Forces shaping up the IT Industry
The fluidity of IT makes it very difficult and even risky to predict the disciplines that are going to take center stage. The IT industry goes through a constant flux with a penchant for new innovations that fuel the industry as it continuously tries to brace itself to foster and support the next generation ideas and innovation.
It is difficult, often times risky to predict what new may come up. However, we can make some educated guesses based on the disciplines that are getting more and more stabilized and mainstream in IT. As an example, the value of Analytics vis-à-vis its applicability in specialized industry domains has matured itself out of the hype circle into truly a competitive differentiator for organizations. Commoditization of analytics is seen as one of the few remaining competitive differentiators in the marketplace. Cloud computing has also become mainstream and organizations are realizing a significant reduction in CapEx spends on IT infrastructure by outsourcing the same under the auspices of the cloud computing paradigm.
However, most organizations realize that making their data (which often manifests itself as the key IP) available in the cloud is not only risky but also violates industry-driven data privacy and protection policies. As such hybrid clouds are going to be more and more pervasively implemented. The challenge with hybrid clouds boils down to data and network security. Advances in network and data security are ultimately going to be the lynchpin behind the commoditization of the two current forces in IT – Analytics and the Cloud.
What will make news is some advances in security that will be able to address the strict policies around data privacy and enable data to break the shackles of the traditional enterprise perimeters and accelerate the commoditization of the convergence of analytics and cloud computing.
Major challenges in IT Industry
The 3 Vs (Volume, Velocity, Variety) of Big Data has proven to be real. The real problem that organizations are facing more and more, with Big Data implementations, is that the issues with poor quality data are being magnified multifold. Poor data quality is conspicuous in its adversarial effects on the viability of analytical outcomes.
The challenge with predictive analytics is the reusability of analytical models in different context. Consulting firms often tout that they have analytical machine learnt models that are ubiquitously applicable in multiple organizations; some even go to the extent to declare that their analytical (predictive) models are applicable across industries. The reality is that the applicability of statistical and probabilistic models is not natural when moving from one organizational business problem (or context) to that of another. Models need to be refined, retrained and often rebuilt to support the variability of the business metrics across multiple organizations. For predictive analytics, their reusability across multiple organizational business problems (even if they are similar in nature) continues to be a challenge.
Another challenge in predictive analytics is the timeliness of its application. More and more companies are now expecting their predictive algorithms and models to be applied closer to the point of origin of the data. Edge computing will require addressing the invocation of predictive models at the edge.
Big Bets in Information Technology - 2016
Internet of Things (IoT) addresses the ‘last mile’ problem in analytics. The real value of IoT comes from the art of the possible in turning physical assets into participants in the real-time global digital markets. The bets are big in IoT: the IT giant IBM announced (in 2015) its investment of $3 billion in the establishment of an entirely new business unit focused on IoT.
The true realization of IoT will only happen when the line of demarcation between the OT (Operational Technology) and IT will cease to exist in ways such that the physical assets become as ubiquitously available and accessible as traditional data sources like relational databases. Since the bets are huge, both the OT vendors as well as the IT vendors are trying to make their mark in offering IoT solutions. These two traditionally disparate solution providers need to come together in solving the problem rather than trying to address the problem domain individually. Additionally, the IT industry will see the advent of tooling platforms that will accelerate the time-to-value of device connectivity by reducing the turnaround time to generate insights from what the ecosystem of physical assets are telling us; platforms like Bluemix are seeing an exponential increase in their adoption rates.
Cognitive computing is fast gaining it’s own space in the digital transformations. When IBM’s Watson won the Jeopardy game, by defeating the two top Jeopardy players, the true potential of cognitive computing just started to surface. However, it took a few years to use the power of the Watson technology in truly transforming the human experience. When Watson was able to predict the skin cancer called melanoma with an accuracy that surpassed the diagnostic predictions of the best doctors in the USA, the value and reach of cognitive computing became truly transformational.
Cognitive computing will not, in anytime in the near future, be a replacement of the humans. However, the value of cognitive computing will be increasingly harnessed as an aid to the human cognition by distilling out insights and knowledge from potentially all data that is available in the Internet and beyond. The applications of cognitive computing will be manifested as Advisors that help the humans, through real-time intuitive interactions and engagements (with the humans) in taking better, more informed and timely decisions. Cognitive Advisors will become a big bet in the next generation IT.
Significance of Cloud Stack in Business
Cloud computing is a relatively new paradigm which has caught on immensely quickly in the industry. In fact, any enterprise which has an IT presence are considered to be quite far behind if they do not have some form of a cloud-based infrastructure and computing strategy. Cloud computing obviously has some distinct advantages which makes it such a powerful value proposition.
Some of the value propositions, but obviously not limited to them, would be the following:
• Reduced capital and operational cost –as infrastructure and compute can typically be requested and made available on-demand with the elasticity to grow on an as-needed basis. No upfront locked in cost of standing up the infrastructure, regardless of its usage, and monitoring and maintaining the same. The billing model supports pay as per usage; the infrastructure is not purchased thus lowering maintenance; both initial and recurring expenses are much lower than traditional computing.
• Massive data storage – as storage & maintenance of large volumes of data on an elastic compute platform is possible. Sudden workload spikes are also managed effectively & efficiently on the cloud owing to its dynamic scalability.
• Flexibility –for the enterprises as they need to continuously adapt even more rapidly to changing business conditions: speed to deliver is critical requiring rapid application development which is made possible by assembling the most appropriate infrastructure, platform and software building blocks on the cloud platform.
However, there are some inherent challenges that ought to be addressed. Some of the challenges stem from the following:
• Data security - is a crucial element as enterprises are skeptic of exposing their data outside their enterprise perimeter; they fear losing data to competition and the data confidentiality of consumers. While enterprise networks traditionally put the necessary network infrastructures in place to protect the data, the cloud model assumes that the cloud service providers to be responsible for maintaining data security on behalf of the enterprises who would have to rely on them.
• Data recoverability and availability – requires business applications to support, often stringent, SLAs. Appropriate clustering and failover, disaster recovery, capacity and performance management, systems monitoring and maintenance becomes critical. The cloud service provider needs to support all of the above; their failure could mean severe damage and impact.
• Management capabilities – will continue to challenge the current capabilities and will require pushing the envelope towards more autonomic scaling and load balancing features; far more sophisticated than what the current cloud providers can support.
• Regulatory and compliance restrictions – place stringent laws around making sensitive personal information (SPI) being made available outside the country borders: pervasive cloud hosting would become a challenge since not all cloud providers have data centers in all countries and regions.
However, the advantages significantly outweigh the challenges to make cloud computing a significant value proposition to fuel its exponential growth of adoption.
With sustained adoption comes stability. The combined value of IaaS, PaaS and SaaS will continue to stabilize and harden over the upcoming years. The added layer of value, which is built on top of the three (IaaS, PaaS and SaaS) layers will emerge from a different class of cloud based systems that can be marketed as ‘Solution As a Service’, and which I abbreviate as SLaaS! The fundamental tenet of SLaaS is that it completely abstracts the three traditional cloud layers and only exposes a set of consumable solutions (which are ideally industry specific in nature).
SLaaS solution providers will be able to monetize industry solutions and applications through a truly rental model. We will see the emergence of SLaaS providers who will make bold moves to charge their customers based on the insights they provide. Imagine upcoming times when SLaaS providers will charge fees for providing base insights, and increased premiums for competitive and differentiated insights!