you budget digital teams to build AND improve something. Those teams don’t go away. They don’t rotate. And they include ALL of the capabilities you need to successfully deliver digital experiences. Your developers don’t rotate off, your designers don’t go away, your VoC folks aren’t living in a parallel universe.

When you do things this way, you embody a commitment to continuous improvement deeply into your core organizational processes. It almost forces you to do it right. All those folks in IT and creative will demand analytics and tests to run or they won’t have anything to do.

That’s a good thing.

This type of vertical integration of digital capabilities is far, far more important than the balance around centralization or even the home for digital. Yet it gets far less attention in most enterprise strategic discussions.

The existence or lack of this vertical integration is the single most important factor in driving analytics into digital. Do it right, and you’ll do it well. Do what everyone else does and…well…it won’t be so good.