OK, so I’ve sent a pile of presents from Amazon, lined up a James Bond rerun, added “Love Actually” to my Netflix playlist, split enough oak for the holidays, ordered a tin of celebration chocolates, and now comes the obligatory Top 10 Predictions list for 2018.
This year after noodling the question, I have some not so earth-shattering predictions, a few controversial ones, and a couple of subtle nuances to share. I do think they broadly cover what we’ll see as the year unfolds so here goes:
- Emergence of work Robo advisors
If you’re not Rumpelstiltskin, you’ve no doubt noticed the prolific rise of the talking speaker. After living with one for a while, I can’t help thinking it’ll make its way into the workplace post haste for appointment setting, travel booking, etc. There seems little to technically hold this back and from a cost optimization and process automation perspective, it makes all the sense in the world.
- Augmented Reality meets GoToMeeting
A little play off #1, but if you mash what’s going on with AR with the prohibitive cost of dedicated telepresence rooms, you get a budget busting way to see a screen with you and your colleagues sitting at the same table and interacting. You’ll probably also be able to attend meetings in virtual person through a physically present android of some semblance (R2D2 style or perhaps like that annoying holographic person in the airports).
- Self-driving electric car innovations convergence hit a feverish pitch
Chunky four German auto armies (Audi, Mercedes, Porsche, and BMW) release competing vehicles resulting in Telsa stock being challenged over their stretched diversification strategy and the need to refocus on the immediate autonomous competitive onslaught. Basically, good for both sides and the consumer, but this drumbeat is getting louder and louder moving all-electric autonomous cars from “Off Broadway” hits to prime time viewing.
- First publicly recorded event showing risk of weaponizing AI
A rather unsavory topic, but with all the talk on cybersecurity, AI and drone weaponization, I feel we’ll sadly witness an event that will galvanize the industry and lawmakers into paying sharp attention to the rigors and governance of prevention.
- Cloud cybersecurity receives major focus as ransomware moves to homes through IoT devices
Nope, I am not just talking about security in the cloud, but the way the cloud has burst into our homes with the likes of Amazon’s Echo Dot Alexa. Now I am not targeting Amazon specifically or commenting on their security measures. But, when I looked recently, I have ~30 connected devices in my home of which only one of them is a computer. Generally, consumer electronic companies don’t have the same security rigors as enterprise companies so the likelihood of your home network being breached by your fridge is alarmingly high. If you put this fact together with the feeling that companies are starting to get their arms around ransomware, the result is forcing the fraudsters to move to greener pastures. This just might mean the fraudsters opt to go down your Comcast pipe and start running the same plays on you.
- Blockchain technology breaks out of FinServices and becomes chic, new, smart contract and business model
Lots of predictions on Blockchain moving more into financial services, but that’s like saying it will rain next year. Looking at the value of distributed bookkeeping and the cost associated with chasing a PO through the system, you can’t help but see it would be fast tracked if Blockchain were being utilized. Companies could start to implement a smart contracts facility that has auto-pay-for-service or perhaps the digital economies incarnation of the utility computing business model. Progress indeed.
- IoT dedupe micro service market emerges
There’s just too much stuff being spewed out and collected just in case. Storage costs, and just as importantly, network and cloud utility billing denotes the need to be smarter at what gets sent, which demands a model. Likely, we will see the emergence of architectures similar to the retailers with HQ, distro centers, stores, and tills (till being the equivalent of an IoT device) equating to a distributed data mesh and edge dedupe for IoT.
- Customers, not vendors, are driving innovation
Technology innovation is happening at a dizzying pace with myriad technology companies, large and small, tracking credit for these innovations. However, it’s really the customers who are the driving force behind many of the game-changing solutions that are developed. In our world, big data analytics, customers know what outcomes they need to be more competitive and, in turn, deliver value to their customers. The problem is, more often than not, they can’t find what they need and, as a result, innovation is happening at the customer site, creating their own data science recipes and exploring advanced development utilizing building blocks the industry enjoys. In 2018, we’ll see that model further evolve: more customers will seek out an accelerated development lifecycle model that allows them to keep innovating and evolving in smaller and more rapid increments. This might not sound like much of a shift but it’s simply not the vendor community breaking new ground anymore. The innovation baton is firmly in the hand of the customers triggering vendors to both deliver outcome-based rapid assembly building blocks and finding ways of weaving the wealth of originated ideas to a wider audience with monetary reciprocity.
- The data lake is recognized as an antiquated catch-all analytics approach and the Achilles heel for fast insight and actions needed to compete, Et Tu Batch!
With the advent of the Internet of Things, data growth is set to accelerate. Data sources are moving from humans to machines as sources move from web to mobile to machines. This has created a dire need to scale out data pipelines in a cost-effective way. Big data and cloud ecosystems realized that they cannot be just about search indexing or data warehousing. They need to service all enterprise data flow, be it human (web, mobile) generated or machine generated. The ability to respond in real-time provides a dramatic competitive advantage. An enterprise that can do predictive analytics in real-time will gain a competitive edge over one that does not. Big data needs to be real-time, agile, and operable. Moreover, to get real-time, agility, and even to some extent operability back, data lakes cannot be in between this data flow. The data lake served companies fantastically well through the data “at rest” and “batch” era. Back in 2015, it started to become clear this architecture was getting overused, but it’s now become the Achilles heel for REAL real-time data analytics. Parking data first, then analyzing it immediately puts companies at a massive disadvantage. When it comes to gaining insights, and taking actions as fast as compute can allow, companies relying on stale event data create a total eclipse on visibility, actions, and any possible immediate remediation. This is one area where “good enough” will prove strategically fatal!
- The commoditization of enterprise applications is on the rise
Long gone are the days when critical enterprise applications were developed in multi-year cycles. The rapid speed at which companies, and their adversaries (ransomware, account takeovers, etc.), are evolving requires the technology industry to change its enterprise application development and delivery model. The market is moving so much faster now. Enterprise-grade applications need to be deployed in weeks or months and certainly not years, but new apps generally don’t have the “bake in” time that gives them that industrialized robustness. A general rule of thumb is if a customer knows they need a change then they’re already late. Commoditized enterprise applications give customers critical building blocks at enterprise-grade, getting them to an outcome-based result within a quarter or two at most. The demands are such that there won’t be any leeway for availability or timeline so let’s just get used to developing in the digital economy.