Here comes the data deluge

Whether they realize it or not, social media is already the most important technology channel for enterprises to engage their customers. The usage and adoption rates on social media may currently lag alternatives such as call centers or traditional media, but look at the trend lines. A recent survey by IBM shows social media moving from 16% to 57% usage as the primary customer interaction tool over the next three to five years, while call center usage drops from 40% to 31%, and traditional media goes from 39% to a paltry 15%.

While these numbers are interesting, they trigger a broader and potentially far more complex debate. In spite of all the noise and attention social media receives, most enterprises do not have even a remotely clear idea of how to manage social imperatives within their existing workflows. This integration will be the key driver for long-term success, and if it follows previous technology adoption patterns, a small % of companies will get it and thrive, a bigger percentage will fail, and everyone else will muddle through. Every business out there is dependent on a workflow, whether it’s processing a mortgage, developing software, or baking cookies. Anything that streamlines or improves the efficiency of the workflow is adopted; anything that slows it down in the slightest will at best get perfunctory attention, and then slowly die of neglect. This is further compounded by the fact that social media is amorphous, reactive, and often not even remotely logical—which is the exact opposite of how most enterprises would prefer to run their business.

Now here’s the scary part. The amount of data being generated by consumers, as vast as it is at the moment, is merely the first trickle that will turn into a massive tsunami as machine to machine (M2M) data starts to become more integrated with the consumer data wave. While there are billions of mobile devices and users, there are over a trillion wireless sensors, all gathering data continuously, and the boundary between these two sets are becoming increasingly blurred. If you think we have a challenge integrating social data into the enterprise workflow now, just wait until the consumer and M2M spaces align. As disruptive as this is going to be, it also means an even bigger opportunity for the right technology at the right time.

A better approach to New Year’s resolutions

As another year rolls around, I see a lot of people making New Year’s resolutions; to lose weight, to stop smoking, to do this or that, etc. And let’s be honest, most of these resolutions rarely make it past the end of January. I have developed a very effective alternative framework for what are essentially annual objectives, and it works really well. This includes three areas:

Become an expert. Most of us are already experts at something (hopefully our jobs), but I mean become an expert at something you don’t know anything about, that doesn’t necessarily focus on work. For example, last year I decided to become an expert on Russian history. Every night before I went to sleep I would read about Russian history for 30 minutes. I may not have PhD level knowledge, but last year I read about 30 books on Russian history, so compared to your average bear I am now very knowledgeable. It wasn’t a big time sink, and over the course of a year I picked up something new and interesting.

Learn a new skill. This could be learning to play an instrument, learn to speak a new language (e.g. listen to language podcasts rather than music on your way to work, it’s a great way to use otherwise down time), or focus on something that makes your brain work in new ways. Last year I focused on learning jazz improvisation on the piano, and now I can jam with my son. Totally cool.

Learn a stupid skill. It can be very easy to forget it’s important to have fun. There are lots of relatively pointless skills you can pick up that are entertaining and can be used to liven up a party, and depending on the skill, even liven up a business presentation. Last year I learned to juggle three balls (and actually used it to illustrate a point while training sales reps on a new product—it totally worked), the year before I learned how to moonwalk. YouTube is a great resource for tutorials on stupid skills.

I’ve suggested this framework to my friends over the years, and the feedback I get is consistently positive. The beauty of this method is it doesn’t really cut that much into your otherwise tight schedule (everyone has downtime—why spend time vegging in front of the TV when you can become an expert or learn a new skill?), and every year you can layer on more skills and knowledge.

I hope this post stimulates some new thinking, and a very happy new year to those of you who’ve read this.

COPPA and the Privatistas

The FTC has release another wave of regulations that aim to stymie online advertisers’ ability to create a compelling experience for children. By expanding COPPA (Children’s Online Privacy Protection Act) to include limitations driven by persistent cookies, mobile device identifiers, IP addresses and geolocation data, the FTC has added a thick layer of ambiguity to an already poorly defined regulatory framework.

The first problem is that the regulatory parameters they’ve chosen such as cookies, device identifiers, etc. don’t identify a person, they identify a device. You can claim the device is tied to the person; sometimes yes, but more often no. Simplest example? We have multiple iPhones, iPad, Macs and PCs at our home, and everyone uses whichever one happens to be closest. Any device identifier or cookie data is going to reflect device use across a very diverse set of demographics (me, my wife, and my son), so claiming that device specific technology will tell the network exactly what I’m up to is an inherently flawed approach.

The second problem, which the FTC and the Privatistas over at the Center for Digital Democracy still don’t seem to understand, is that Behavioral Targeting is a science done at an aggregated level. Most of this technology is not about going after a very specific person (child or otherwise), its about targeting cohorts of common interest in order to minimize spam and create a compelling experience.

The notion of greedy advertisers drooling over kids as they traverse a network is the core driving element of what the CDD harps on, and is completely off point. Aggregate data in terms of how apps are being used is critical to developing a product roadmap that maps to market requirements, and anonymized cookies are designed to do exactly that.

The third issue is defining the scope of what is gathered and what is done with it. The idea of capturing my child’s name and location does make me feel incredibly uncomfortable, but that is an extreme of what this technology can deliver, and it is up to industry groups such as the IAB to self-police to the point that this sort of thing doesn’t happen. Or they can continue to play the defensive role (which they seem to be good at) and watch the scope of their capabilities continue to shrink.

The future of enterprise mobility

Predicting what is likely to happen in the coming year is always a fun exercise (like watching the guy on the high wire without a net is fun), but given the number of mobile initiatives and potential confluence points that are on the short term horizon, I think we would be well served to polish the crystal ball and make some prognostications. I see four main areas, which tend to overlap:

First: Expect a fundamental, permanent, and transformative shift in how end users interact with corporate information resources. There are a number of sub-elements to this, which revolve around changing form factors, cooler user interfaces, and better information. The form factor argument is obvious (the rise of the tablet) and is prediction #2. The cooler user interface is driven by a touch-screen paradigm; we’re still in the gee-whiz stage of appreciation of what Apple (and now others) have brought to the table with the swipe interface. At the risk of dating myself, remember when the mouse was introduced? Same reaction back then. We’ll get used to the swipe interface, and then Apple will do something even cooler, because that’s what they do. The point is that the way you interact with information defines the quality and context of the experience. I’m not suggesting that enterprise software will have an “Angry Birds” feel to it (be great if it did though), but it will be a significantly different experience that what it has been, and it will be a noticeable improvement, driven by both swipe interfaces and the fact that it’s now delivered as a smaller, denser mobile payload, which by definition drives pithiness, which drive efficiency, etc.

Second: Tablets will NOT kill laptops, laptops will have to schooch over and make room on the bench. There are all sorts of things I can do with a laptop that I can’t do with a tablet, and vice versa. Part of the reason tablets have taken off is that they fill a niche between smart phones and laptops, but filling a niche is not the same as taking out a lateral device. Laptops will continue to be around for heavy lifting, but if all you’re doing is consumption, then the tablet is the right choice. It is not the end of the laptop, but it is the end of their dominance.

Third: Mobilization expands beyond devices to processes and workgroups. People often assume that because they have a smart phone, they’ve embraced mobility—they haven’t. Embracing a cool device is not the same thing as embracing true mobility, and we need to be more precise about the difference. Mobility in the workplace is about taking existing workflows and creating a different kind of experience, not just for the individual, but for the group the individual works within. Anytime a new technology is introduced to a process, it changes that process, and 2011 will be the year that mobility gets serious traction within the framework of enterprise workflows. Part of the reason this is very likely to happen is that the enterprise mobility solutions from Sybase and SAP are, in fact, getting serious traction, and a year from now the roster of companies that are leading this transformation will include a significant number of high-profile, visionary enterprises..

Fourth: Expansion of cloud services for enterprise applications. Cloud-based services are as much of a no-brainer as you can find. The gating factors up to this point have been security (which is now airtight), bandwidth (which is improving by leaps and bounds), and the need for form factors that allow better interpretation of complex information, combined with the adoption of HTML 5 and a persistent rich media experience, which is perfect for cloud services. The non-gating factors are driven by pricing models (CAPEX vs. OPEX) and have always been compelling, and the elements needed for broad adoption are now in place.

Bottom line? We have alignment and we have momentum. The needs mobility addresses are widespread, touching nearly every process and vertical market, and are fundamental to the way any business operates. 2011 will be remembered as the year of the true transformation of the enterprise.

Why Mobilize?

One of the issues that came up after the acquisition of Sybase by SAP focused on our overall approach to mobility. We (Sybase) were approaching the market under the assumption that a mobilized enterprise was a given—that is, everyone understood the imperative, and it was really just a matter of execution. We were quickly bought up short by a number of our cohorts at SAP who said “Why would you make that assumption? The world at large doesn’t have the same perspective on mobility that you guys have. You need to be asking the question ‘Why mobilize?’ Rather than ‘How do you mobilize?’”

This was, of course, an excellent point. I’ve been involved with mobility since the technology first became commercially available, and have therefore spent my entire career surrounded by experts. This naturally creates a reality distortion field that can often be hard to recognize. So therefore, asking a question that was so obvious I missed it, why indeed should an enterprise mobilize?
There are two questions that need to be addressed at the beginning of any conversation about enterprise mobility. First, what does your company do for a living? That is, what is the value your company delivers to its customers in the form of goods or services, and how can a mobility solution increase that value? Second, what do your employees do while sitting in front of a computer at work, and how can mobilizing them and their functions make them more effective at what they do? Approaching the issue of mobility from this perspective shifts the driving rationale towards the value associated with transforming the enterprise, and creates a much more strategic and holistic view of mobility.

This approach, however, triggers the next question, what does ‘transforming the enterprise’ mean? The best example I can think of is looking back to the last time enterprises went through a pervasive, fundamental transformation. This started back in the mid 90s, when a broad range of businesses transformed and subsequently extended their delivery model from brick and mortar to on-line. Think about the relative efficiencies of an on-line model compared to a brick and mortar model (one-click and a gift wrapped package shows up two days later, vs. the staggering hassle of “traditional” holiday shopping). That level of efficiency-on-steroids is happening again as companies begin to evolve towards a mobile paradigm, and the relative scope of opportunity is at least as large as shifting the brick mortar world on-line, and probably much greater.

So back to the earlier question, why mobilize? If done correctly, everything will move faster; your employees are more responsive, your transaction rate increases, your customers are happier, you gain a non-trivial competitive edge over the slackers who don’t mobilize, and all of this accrues to both top and bottom line revenues. And this is just if your enterprise mobilizes. What happens when your entire supply chain and distribution channels mobilize?

Are you still wondering why to mobilize?

Sales AND Marketing?

One of the things that has always puzzled me are position descriptions for a “Sales and Marketing” VP. The fact that sales and marketing are often mentioned in the same breath demonstrates a lack of understanding of the fundamental difference between sales and marketing, particularly in smaller firms. Having worked with both domains extensively for years, I continue to be surprised by the extent of the number of people at a senior level who don’t understand the difference.

There are lots of analogies at play here, the best I’ve come up with (so far) is the race car model. If Sales is the guy driving the Formula 1, Marketing designed the engine, built the car, paved the road, went out and got sponsors, provided detailed performance specifications on the other cars and drivers, and provides pit crew support (including spare parts, personnel, and fuel).

Designed the engine. In most companies Product Management resides within Marketing. This is the function that essentially tells engineering what to build through process-centric deliverables such as Product Requirements Documents (PRDs), and Market Requirements Documents (MRDs). The MRD/PRD is based on market requirements driven by extensive research on customer needs, competitive offsets, channel requirements, etc. They are generally long, very detailed, and updated continuously in parallel with engineering development efforts.

Build the car. What defines the user experience? How easy to use and intuitive is the product or service? What does packaging and pricing look like? Product Marketing owns this function, and again, serves as a strong bridge between end-users and engineering. The work is (like most marketing efforts) very detailed and surprisingly technical.

Pave the road. Arguably the most complex task. Raising awareness of your product/service requires reaching out to potential customers, channel partners, analysts, journalists, bloggers, as well as competitors (whom you’ll reach whether you want to or not). This is where the analytic aspect of marketing kicks in; Search Engine Optimization, Search Engine Marketing, optimizing landing pages, creation and tracking of microsites, multi-level, multi-touch rich media outreach campaigns, portal placements, using blogs as media advisories, the list goes on for quite a while, and each aspect has a detailed metrics component that needs to tie into profitability analysis both for the individual product and the overall product portfolio. This particular aspect of marketing has become incredibly more complex as the Internet grows into a major distribution and information channel for most companies.

Get sponsors. One of the most valuable assets in a marketing portfolio is a happy customer who is willing to serve as a reference. This is one area where sales gets involved (since they own the customer), but Marketing spends a lot of time cultivating and grooming the customer champion for events that include analyst and media interviews, participation in webinars and public panels, guest blogging, etc.

Competitive analysis. Who is sales going to be going up against, and how are they likely to be attacked? What is the best offense to counter their defense? Detailed, continuous due diligence on competitors and their ecosystem is the province of marketing, and is one of the most useful tools supplied to sales reps before they walk in to speak with a prospect.

Pit crew. Marketing provide sales with a steady stream of qualified leads, provides all support materials they need (both on-line and off-line), schedules participation in industry events–as well as pre-show promotion, post-show follow-up, plus managing the show itself and all the leads that are generated.

All of this is very different from a Sales skill set. Sales has always been more about relationship management (herding the rabid cats), which has an entirely different set of requirements. The main difference? Sales is difficult, Marketing is complicated. I would also point out that Sales is by far the most critical role in a company. No sales, no revenue. No revenue, no company. It doesn’t matter how brilliant your engineering is, or how clever your marketing is, if people aren’t buying, none of that matters. However, sales cannot succeed without marketing; finding someone with the skill set to manage both functions is nearly impossible, because the skills required are so very different. On the other hand, finding someone who understands both functions and is smart enough to hire genuine experts at each should (in theory) be more straightforward, and can provide a genuine framework for success.

Geeks vs. Wonks

While Business Intelligence, Predictive Analytics, and other forms of metrics-driven insights into corporate behavior have burrowed into select areas of Fortune 500 companies, the use of this technology in broader markets is in its infancy at best. There is potentially a huge opportunity for companies to tap into what is essentially a greenfield opportunity; the vast majority of companies in the US (and globally) are small businesses, with the same problems and challenges as the multi-nationals on a much smaller scale. The primary challenge for small businesses is a lack of sophisticated tools to analyze their business processes, and the hidden, or secondary problem with this is that even if a sophisticated solution was available at a reasonable price, most business owners wouldn’t have a clue as to how to get started.

The day to day processes that define how a business operates are generally not technical in nature, but they are very transactional. Most people tend to deal with the same types of situations on a regular basis, and as such tend to become “experts” in specific aspects of their part of the transaction flow. It’s this type of granularity that is begging for a business intelligence overlay; connecting the expert analyst capabilities with expert process capabilities is what will move this forward, with one caveat. The analyst has to adapt to the process expert, and for two reasons; analysis has to fit the business model, and most important, the process expert is the customer, who is well within their rights to expect to have their needs met.

Invisible Intelligence

One of the core gating factors in deploying a Business Intelligence application is its overall effect on production workflow for the company in question. Over the years I’ve worked with companies who went through a Six Sigma process; they were all big (Fortune 1000) companies, with lots of infrastructure and process methodologies already in place, as well as a surplus of people who seemed to have the bandwidth to take on an additional large, complex project. Even within the context of these types of companies, implementing a structured, rigorous process for quality improvement was disruptive (“gee Dan, I know you have a sales meeting in Europe next week, but we really need you here for the Six Sigma meeting”). It’s possible to get away with this sort of thing at a large company (primarily due to excess bandwidth), but it becomes a much greater challenge when you’re dealing with a small or medium sized business where every single person is critical to keeping the machine moving forward.

In order for BI to have the desired effect on the quality of an organization’s information process flow, the deployment of the application has to integrate into the existing workflow without being disruptive. I’m not suggesting that business intelligence should be applied to what is potentially a faulty process, what I’m saying it that these companies can’t be turned on a dime. An increase in focus on quality does not just affect internal processes, it also affects customers, channel partners, customer support, etc (implementing any type of change across a company always slows processes down before speeding them up, and customers may not understand and appreciate the slow down). In an ideal world, the application of BI to an aggregate process flow would be nearly invisible; most interactions within and between systems are transactional anyway, so an incremental transactional improvement would be less disruptive, and because the effect on the workflow (and those responsible) is incremental on a transactional level, it is less likely to be disruptive, and more likely to begin to effect the desired change. Which is to say, the development of process rigor should be an integral and evolutionary part of a BI introduction, rather than a precedent.

Making non-linear linear

There has been a fair amount of recent coverage on the shortcomings of business intelligence as the concept starts to move out of the purview of Fortune 1000 companies and into more mainstream usage. One area referenced consistently as an area needing work is the BI community’s steady focus on structured data at the expense of unstructured data. Business intelligence as an application suite is still relatively nascent in its deployment; while it is widely used by large companies (although not consistently or comprehensively), the vast majority of businesses are not Fortune 1000, and wouldn’t recognize business intelligence if it hit them in the head. The opportunity here is that with no prior frame of reference, there is a great opening for BI vendors to step in with a solution that is ideally geared towards the requirements of the SMB market. Two core drivers here are 1) use of unstructured data as a BI feed, and 2) dumbing down the application as much as possible so mere mortals can feel comfortable using the product on a transactional level.

Most data (over 80%) in most companies is unstructured. E-mails, narrative reports, legal documents, any product centric information (data sheets, functional specifications, etc.) is unstructured, and it’s where the majority of mission critical information exists. There is a huge inventory of information just sitting there, beyond the reach of BI or analytics engines because most BI apps are designed to think in a linear fashion, and unstructured data is by definition non-linear. You can add metatags or some form of XML structure to your documentation (which is finally starting to happen), but this also pre-supposes some sort of referential taxonomy to organize the information once it’s been made ready to be pulled into a BI application. The people who are most likely to be transactional users of this type of technology are not trained to think in terms of a taxonomy, this is generally a luxury that only large companies can afford. So that is one area that would need to be addressed before there is broader market acceptance of sophisticated business intelligence applications.

This leads to the second requirement; make this thing easy to use. If you’re like most of us, your day-to-day work keeps you running at full tilt. Stopping what you’re doing to run up a long steep learning curve is probably the last thing you want to do, yet that is what most BI vendors expect of their end-users. The more you can shield your end-users from the innards of the technology and provide them with a simple, graphical, drag and drop interface, the more likely they are to adopt a system that minimizes a trip outside their comfort zone. This is another, potentially fatal sin of BI developers: “we’ve developed a highly sophisticated analysis product, let us show you”, when what they should be saying is “what type of information do you need to do your job better, and how can we make it as simple as possible?”

In like a lion, out like a lamb

Looks like the first serious foray into ISP-based behavioral targeting is finally sputtering to a close. Spooked by misinformed and often hostile congressional attention, most of NebuAd’s customers have dumped the company and beat a hasty retreat, and today their CEO surfaced working somewhere else. What is the take-away in all this? To use a popular term, there appears to have been a distinct lack of “vetting”; introducing this kind of disruptive/invasive technology requires a broad base of support, it’s not just about commercial validation, but about buy-in from influencers prior to pushing the product out the door. Careful legal review, not just from the “technically correct” point of view, but from the “how to socialize this with people who can shut you down on a whim” perspective would have probably been a good idea. Privacy advocates notwithstanding, I think most people would agree that targeted ads are a good idea (or do you prefer spam?), but like a lot of early stage start-ups, there was way too much focus on the technology, not nearly enough focus on the benefits, which would have probably been significant. On the other hand, those of us with an interest in this space now know what to avoid, so again, a big thanks to NebuAd for setting off the traps.