What are the key digital and technology trends for this year? What business outcomes can organizations expect from these technology trends and how are these technologies expected to benefit organizations?
So, with that let’s get started and review the top seven trends that I see are on CIOs agendas for this year.
There’s nothing new when we say that we live in a world where change is the only constant. What is surprising however is the rapid pace of change that industries and organizations are experiencing and going through. The impact of the change is being felt almost globally and across all organizations and industries, as organizations rush to alter their business models to minimize the impact of disruption triggered by this constant and rapid change. A number of new technologies are causing this disruption and triggering new business models. To stay ahead of the curve, organizations should get aggressive about exploring opportunities and explore the relevance of these technologies to their organizations.
Technologies that are enabling this change and our new world are many. But in today’s episode we will focus on technologies that are at the forefront and are changing the way organizations operate and compete in the new economy. Any organization that hasn’t yet identified potential use cases for the application of these technologies should do so before they are left behind by their competition.
CIOs across industries are applying these technologies to bring business benefits such as increased automation, improved user experiences, new products and services, and other such business outcomes for their organizations.
A review of these technological trends can help you learn not just some of the buzzwords but also to learn about their disruptive potential, the opportunities they offer, and what organizations need to do to start weaving them into the fabric of their organizations.
Before I jump in and cover those 7 trends, they are:
- Advanced and Augmented Analytics
- Digital Twins
- Blockchain
- New trends in Cloud Computing
- XaaS (Anything as a Service)
- Internet of Things
- Mixed Reality (which includes Augmented and Virtual Reality)
Advanced and Augmented Analytics
The first trend I would like to highlight today is about the advances that are occurring in the field of analytics. We can call it advanced analytics or as a number of research organizations like Gartner like to refer to it as Augmented Analytics. Essentially, advanced or augmented analytics is analytics on steroids. More specifically, its analytics coupled with Artificial Intelligence and automation.
Over the past few years, we have observed how numerous technologies in the area of AI, ML, and data science have made it possible for organizations to run sophisticated analytics on the massive amounts of data that they are generating, along with the historical data that they already have and the external data that they can get to from social media and other sources. However, accordingly the amount of preparation and other related manual work has made this process quite tedious. As the need for getting ones hands on advanced analytics and business insights goes up, it has resulted in more work related to data preparation, data discovery, ML model selection, searching and querying for data insights and more. To tackle the manual and laborious effort that accompanies such tasks, many systems and tools today are embedding a number of features that are automating a number of these steps. This then is helping businesses to focus on getting key business insights and intelligence more quickly thus helping them make the right decisions rather than struggling with the many steps of getting to that point.
So, getting back to augmented or advanced analytics – we can say that this discipline refers to a suite of technologies in the realm of analytics that brings more automation and intelligence to this overall process of data preparation, discovery, model creation to analyze and extract insights, and searching for or querying of insights through a natural language interface. Again, the idea is to help organizations spend more time on acting on the insights and intelligence that they derive from data rather than spending manual time and effort on preparing the data and getting insights from that data.
For CIOs this should especially matter if they have been investing in standalone data science, data integration, and other analytics solutions as they may find that a number of the functions and features that they may be paying for are perhaps now being addressed by their respective systems of record or ERP vendors and these functions are features are perhaps becoming more accessible part of other tools as well. For sophisticated AI applications they may still need those tools but that’s something that should be looked at more closely on a case by case basis for each business. If, for example, we look at enterprise systems related to HR, CRM, finance, procurement, customer service, and others, we will see that a number of them now incorporate a number of such capabilities to help their users with their decision making. To cite a specific example, SalesForce has introduced Einstein Analytics, which is essentially advanced analytics powered by AI. These sutie of tools on SalesForce pack a lot of functionality related to providing predictive insights and prescriptive recommendations and accordingly provide apps and functionality, which allow organizations to not only visualize their overall sales and marketing pipelines but also help in making complex forecasting decisions.
We should note that there is no one tool per se to deliver all such capabilities for an organization but rather it’s something that organizations must understand as part of their overall AI, analytics, and decision making framework so they can start making the right changes to their processes and overall strategy of acquiring and operationalizing technology related to getting more intelligent insights. By understanding the overall process and the complexities inherent in the process, organizations will start to select the right business tools which will make it easier for them to get the right insights and to automate the many tasks that are involved in the derivation of these business insights so they can make faster decisions.
We see analytics also getting advanced to a level where organizations are enabling constant streaming of data from their business processes and analyzing that data in real time to give them real time intelligence and business insights. A number of technologies come together to make this happen including those of Artificial Intelligence, ML, DL, data management, data science and others. The idea is to have an organization’s systems and processes create new intelligence constantly to help it in making instant decisions.
Digital Twins
The next trend I would like to cover is that of digital twins. A digital twin refers to a digital representation of any physical entity, which needs to be monitored. Physical entities include but are not limited to people, process, equipment, places, and others. Creating a digital twin (or having a digital representation of the entity) allows an organization to study the behavior of the actual physical entity and to run various types of analysis to understand its behavior, improve its functioning, perform diagnostics, and more.
Due to the nature of the digital twin, it’s important in many cases to have data related to the entity constantly transmitted to the system using sensors and IoT devices. For example, to have a digital twin of an equipment such as a jet engine or large machinery, sensors on them would constantly transmit data to the main system providing engineers deep insights into the behavior of the equipment. They can then use that data to predict failures, test configurations, and perform more of such analysis.
Although the idea is not new and we have seen its applications on a number of platforms, its adoption is still not as widespread. For example, GE’s Predix platform is a digital industrial IoT platform and cloud based service that maintains digital representations or digital twins of various industrial equipment and provides its users the ability to run analytics and learn more about the devices and equipment that it monitors. This concept of digital twins is also used in managing assets in the energy sector where lifecycle of physical assets can be studied and improved. Organizations, for example, can monitor offshore oil rigs and study variables, which can further help in improving their performance without being at the physical rig itself.
IoT and sensors have further popularized the concept of digital twins and its use is expanding to pretty much all industries where there’s a need to monitor physical entities.
Blockchain
Moving on, the next technology that we will discuss is that of Blockchain. Blockchain is a distributed ledger technology that provides decentralized trust across a network of untrusted participants. Cryptocurrencies like Bitcoin and Ethereum were founded on these technologies. Since then, interest in Blockchain technologies and relevant investments has grown exponentially for the past few years. According to Statista, a leading provider of market and consumer data is forecasting that blockchain technology revenues will grow to more than $23 Billion by 2023. Organizations that have shown more interest in Blockchain are from the financial industry — and that’s for a good reason. It’s in this industry where organizations participate in carrying out financial transactions in the larger ecosystem and have to trust each other. Blockchain due to its distributed ledger technology solves that problem for them.
Other organizations from different industries are also experimenting and implementing blockchain in their businesses. We see this technology being applied to solve manufacturing supply chain issues, food and agriculture industries, and others.
A number of technology service providers have built platforms, which allow organizations to build and deploy blockchain platforms. IBM, Microsoft, and Amazon provide cloud based blockchain services, which many have been experimenting with for the past couple of years.
Building enterprise blockchain solutions can be more challenging as it not only requires a strong technology platform but also cooperation from various participants. It also requires extensive testing to ensure security, performance, trust, and scalability issues are appropriately addressed. It’s for this reason that sometimes it takes longer and relatively more extensive planning to roll-out these solutions.
Cloud Computing
Another technology worth mentioning again is that of cloud computing. Although, the move to cloud computing has been going on for a few years now, but we also know that not all organizations have moved to the cloud. Many who have started the process, have only moved a fraction of their workloads on the cloud. But due to the successes of this computing paradigm the trend continues to be hot and enterprises will continue to invest a sizable chunk of their investments to migrate their old and new workloads to the cloud. Although the initial move to the cloud was triggered by cost reasons and for software and applications to be used as a utility, many other advantages have come to fore over the past few years. A number of technical innovations over the past few years simply would have not been possible without cloud computing. This includes but is not limited to Artificial Intelligence applications that deal with a lot of data and need massive processing power. Technologies such as Blockchain and IoT also have gained a lot from having a cloud based backend. So, in general we can say that enterprises have plenty of reasons to move to the cloud.
Before discussing some of the trends related to cloud computing, let’s review some of its basics. Cloud computing is a computing paradigm, which allows for network access to a number of computing services made available through shared physical and virtual resources. Cloud computing services are available in different configurations. First, in a non-cloud environment, an organization manages all of the resources and services related to networking, storage, servers, virtualization, O/S, Middleware, Data, and applications. In an IaaS configuration, everything related to the applications and middleware is managed by the organization, whereas the cloud service provider manages the bottom layers of the stack namely the networking, storage, and hardware. In the PaaS configuration, with the exception of the applications and data, everything else is managed by the cloud service provider. Finally, in the SaaS model, all layers are managed by the service provider. So, which model you or your organization decide to get depends on your specific business case.
Having said that, we should mention a number of trends in that arena. First, we see that the hybrid cloud environments are becoming more popular and many organizations, especially the larger ones are settling on this paradigm. A hybrid cloud environment is one where an organization uses a mix of public and private clouds and on-premises environments for its computing needs. Although a number of enterprises have started the move to the cloud, many have come to realize that they won’t be able to (or have reasons to) migrate all their workloads to the cloud. So, a hybrid cloud configuration gives them the best of the different worlds out there and gives them the flexibility of capitalizing on cloud technologies for applications that can benefit the most from such computing. Another trend that we see within the realm of cloud computing is that of serverless computing because it provides the organizations the flexibility to get and pay for the software and computer services without worrying about the underlying infrastructure. In serverless computing although the servers are still there but customers don’t have to worry about them as they are focused on getting software and compute services from the cloud services provider. This is another step up from the orginal cloud computing model where customers have only to pay for the compute services they use rather than leasing cloud resources that they wouldn’t use. In the past couple of years, the industry has seen a rising popularity of this model, especially if there are services that organizations can use from the cloud services provider without worrying about getting the underlying infrastructure.
Another cloud computing model that has been on the rise is that of edge computing. Edge computing is an architectural construct, which refers to running certain programs near the edge without having to run everything on the cloud. This helps with latency, bandwidth, and other performance issues and compute tasks and information can be allocated across the overall architecture more intelligently. This model has become more popularized with the emergence of IoT computing, where computing can be run on or near the IoT devices near the edge without overloading the cloud.
XaaS (Anything as a Service)
The next trend that we will cover has to with CSPs providing Anything as a Service or XaaS as it’s usually written. Xaas or Anything as a Service is a general term that refers to various services that are available through Cloud Service Providers. Over the years with the successful adoption of cloud computing and the services that one can get from the various cloud service providers, the world has seen service providers increase the number of such services. We are all familiar with the IaaS (or Infrastructure as a Service), PaaS (Platform as a Service), and SaaS (Software as a Service). In addition to these, other types of services have started to come up recently. So, here we will review some of the services that are becoming increasingly popular.
The first one I will state is the Storage as a Service. With demand for storage constantly growing, organizations are increasingly running into limitations of having their own storage irrespective of the type of cloud environment that they have. For more storage needs, organizations are therefore, turning to their CSPs for their storage needs as through them they are finding better performance, scalability, flexibility, and manageability options for their storage needs.
Another cloud service worth mentioning is that of DBaaS (or Database as a Service). As organizations’ needsrelated to databases is constantly increasing, it’s equally becoming difficult and costly for them to provision, manage, configure, consume, and operate their databases. In such cases, organizations are turning to their CSPs, who alternately provide a better, efficient, cost effective and overall agile way to perform such activities.
A third service we will mention here is that of Analytics as a Service. We know how the field of business Intelligence and Analytics have exploded over the past few years. In line with that trend, CSPs have been maturing in the services that they have been offering and we therefore see many organizations turn to CSPs for their business intelligence and analytics needs.
Finally, in this context, we will also mention AI as a Service. As we have mentioned in the previous episodes on this show, many CSPs such as Amazon, Microsoft, Google, IBM, and others offer a number of AI based services through their public cloud environments and the consumption of such services is on the rise.
Besides these services, there are many others that CSPs provide. We will cover more of these services in other episodes of the CIOtechCentral podcast shows.
Internet of Things
The next technology I would like to focus on is the Internet of Things or IoT as it’s more popularly referred to. Although IoT and related technologies have been used in the marketplace for a number of years – may be even couple of decades, their use has skyrocketed more recently due to innovations such as cloud computing, faster Internet, ability to store data collected from those IoT devices and so on. If we look at manufacturing organizations, we will observe that they have been using these and related technologies for more than two decades where they had intelligent devices attached to certain manufacturing and factory assets collecting data on the health of those assets and then making that data available for analysis. However, the miniaturization of devices and other technologies that I just mentioned earlier has enabled IoT to take over the world by the billions and perhaps this number will go even higher. Intelligent devices are making their way in a wide number of business processes. And CIOs challenge is not so much about learning the basics of this technology because as I mentioned earlier, they have been around for a number of years but their challenge is more on how the larger acceptance and adoption of IoT will impact an organization’s technology infrastructure. So, that’s what we will look at today.
Let’s first take a quick look at the potential use cases in the market. If we look at the data from the various research organizations, it’s clear that the market for IoT is growing without bounds. Most electronic devices are now getting connected to the Internet and coming online providing more opportunities for them to communicate with each other. These devices include laptops, household appliances, automobiles, vending machines, and others.
IoT technologies are also finding their use in smart homes, wearables, industrial internet, retail, supply chain, etc. Cities across the world are deploying sensors in cameras, streetlights, and other electronics deployed across the city to track various types of activity. These sensors capture and relay all types of data including audio, video, and others. IoT is also being used in Industrial Internet settings to ensure effective operations of industrial equipment and to facilitate a safe working environment. The industry for Industrial IoT or IIoT is expected to grow exponentially until it makes its way in most industrial operations. These applications are already helping organizations control costs, increase operational efficiency, and facilitating safer industrial operations. This is impacting industries such as Oil and Gas, Healthcare, Electric and Water, Transportation, and others.
Key considerations that CIOs should be looking into have to do with ensuring implementation of the right IoT platform. This obviously is driven by the extent of the expected IoT use within the organizations. The overall IoT platform architecture usually comprises of the IoT devices communicating with some edge devices and then to some type of a gateway. The gateway connects to the cloud where most of the data collected from IoT devices is stored. Depending on the scale of the overall operation, organizations could be looking at storing massive amounts of data so scale should be considered. Some devices, for example, generate and transmit millions of pieces of information to the backend, which then necessitates considerations for data storage and processing at the backend.
Besides scalability, security should be another consideration to ensure end to end protection of all the data. Also, with all this data being generated, chances are that CIOs would want to process and store the data in a way to help them get the right analytics and insights. In that case, a number of system integration issues will also have to be considered that would ensure that all operations from data transmission from IoT devices to storage to processing at the cloud and making it available for analysis works seamlessly.
So, these are some of the issues that organizations will be grappling with this year and next related to IoT. Accordingly, they will be looking at the right solutions to ensure they can address these issues to maximize returns from this technology.
Mixed Reality
The last technology trend that we will look at today is that of Mixed Reality. Mixed Reality refers to two technologies related to Augmented Reality (or AR as it’s called) and Virtual Reality (VR). Although these technologies are still in the early stages of their development and use, according to Statista (which is a market research organization), the market for Mixed Reality is expected to hit around $4 Billion by 2025.
So, let’s review these two technologies. First, VR or Virtual Reality is a technology that uses computer simulation to provide the user with an experience of being in another location or space. Their common applications to date have been in the area of gaming and similar entertainment. Sophisticated headsets in the market that enable greater immersive experiences allow customers and organizations to explore new opportunities in the enterprise space. VR has potential applications in retail where it allows customers to experience the products or services in multiple dimensions and in greater detail and can help them decide quickly about buying those products and services. VR is also making its way in the education market where customers can go through better experiences for learning and training. The military, too, is making extensive use of VR in training their soldiers for tough terrains and situations.
AR, on the other hand differs from VR in that it provides a blend of real and virtual worlds where users while wearing special headsets can see a projection of a virtual world (such as graphics) onto the real world that they see. It essentially augments the user’s experience to allow them to experience virtual items in physical spaces. So, augmented reality enhances the real life environment and provides immersive experiences. It can be used in the area of Construction and Architecture for example, where structures can be superimposed as 3D visuals onto real space to provide an idea of how they would look like in reality. It can also be used in the field of education where supplementary information can be superimposed on the actual physical learning materials. Microsoft’s HoloLens and Google Glass are examples of products and services that provide both AR and VR capabilities. A number of other AR headsets, apps, glasses, and other devices have started to appear in the market. Their uses are becoming popular in a lot of industries such as manufacturing, retail, energy, and others. These devices can help in the performing of complex surgeries where surgeons can get an enhanced and 3D view of the area being operated on with the devices pointing out various details to help in the surgery.
Shoppers can walk into showrooms and don one of these headsets and see their choice of configurations before making a selection. For example, a shopper can visualize a car with a specific color, and other accessories before making a selection. Within a manufacturing setting, imagine an inspector walking onto a manufacturing floor wearing these devices, where these devices can point out certain processes, people, equipment and other details on the shop floor that can help in their inspection. Its use is becoming even more popular in the area of product design where designers can visualize a number of models and make changes before finalizing the design of the product. Headsets are available to allow users and tourists to visit places virtually and walk on a beach or downtown of a city and experience the city without actually going there or to get a taste of the place before buying tickets. Similarly, tourists can experience hotel rooms and other spaces before making their accommodation reservations.
As a technology leader of your organization, depending on their business of course, you can find its applications in a number of areas. You can also develop your own customized mixed reality experiences using various developer toolkits. Microsoft HoloLens among others provides that type of support. They also support an open API surface and driver model in line with open standards making it easier to build applications for your business that can provide immersive experiences. For Microsoft, they provide support for developing and deploying these applications on their Azure cloud platform where you can build cross-platform, spatially aware mixed reality experiences and connect it with other services as well.
