By 2020, every company that exists today will have become a digital predator or digital prey. This isn't news to you if you follow my research or blog posts. In fact I've been saying that since 2014. But why is it that some companies seem to understand digital business more than others? Is their a difference in the leadership in digital predators compared to digital dinosaurs?
These are the questions I dig into in my latest research "The 2016 Guide To Digital Predators, Transformers, and Dinosaurs." To get at the answers, we analyzed the results from the digital business survey to tease apart companies that are already digital, those that are transforming to become more digital, and those that are remaining less digital. Examining the perspectives of business executives in each group, we reveal how the digital business DNA differes between predators, transformers and dinosaurs.
Executives At Digital Predators Are Really, Really Customer Obsessed
While all companies profess to put customers first, it’s clear from the data that executives at digital Predators care more passionately about the customer across multiple dimensions: In every customer metric we measured, these executives rated the importance of the customer higher than peers in transformers and dinosaurs – in short, they are not just customer obsessed, they are really, really customer obsessed. Nowhere is this clearer than in the perception of how to apply digital thinking to the business:
They say a picture is worth a 1000 words .... so our graphics designers and my wonderful research associate, Rachael Klehm, created this infographic to highlight a few of the data points from the Digital Transformation Playbook and the Forrester / Odgers Berndtson Digital Business research study.
The challenge most companies still have is their CEO either doesn't understand how fast their world is about to change or simply cannot allocate sufficient investment to something that will not bear fruit (at least from an investor's perspective) for a year of two. Unfortunately, when it comes to digital transformation, a short-term share-price focus is likely to lead to failure within a few years.
True transformation (vs bolt-on) is a fundamental strategic shift for most companies.
On the plus side, that's no doubt one of the reasons why so many clients ask me to present to their executive teams!
Over the past several years, Forrester's research has written extensively about the age of the customer. Forrester believes that only the enterprises that are obsessed with winning, serving, and retaining customers will thrive in this highly competitive, customer-centric economy. But in order to get a full view of customer behavior, sentiment, emotion, and intentions, Information Management professionals must help enterprises leverage all the data at their disposal, not just structured, but also unstructured. Alas, that's still an elusive goal, as most enterprises leverage only 40% of structured data and 31% of unstructured data for business and customer insights and decision-making.
So what do you need to do to start enriching your customer insights with unstructured data ? First, get your yext analysis terminology straight. For Information Management pros, the process of text mining and text analytics should not be a black box, where unstructured text goes in and structured information comes out. But today, there is a lot of market confusion on the terminology and process of text analytics. The market, both vendors and users, often uses the terms text mining and text analytics interchangeably; Forrester makes a distinction and recommends that Information Management pros working on text mining/text analytics initiatives adopt the following terminology:
At the TechCrunch Disrupt event in NY today, Dag Kittlaus delivered the first public demonstration of Viv, his team’s follow-up to the popular Siri service. There’s been a lot of press in advance of the demo and frequent chatter around eBusiness and bots and what Viv means to them. My focus is on Application Development and Delivery (AD&D) professionals, so I thought I’d update you on what all of this means to them.
Viv is attempting to create what they’re calling a Global AI. While I’m not an AI expert, as I understand it AI entities are typically ‘trained’ using either algorithms or by dumping a bunch of data into it and helping it sort through it all. The self-training algorithms are where AI research had stalled until recently, but machine learning and other methods are revitalizing it. The Viv team, however, is taking a different approach. They’ve built the requisite language processing capabilities (through a partnership with Nuance) and coupled that with a code generator (what they call dynamic program generation) that delivers the needed results. What happens in between? Well, that’s the special sauce that will be very interesting for developers.
The knowledge Viv uses to deliver on voice requests is directly driven by direct input from developers. Well, that’s not necessarily true, but you’ll see what I mean in a minute.
What are we going to call cars that drive themselves? The term "automobile" would be perfect, but that's already taken.
"Nemo" means nobody in Latin -- a car driven by no one would be called a "nemobile." And you could call them "nemos" for short.
By the way, that's nemo with a long "e" -- pronounced like the fictitious fish or the commander of Jules Verne's submarine the Nautilus.
Short, distinctive, meaningful, good nickname. Much better than "driverless car," or "self-driving car," or the inevitable flat acronym "SDC."
By the way, if you are doubting that nemos will be on the scene anytime soon, consider this, as noted by Erik Brynjolfsson and Andrew McAfee in their excellent book Race Against The Machine: In 2004, DARPA offered a prize for any autonomous vehicle that could navigate a 150-mile course in the Mojave Desert. The best performing nemo only travelled eight miles, and it took two hours to do that. But by 2010, Google's self-driving cars had logged 1,000 miles on US highways.
Now fast forward to the summer of 2015 -- Google reported that its cars had logged one million miles of autonomous driving. And in 2016, the Tesla S and X now offer easy-to-use and dependable autopilot -- a very credible early form of self-driving.
Since Mobile World Congress, where the reality on the show floor was often either virtual or augmented, I’ve been thinking quite a bit about the practical uses of AR and VR – particularly in government and a smart city context. It’s not just all fun and games, is it?
The example of changing a roller coaster experience with new settings delivered via VR glasses is really cool. Yes, you can imagine repeating the ride to experience catapulting through medieval battle, flying through a tropical jungle, or bobsledding down alpine slopes. But the practical side of us – or at least me – wants to know what else there is. And, fortunately, I have a colleague who has already been thinking of these things.
A few months ago, I had the pleasure of collaborating with JP Gownder on a presentation for Forrester clients in Geneva. I presented on the ways to derive value from data and opportunities to leverage new insights service providers – clearly something top of mind for many of our clients. But alas JP’s presentation was much cooler, providing examples of how to derive real value from new technologies including AR and VR. Since then I’ve being thinking about how the two are related. And, in fact, they are.
Smart watches are not a must-have device – yet. The novelty of the device – combined with early adopters eager to have the next great thing – has carried smart watches from an obscure idea to a well-known device, but neither critical mass nor mass market adoption. So what’s missing?
Smart watches or similar wearables will hit critical mass (20%) and then mass market adoption (> 50%) only once consumers adopt these five applications:
1. Notifications. Among consumers surveyed by Forrester, 40% are tired of pulling their phones out of their pockets or purses. Moreover, according to a study conducted by Mary Meeker from Kleiner Perkins, more than 60-70% of consumers’ mobile moments are simply a quick glance at their devices to get information they need to make a decision or take action. Notifications could range from a sports score to a reminder to pay a bill. Smartphones and apps are overkill for these interactions or mobile moments.
2. Payments. Mobile payment solutions from companies like Apple, Google, and Samsung, among others, are game-changing. The combination of near-field communication (NFC) and payments drove adoption of the current generation of smartphone upgrades. Mobile payments remove friction from the payment process both online and in-person. For example, I use my Apple Wallet so often that it took me six weeks to realize that my ATM card had expired.
CIO pushback is part of a typical growing pain of all business intelligence (BI) startups. It means your land and expand strategy is working. Once you start expanding beyond a single department CIOs will notice. As a general rule, the earlier the CIO is brought on board, the better. CIOs who feel left out are likely to raise more objections than those who are involved in the early stages. A number of BI vendors that started out with a strategy of purposely avoiding the CIO found over time that they had to change their strategies - ultimately, there’s no way round the CIO. Forrester has also noticed that the more a vendor gets the reputation of “going round” the CIO, the greater the resistance is from CIOs once they do get involved.
There is of course also the situation where the business side doesn’t want the CIO involved, sometimes for very good reason. That notwithstanding, if there’s a dependency on the CIO when it comes to sign-off, Forrester would strongly recommend encouraging the business to bring him/her to the table.
The two key aspects to bear in mind in this context are:
CIOs look for transparency. Have architecture diagrams to hand out, be prepared to explain your solution in as much technical detail as required, and have answers ready regarding the enterprise IT capabilities listed below.
I’ve been a part of several development organizations, and, for several of those teams, security was an afterthought to the development process. We’d secure databases and even implement field level encryption but we rarely had to consider many attack vectors as we were building internal apps for enterprises and the risks were there, but not as great.
Fast forward to the Mobile First world we live in and that lazy attitude is no longer acceptable. S&R teams have real concerns and actively work to protect their computing environments – both internal-facing and external-facing. Development teams work the other side of that and implement secure code as part of their daily activities (right?). With an appropriate level of trust between the two organizations, many use code scanning utilities to verify delivered code and hunt for vulnerabilities. There are many sources of vulnerabilities; it could come from code written by the company’s developers, code pasted in from Stack Overflow or even added through some third-party or open source library. In my experience, static code scanning tools are effective and can catch a lot of potential vulnerabilities but, from a developer behavior standpoint, what the ultimately do is simply teach developers how to get their code to pass the scans, not actually deliver more secure code.
Years ago, I worked at a large customer service vendor. Our CEO had tasked us to "eat our own dog food" - that is implement our own solutions for our customer service operations which comprised of 40 or so tier 1 and 2 customer service agents. With these marching orders, I put a group of consultants and business analysts together to get this done. And after several months, the project stalled; got restarted; stalled again; then finally died. We limped on with our old systems in place for many more years.
Why did this project fail? It was because of a mismatch between the complexities of the solution that we were trying to implement, and the company's business needs. The customer service company that I worked for made enterprise software solutions, suitable for large organizations, which was typically implemented in call centers of many hundreds, if not thousands of call center agents. These solutions offered robust case management, with very customizable workflows, queuing and routing rules. These solutions also offered complex knowledge management, email and chat engines that could support millions of interactions a month. Implementation tended to span many months, where professional services consultants dove into the business processes that agents followed, and then reproduced them in these enterprise solutions.
Yet these solutions - as powerful as there are - were too complex for our simple needs. There were no simple "out of the box" best practice process flows. There were no rapid deployment options to get a company up and running quickly. There were no simple ways of setting up FAQs or simple knowledge, or creating simple email and chat routing rules for a moderate volume of digital interactions. What we needed was a highly usable solution, with a quick time-to-value, which contained just the most common functions of the enterprise solution.