“Dream no small dreams for they have no power to move the hearts of men.” — Goethe
- The Digital Mesh –
The rise of the machines has been well documented but enterprises are waking up to the possibilities only recently. Massive data volumes are now being reliably generated from diverse sources of telemetry as well as endpoints at corporate offices (as a consequence of BYOD). The former devices include sensors used in manufacturing, personal fitness devices like FitBit, Home and Office energy management sensors, Smart cars, Geo-location devices etc. Couple these with the ever growing social media feeds, web clicks, server logs and more – one sees a clear trend forming which Gartner terms the Digital Mesh. The Digital Mesh leads to an interconnected information deluge which encompasses classical IoT endpoints along with audio, video & social data streams. This leads to huge security challenges and opportunity from a business perspective for forward looking enterprises (including Governments). Applications will need to combine these into one holistic picture of an entity – whether individual or institution.
- Information of Everything –
The IoT era brings an explosion of data that flows across organizational, system and application boundaries. Look for advances in technology especially in Big Data and Visualization to help consumers harness this information in the right form enriched with the right contextual information.In the Information of Everything era, massive amounts of efforts will thus be expended on data ingestion, quality and governance challenges.
- Ambient User Experiences –
Mobile applications first begun forcing the need for enterprise to begin supporting multiple channels of interaction with their consumers. For example Banking now requires an ability to engage consumers in a seamless experience across an average of four to five channels – Mobile, eBanking, Call Center, Kiosk etc. The average enterprise user is familiar with BYOD in the age of self service. The Digital Mesh only exacerbates this gap in user experiences as information consumers navigate applications as they consume services across a mesh that is both multi-channel as well as provides Customer 360 across all these engagement points.Applications developed in 2016 and beyond must take an approach to ensuring a smooth experience across the spectrum of endpoints and the platforms that span them from a Data Visualization standpoint.
- Autonomous Agents and Things –
Smart machines like robots,personal assistants like Apple Siri,automated home equipment will rapidly evolve & become even more smarter as their algorithms get more capable and understanding of their own environments. In addition, Big Data & Cloud computing will continue to mature and offer day to day capabilities around systems that employ machine learning to make predictions & decisions. We will see increased application of Smart Agents in diverse fields like financial services,healthcare, telecom and media.
- Advanced Machine Learning –
Most business problems are data challenges and an approach centered around data analysis helps extract meaningful insights from data thus helping the business It is a common capability now for many enterprises to possess the capability to acquire, store and process large volumes of data using a low cost approach leveraging Big Data and Cloud Computing. At the same time the rapid maturation of scalable processing techniques allows us to extract richer insights from data. What we commonly refer to as Machine Learning – a combination of of econometrics, machine learning, statistics, visualization, and computer science – extract valuable business insights hiding in data and builds operational systems to deliver that value. Data Science has evolved to a new branch called “Deep Neural Nets” (DNN). DNN Are what makes possible the ability of smart machines and agents to learn from data flows and to make products that use them even more automated & powerful. Deep Machine Learning involves the art of discovering data insights in a human-like pattern. The web scale world (led by Google and Facebook) have been vocal about their use of Advanced Data Science techniques and the move of Data Science into Advanced Machine Learning.
- 3D Printing Materials –
3D printing continues to evolve and advance across a wide variety of industries.2015 saw a wider range of materials including carbon fiber, glass, nickel alloys, electronics & other materials used in the 3D printing process . More and more industries continue to incorporate the print and assembly of composite parts constructed using such materials – prominent examples including Tesla and SpaceX. We are at the beginning of a 20 year revolution which will lead to sea changes in industrial automation.
- Adaptive Security –
A cursory study of the top data breaches in 2015 reads like a “Who’s Who”of actors in society across Governments, Banks, Retail establishments etc. The enterprise world now understands that an comprehensive & strategic approach to Cybersecurity has now far progressed from being an IT challenge a few years ago to a business imperative. As Digital and IoT ecosystems evolve to loose federations of API accessible and cloud native applications, more and more assets are at danger of being targeted by extremely well funded and sophisticated adversaries. For instance – it is an obvious truth that data from millions of IoT endpoints requires data ingest & processing at scale. The challenge from a security perspective is multilayered and arises not just from malicious actors but also from a lack of a holistic approach that combines security with data governance, audit trails and quality attributes. Traditional solutions cannot handle this challenge which is exacerbated by the expectation that in an IoT & DM world, data flows will be multidirectional across a grid of application endpoints. Expect to find applications in 2016 and beyond incorporating Deep Learning and Real Time Analytics into their core security design with a view to analyzing large scale data at a very low latency.
- Advanced System Architecture –
The advent of the digital mesh and ecosystem technologies like autonomous agents (powered by Deep Neural Nets) will make increasing demands on computing architectures from a power consumption, system intelligence as well as a form factor perspective. The key is to provide increased performance while mimicking neuro biological architectures. The name given this style of building electronic circuits is neuromorphic computing. Systems designers will have increased choice in terms of using field programmable gate arrays (FPGAs) or graphics processing units (GPUs). While both FGPAs and GPUs have their pros and cons, devices & computing architectures using these as a foundation are both suited to deep learning and other pattern matching algorithms leveraged by advanced machine learning. Look for more reductions in form factors at less power consumption while allowing advanced intelligence in the IoT endpoint ecosystem.
- Mesh App and Service Architecture
The micro services architecture approach which combines the notion of autonomous, cooperative yet loosely coupled applications built as a conglomeration of business focused services is a natural fit for the Digital Mesh. The most important additive and consideration to micro services based architectures in the age of the Digital Mesh is what I’d like to term – Analytics Everywhere. Applications in 2016 and beyond will need to recognize that Analytics are pervasive, relentless, realtime and thus embedded into our daily lives. Every interaction a user has with a micro services based application will need a predictive capability built into the application architecture itself. Thus, 2016 will be the year when Big Data techniques are no longer be the preserve of classical Information Management teams but move to the umbrella Application development area which encompasses the DevOps and Continuous Integration & Delivery (CI-CD) spheres.
- IoT Architecture and Platforms
There is no doubt in anyone’s mind that IoT (Internet Of Things) is a technology megatrend that will reshape enterprises, government and citizens for years to come. IoT platforms will complement Mesh Apps and Service Architectures with a common set of platform capabilities built around open communication, security, scalability & performance requirements. These will form the basic components of IoT infrastructure including but not limited to machine to machine interfaces,location based technology, micro controllers , sensors, actuators and the communication protocols (based on an all IP standard).
The Final Word –
One feels strongly that Open Source will drive the various layers that make up the Digital Mesh stack (Big Data, Operating Systems, Middleware, Advanced Machine Learning & BPM). IoT will be a key part of Digital Transformation initiatives.
However, the challenge for developing Vertical capabilities on these IoT platforms is three fold. Specifically in areas of augmenting micro services based Digital Mesh applications- which are largely lacking at the time of writing:
- Data Ingest in batch or near realtime (NRT) or realtime from dynamically changing, disparate and physically distributed sensors, machines, geo location devices, clickstreams, files, and social feeds via highly secure lightweight agents
- Provide secure data transfer using point-to-point and bidirectional data flows in real time
- Curate these flows with Simple Event Processing (SEP) capabilities via tracing, parsing, filtering, joining, transforming, forking or cloning of data flows while adding business context to these flows. As mobile clients, IoT applications, social media feeds etc are being brought onboard into existing applications from an analytics perspective, traditional IT operations face pressures from both business and development teams to provide new and innovative services.
The creation of these smart services will further depend on the vertical industries that these products serve as well as requirements for the platforms that host them. E.g industrial automation, remote healthcare, public transportation, connected cars, home automation etc.
Finally, 2016 also throws up some interesting questions around Cyber Security, namely –
a. Can an efficient Cybersecurity be a lasting source of competitive advantage;
b. Given that most breaches are long running in nature where systems are slowly compromised over months. How does one leverage Big Data and Predictive Modeling to rewire and re-architect creaky defenses?
c. Most importantly, how can applications implement security in a manner that they constantly adapt and learn;
If there were just a couple of sentences to sum up Gartner’s forecast for 2016 in a succinct manner, it would be “The emergence of the Digital Mesh & the rapid maturation of IoT will serve to accelerate business transformation across industry verticals. The winning enterprises will begin to make smart technology investments in Big Data, DevOps & Cloud practices to harness these changes “.