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When it rains, it pours. It seems that with regard to the innovation of corporate IT technology, it is common that more revolutionary innovations can hit the road at the same time. Still, if ever the analogy of painting the car while traveling along the highway is suitable, this time. Of course, you can wait and see the approach regarding adoption, but given the association of these innovations to greater business agility, you risk running the risk of lagging behind your competitors.
Let's take a look at what each of these innovations for the company means and their associated impact on the business.
First of all, let's explore the synergies of some of these innovations. Of course, every innovation can have and has a certain value of its own, however, when grouped together, they can provide powerful solutions to foster growth and new business models.
- Hybrid Cloud + IoT + AI / ML. IoT produces a lot of discharges (data) that translate into two main results: a) immediate analysis resulting in a directive for the IoT endpoint (the basis for many smartX initiatives) or b) collecting and analyzing model research. In any case, the public cloud will offer the most economical solution for IoT services, data archiving, processing and services to support machine learning algorithms.
- IoT + Blockchain. Blockchains provide immutable items stored in a distributed ledger. If combined with machine controlled voices, for example from an IoT sensor, we have non-refutable tests. This is great for tracing the chain of custody, not just the forces of order, but also perishable, such as meat and plants.
- Containers, DevOps and agile software development. These form the basis for providing solutions like those above quickly and economically leading to allowing the value to be realized quickly by the company.
There are companies that are already using these technologies to provide new and innovative solutions, many of which have been promoted by the press and conferences. While these stories illustrate a strong impulse, they also tend to favor the belief that these innovations have reached a sufficient level of maturity, such that the solution is not susceptible to lack of availability. This is far from the case. Indeed, these innovations are far from the mainstream.
Let's examine what it means to adopt IT and business for these different innovations.
Hybrid cloud
I have specifically chosen hybrid cloud and public cloud because it represents an even greater complexity for the corporate IT than the public cloud. It requires collaboration and integration between organizations and departments that have a common goal but very different approaches to achieve success.
Firstly, the cloud is about the management and delivery of software services, while the data center is responsible for providing both the infrastructure and the software services. However, the complexity and overhead of managing and providing a reliable and available infrastructure obscure the complexity of software services, with the result that they often receive much less attention in most self-managed environments. When the complexity surrounding the delivery of the infrastructure is removed, the operations team can focus exclusively on the delivery and consumption of software services.
Security is always a problem, but the process of maturation surrounding the provision of cloud services by leading cloud service providers means that it is an ever-changing environment. With security in the cloud, there's no room for errors or applications could be compromised. This, in turn, requires that after every security update around a service, the cloud team (architects, developers, operations, etc.) must educate themselves on the implications of the change and then evaluate how this change can affect on their production environments. Any misunderstanding of these updates and the environment could become vulnerable.
In addition, hybrid cloud often means that the team must maintain the traditional data center skills and at the same time add skills related to cloud service providers of choice. This is often overlooked in the assessment of cloud costs. In addition, highly qualified cloud staff is still difficult to attract and usually requires higher market wages. You could (and should) enhance your staff, but you will want some experts as part of workplace training for the public cloud, as unprotected public cloud could lead to compromising business situations.
Internet of Things (IoT)
The problem with IoT is that it is not a single thing, but a complex network of physical and mechanical components. In a world that is moving towards a high degree of virtualization, IoT represents a marked return to data center expertise with an emphasis on device configurations, disconnected states, size limitations of exchanged data packets and low-memory code footprints. Anyone in the early days of DOS PCs on the network will be able to relate to some of the constraints.
As with all digital, security is a very complex topic regarding the IoT. There are so many levels inside an IoT solution that accepts compromises: the sensor, the network, the edge, the data endpoint, etc. Because many of the devices that are part of an IoT network can be limited to resources, there is only one overhead that can be introduced for security before it compromises the purpose.
For many, however, when we talk about IoT we immediately see only the analytical aspects associated with all the data collected by the myriad of devices. Of course, by analyzing the data obtained from the sensor mesh and the on-board devices you can get an understanding of how things worked in extremely difficult ways with the coarse-grained telemetry provided by these devices. For example, a production device that reported problems with a low buzz before the use of sensors that now reveal that in tandem with buzzing, there is also an increase in temperature and an increase in vibration. With a few months of data collection, it is not necessary to wait for the buzz, the data indicate the beginning of a problem.
Of course, the value discussed in the previous paragraph can only be expressed if you have the right competent persons in the entire information chain. Those able to modify or configure endpoint devices to participate in an IoT scenario, cybersecurity and infosec experts to limit potential problems due to violations or abuse and to data scientists able to make sense of the volumes of data collected. Of course, if you have not selected the public cloud as an endpoint for your data, you also have the additional overhead of managing network connectivity and managing storage capacity associated with rapidly growing volumes of data.
Artificial intelligence and machine learning (AI / ML)
If you are able to exploit the power of machine learning and artificial intelligence, acquire knowledge of your company and your industry in a very difficult way until recently. While this is apparently a simple affirmation, that word "harness" is full of complexity. First of all, these technologies are more successful when they are operating against huge amounts of data.
The more data you have, the more accurate the results are. This means that it is up to the business to) find, aggregate, clean up and archive data to support the effort, b) formulate a hypothesis, c) evaluate the output of multiple algorithms to determine what will be the best support for the result that we are looking for predictions, trends, etc. and d) create a model. All of this is equivalent to a lot of legs to get the job done. Once your model is complete and your hypothesis has been proven, the machine will do most of the work from there on, but to get there it will take a lot of engineering work of human knowledge.
A point of caution, make business decisions using the result of your AI / ML models when you have not followed each of these steps and then qualified the model result against the real world at least twice.
Blockchain
Considered as the technology that "will change the world", but outside the cryptocurrencies, the blockchain is still trying to establish solid roots in the business world. There are many problems with the adoption of blockchain at the moment, the most widespread is the speed of change. There is no single standard blockchain technology.
There are several technologies that attempt to provide the basis for reliable and validated transactional exchange without requiring a centralized part. The purchase of a particular technology, at this point in the maturity curve, will provide information on the value of the blockchain, but will require constant care and feeding and the potential need to migrate to a completely different network base at some point in the future . So, do not bet the farm on the approach chosen today.
Furthermore, there are still many outstanding technical problems that depend on the value of blockchain, such as the legality of blockchain voices as a form of non-repudiation. That is, a blockchain can be used as evidence in a legal case to demonstrate the intention and validation of the agreed actions? There are also issues related to the effect that the use of a blockchain may have on various partnership agreements and credit agreements, particularly for global companies with GDPR requirements.
Finally, is the value of the blockchain a network large enough to impose consensus? Who should host these knots? Are public networks sufficient for businesses or is there a need for a private network shared between a community with common needs?
Containers, DevOps and Agile SDLC
I have grouped these three innovations together because, unlike the others, they are of a more technological nature and contain elements of the "how" more than the "thing". However, there is a considerable amount of attention paid to these three topics that extend well beyond the IT organization due to their association with companies enabled to become more agile. For example, I add my general disclaimer and my word of caution, technology is only an enabling factor, it is what you do with it that could be valuable or could have an opposite effect.
Containers should be the least influential of these three topics, as this is simply another way to use computing resources. Containers are smaller and lighter than virtual machines, but still facilitate a level of isolation between what is running in the container and what is running outside the container. The complexity comes from moving the processes from bare metal and virtual machines into containers because the containers take advantage of the machine's resources differently than the platforms mentioned above.
While it is quite simple to create a container, making sure that a group of containers can work together reliably can be fraught with challenges. This is why container management systems have become increasingly complex over time. With the addition of Kubernetes, companies actually need knowledge of data center operations in one team. Of course, public cloud service providers now offer managed container management systems that reduce the requirements for such a large set of knowledge, but it is still the task of operations to know how to configure and organize containers from a performance and safety perspective. .
DevOps and Agile Software Development Lifecycle (SDLC) force internal engineering teams to think and act differently if they are moving from traditional cascade development practices. Many companies have taken the first step in this transition by starting to adopt some Agile SDLC practices. However, due to the need for retraining, hiring and supporting this effort, the provisional state in which many of these companies have been called "wagile" means a combination of cascade and agile.
As for DevOps, the metrics related to the commercial value of becoming a performance and organization provider of high performance operations were published. In this age of "software is eating the world", can your organization ignore DevOps and if not ignore, take years to transition to the transition? You will hear stories from companies that have adopted DevOps and Agile SDLC and make great strides in reducing latency, increasing the number of versions they can do over a period of time, and delivering new features and functions to production at a much faster pace with less change the failures. Many of these stories are real, but even in these companies, you will still find pockets where there is no adoption and still follow a cascade SDLC that takes ten months to get a single release in production.
Conclusion
Individually, each of these innovations requires trained resources, funding, and it may be difficult to go beyond the proof of concept to achieve fully operational production results. Combined, in addition to the existing operational pressures, these innovations can quickly overwhelm even the most experienced corporate IT organization. Even in cases where multimodal IT and these innovations occur outside the traditional IT path, it will be necessary to support existing IT knowledge and experience. For example, if you want to analyze the buying trends of the last five years, you will need to support the teams responsible for your financial systems.
All this leads to the really big question, how should companies absorb these innovations? The pragmatic response is naturally to introduce those innovations related to a specific company result. However, as stated, the expectation of introducing some of these innovations could lead to losing ground over the competition. This means that you may want to present some proof-of-concept projects, in particular on AI / ML and Agile SDLC with IoT and Blockchain projects, where they make sense for your business.
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