Category: Blogs

Introduction

Today, more businesses are moving their products to the cloud as part of their digital transformation strategy. In fact, as of 2022, almost 23% of businesses with advanced functionalities (multiple applications/products) have moved their workloads to the cloud.

As cloud adoption maturity grows and more companies migrate their IT infrastructure to the cloud, we’re witnessing a considerable shift from developing products to building platforms.

Companies like Google, WordPress, Airbnb, YouTube, Uber, and Facebook are among those successful platform businesses that testify to the power of this architectural prowess over the more traditional product-oriented approach.

But what has made these software companies take the leap from building standalone products to platforms? That’s a question worth putting to the test. But before we can do that, it’s essential to understand what a platform really is.

What Is a Platform?

A platform is a combination of multiple similar products of a business across various segments. A typical platform maintains a range of software products that can cater to different user needs, thus bringing in significant value and the much-needed competitive edge.

With a great platform, one can witness a vivid difference in the speed, cost, and efficiency of developing new software products. Because having a platform strategy eliminates the need to develop a product from scratch.

Gartner’s definition of “Cloud Management Platforms” reads excellent within this context. The consulting giant refers to them as “integrated products” that “incorporate self-service interfaces, enable metering and billing, integrate with external enterprise management systems, include service catalogs, allow for enhanced resource management, etc.”

All in all, a platform works to:
● Ease the collaboration between the different stakeholders
● Standardize the data among the various systems
● Increase the scalability and flexibility of all the systems on board
● Create a unified user experience across all the systems
● Improve performance of applications integrated with the platform

Why Companies Need to Transition to Platforms

Developing a standalone product every time the need arises can cost tech companies heavily. If they’re looking for a better monetization opportunity while developing great products, having an effective cloud platform strategy is the way to proceed.

Here are more reasons why businesses need to consider progressing from product to platform seriously:

Gain a Larger User Base
A platform offers more utility by catering to the needs of various user groups. The business can rest assured of attracting a wide range of stakeholders — ranging from developers to service providers to product users.Airbnb and Uber, for instance, are platforms that connect app users to service providers directly. Thus, they foster seamless collaboration between all the parties — creating a win-win for them.

Better Customization of Products
Customizing software products per the market requirements over a platform is comparatively easier than standalone. As the products are interconnected, once the product is tested over a platform, adding customized tools to it is far less complex and does not require testing from scratch.

Increased Revenue and Scalability
A self-explanatory benefit of having a platform strategy is the possibility of attracting a vast user base for different product segments. Additionally, developing products is cheaper with platforms due to lower manufacturing costs and a lesser need for extra resources, licenses, and tools.Besides, platforms can indefinitely scale in terms of the number of users, performance, and optimized software products.

Enables Collaboration and Data Sharing
Cloud platforms are built to enable increased collaboration amongst its tools and stakeholders. They communicate by sharing data and internal processes within the existing products. So, one needn’t integrate independent tools for each product on the platform.

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Introduction

In 2022, more than 60% of all corporate data has moved to the cloud, and this trend continues to develop as companies want to shift their vital resources into a safer and better storage space for improved security, reliability, and ease of access.

As it stands, the tech for cloud computing, infrastructure, servers, and storage is incredibly versatile. Cloud computing, as a broad term, encompasses different types of services. Along those lines, this blog will discuss the three major types of cloud – distributed, hybrid, and multi-cloud, elaborate on their key similarities and differences, and outline relevant use cases.

What Is Distributed Cloud?

The distributed cloud architecture distributes cloud services to meet compliance goals and support edge computing or performance requirements. However, a public cloud service provider centrally manages it.

Because it is a public cloud, it runs across multiple locations, which includes the infrastructure of the public cloud provider, the data center of the cloud provider, or any third-party location.

With distributed services, organizations can meet specific requirements pertaining to performance and response time, governance or regulatory mandates, or availability zones.

Benefits

• Better compliance:To meet the regulatory requirements, workloads and the related data can be distributed naturally regardless of the location.
• Increased Uptime: As cloud services reside on the local subnets, you can isolate them or untether them from the cloud to ensure complete isolation.
• Flexibility:Simple installation and deployment, easy debugging.
• Scalability: Adding nodes or VMs as a requirement to increase scalability and improve overall availability.
• DevOps: Dealing with all the clusters equally while deploying applications.
• Consistency:Single core location to manage computing resources anywhere.

Distributed Cloud Use Cases

The distributed cloud offers an array of applications, from edge computing to multi-cloud environments. Some common use cases are:

• Content Optimization: Distributed cloud effectively converts into a CDN (content delivery network), improving the streaming experience and reducing load time latency to offer the best user experience across multiple applications.
• IoT/Edge: Facial recognition, video inferencing, manufacturing, medical imaging, smart buildings, etc., — the need for real-time data analysis across these applications is fulfilled by distributed cloud and edge computing.

What Is Hybrid Cloud?

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Introduction

Many projects fail because they commit to big bang deliveries without assessing
whether their product is a market fit. In fact, as per a CB Insights study, about 35% of the startups fail due to “no market need” — making it one of the top reasons for startup failure, only second to “insufficient cash/capital” (38%). That’s precisely where the significance of a minimum viable product (MVP) comes into play.

What is a Minimum Viable Product (MVP)?

MVP concept has originated from agile development best practices. This approach to
product development involves the gradual evolution of a product with consideration of
customer feedback and experience. In a nutshell, a product is minimally viable when it
has minimum features that can address customer needs and project the company’s core
value proposition.
MVP is key to business success because it:
● Validates the business opportunities and mitigates prototype risk before
developing a full-fledged product.
● Defines the brand and makes a value proposition to the current and potential
users.
● Helps draw potential investors and raise future funds that result in increased
scalability and revenue opportunities.

Categories of MVP

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Introduction

Did you know that poor documentation leads to 48% of all software project disasters? Some projects fail in simple documentation, whereas others have major defects within. Unarguably, mistakes drive learning; however, they can cost dearly if not rectified in time.

To that end, learning what is not right, what could go wrong, and ways to overcome the same can hone problem-solving capabilities and bolster judgment. This readily applies to product development.

New products go through a series of defined, though not essentially linear, phases to become market ready. Everything begins with ideation – a phase in which the product team thinks through different features of the products and brainstorms prospective designs. Thereafter, teams design, develop, and refine prototypes and return to the initial phase of ideation to address a roadblock or encounter more challenges.

Product design and development is one of the most elaborate disciplines involving multi-step processes. Every step involves multiple skills and departments, increasing the risk of expensive mistakes.

Noted below are the seven common product development mistakes that should be avoided at all costs.

1. Rushing the Development Phase – Starting Early

Product development involves three distinct phases: strategizing, conceptualizing, and developing.

The first phase is about discovering the product requirements and defining the strategy to create and deliver a fruitful product. The second phase is about taking the results of the first phase and determining the product architecture. The third phase involves taking the product in the direction validated in the second phase, making it design ready for production, testing it, and performing the first-run production.

Delivering a robust and valuable product should be the aim. While businesses may think the sooner the product is released, the better, they are grossly mistaken. The first two phases provide a foundation for product development and success. In these critical phases, target market and user needs are defined, product specifications and system-level architecture are planned, and the product concept is vetted through user evaluations and testing.

Skipping any of these steps in your quest to be hasty is WRONG!

2. Doing Too Much with Fewer Prototypes

Remember, prototypes answer questions. If a prototype is designed to address multiple questions, split them into different prototypes. When the development phase begins, making fewer prototypes may not allow the team to test parallelly.

Product development teams need to create prototypes and test them. It is quintessential to verify that the design meets the user requirements, design goals, and product specifications. The teams must also uncover design changes in the early development phase, which saves money and time.

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Introduction

IDC predicts that the spending on the Internet of Things (IoT) will surpass USD 1.2 trillion in 2022 worldwide.From manufacturing to retail, smart homes, and even healthcare, the IoT ecosystem is rapidly expanding its presence. With billions of devices getting connected to the Internet every year, it is about time that businesses evaluate the real end-to-end benefits of IoT in their operational models.However, it is not just the smart devices or machinery that contribute to the success of an IoT initiative. In fact, data from devices only form one part of the success formula. Collecting data from devices and your physical infrastructure alone holds very less significance. To derive real benefits, the key area of focus is the IoT software or platform that a business leverages to manage its data streams.Digital applications that can harvest the data from physical devices and compute valuable insights will be the key competitive advantage that businesses can rely on in tough markets. When we talk about IoT software, it encompases both in-house software that enterprises build for their needs as well as 3rd party software that they leverage from external vendors.In the end, it is the IoT software that enables the business to leverage the capabilities of IoT infrastructure to realize different real-world use cases. On this note, let us explore some of the top tips to derive real value from IoT software:

Build for scalability

For most businesses, IoT may start as an experimental initiative. But over time, as the business expands, the horizon of coverage for IoT integration also increases. This will directly correspond to increased data flow, more complex operational models being deployed, and newer and more powerful analytical and intelligent capabilities being expected from the IoT ecosystem.
The onus falls on the IoT software to accommodate the growth prospects of the business in every dimension. From having to handle a larger data volume to realizing objectives through supporting a larger number of business systems, there will be a huge demand for scalability.
Hence, it is important to have your business’ IoT software built with scalability as a central focus area. Leveraging cloud-based development approaches, opting for microservices or micro-front-end architecture, etc. are some of the ways to build applications with risk-free scalability. Leveraging a cloud-based approach also offers the freedom of on-demand availability and cost-effectiveness.

Secure your experiences

The progressive growth or business expansion mentioned earlier also implies that more consumer devices will get connected to your IoT ecosystem over time. More critical customer engagement channels will open due to this connectivity in sectors like healthcare and utilities. There will be a sizable amount of confidential or personal data that will be transacted in your IoT software. Additionally, the internal IoT software that an enterprise may develop to handle operations like performance monitoring of machines, proactive environment configuration, etc. As business expands, more plants in different geographies will be brought under your internal IoT software and this will directly increase the number of end-points that the enterprise’s digital landscape must entertain for data and information exchange. It will be your responsibility to ensure that the data is safeguarded from fraudulent activities or unauthorized access. While building your digital applications to handle IoT data streams, it is important to inculcate security as a core development philosophy in every stage of software development. Your technical or development team needs to be made aware of hidden risks and vulnerabilities that may become lethal if left unnoticed in the IoT network once deployed.

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Introduction

How do you prioritize what goes into your technology products? Do you build what’s easiest to build or dive deeper and create something more valuable?
If you are struggling to prioritize your digital and technology product roadmap, we developed a fun but scientific method that would work for any product. In this blog post, we will share how to use the concept of Value Potential to help you prioritize product features in product development.

Address your customers’ needs

Customer development is about building a product that meets the needs of your target audience. It’s about learning from real people why they would use (or not use) your product and what would make them more likely to use it.
This will help you determine which features are most important for them. If there are multiple solutions for one problem, prioritize based on which one works best for each user group. For example, if two different users want two different ways of doing something, then offer both options and let the users choose which one works best for them personally.
The best way to do this is by gathering customer feedback through surveys, interviews, or focus groups. Gather feedback from everyone from current users to potential customers and other stakeholders such as investors or partners who may have different perspectives than actual users. The more diverse your sample size, the better! If possible, ask them about their biggest pain points so that you can identify which features will provide the most value for them.

What functions do customers use the most?

It’s easy to get caught up in the excitement of adding new features, but it’s important to keep track of which features are most popular with customers.
You can use tools like Google Analytics, Mixpanel, or Kissmetrics to measure which features they often use. This will help you determine what features to prioritize — and which ones you can cut if necessary.
There is also another way to find out about customer preferences that take less time and effort. It’s called user testing.
In user testing, you get people who have never seen or used your product before and see what they like most about it. It’s a great way to test out different ideas on real users before you build anything.

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Introduction

With smartphones accessible to almost everyone lately, Medtech or healthcare applications are on the rise. The healthcare sector has realized the need to consistently keep up with the evolving technology that caters to patients’ concerns along with transforming their operations. A well-designed software for a healthcare domain should ideally focus on the ease of communication between patients, doctors, and clinic staff while adhering to industry standards and improving the quality of care.

As per GlobeNewswire, the IT market in the healthcare industry is predicted to be worth USD 76.45 Billion by 2025. And the credit goes to promising advanced technologies like AI, IoT, Big Data, and others that have a proven record in enhancing the healthcare IT infrastructure. A good combination of the rising demands of the customers with these advanced technologies can surely accelerate business growth and profits.
But how to be sure while looking for the right partner to help you implement your healthcare business idea? Here’s a look into what outsourcing your IT development means and what makes the software vendor reliable.

What does it mean to outsource software development in healthcare?

In the wake of the pandemic, many healthcare organizations have realized the need to focus more on personalized crisis management application upgrades. And there’s no better way to achieve this than by outsourcing to specialists in this field.
Outsourcing of services means hiring an expert partner to help execute and deploy the business requirements which is done through a limited period contract. And in this case, you would delegate the custom application development activity to a partner or a software vendor who are experts in the area.
Let’s now take a look at the ultimate checklist that you need to follow to hire the best partner for developing your healthcare applications.

How to know if you’ve found the right partner to outsource your custom healthcare application development?

For your application to be high-performing and to be put to optimum use, having a well-defined approach to choosing the best partner to outsource is quite crucial. Here’s a list that will help you decide.

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Introduction

Unlike your best friends and wine, the software does not get better with age. Clearly, your current smartphone is far more powerful than the one you owned years ago. Similarly, the business you are running is not the same it was when it was founded. And therefore, using legacy and outdated systems is not a smart business decision. Embedding digital at the heart of your corporate strategies and modernizing legacy systems is all you need to level up your game in the present era.

Why modernize legacy systems?

Digital transformation has made it imperative for organizations to uncover effective ways of optimizing their underlying infrastructure. Although legacy systems play an indispensable role in the overall functioning of the business, they just may not be capable of driving the level of efficiency that the modern world demands.

Here are 5 reasons why it’s critical to modernize legacy systems:

1. Competitive advantage: If you want to drive competitive advantage in your industry, modernizing legacy systems is crucial. With almost every organization today on the digital transformation journey, continuing to rely on rigid and inflexible systems will cause you to be a laggard, causing not just your customers but also your employees (and other stakeholders) to choose your competitors over you. Over time, popular enterprise solution PeopleSoft struggled to keep up with the requirements of the modern-day. Not only did organizations find it difficult to incorporate tailored business practices; they also couldn’t efficiently integrate their business via one unified platform.
2. Happier end users: Today’s users want the same sophistication and innovation in the enterprise systems they use as they enjoy in their personal lives. Legacy systems, being difficult to use and integrate, tend to impact the overall efficiency and productivity of end users. Modernizing them can aid in improving their morale and also the accuracy and timeliness with which they can accomplish tasks and meet goals.

3. Unlocking big data opportunities: Legacy systems do not possess the analytical capabilities that businesses today require. Since they are poorly integrated, they are incapable of sharing data with other systems in the tech ecosystem, leading to rigid data silos, and thus, poor decision-making. Modernizing them, instead, can allow you to unlock several big data opportunities, allowing for data to flow and be collected seamlessly – from across the organization. Post COVID, employment organizations in the US witnessed a severe inability to process the requests of 13% of the national workforce that were left without a job – mainly because their systems were more than 4 decades old, and were built using proprietary technologies like COBOL.
4. Future-ready business: Modernizing legacy systems while embracing the latest digital technologies like IoT, analytics, cloud & edge computing, digital twin, etc. can also make your business future-ready. Not only will you be in a better position to keep up with the latest trends; but timely modernization will also allow you to respond to new changes and requirements with far greater agility. The Department of Internal Revenue Service faced massive issues when their legacy tax filing systems could not process electronically filed tax returns of nearly five million Americans.
5. Better reliability and performance: Enhancing system reliability and performance are some other reasons for legacy modernization. By getting rid of features and functionality that are no longer required and updating or upgrading those on par with the latest innovations, you can make sure to leverage these systems to meet your targets. The decision to modernize Windows XP, for instance, was taken by Microsoft as the proprietary operating system could no longer keep up with the security and performance requirements of a modern world.
6. Ease of access: Another key reason to modernize legacy systems is to improve access. Since modern systems can be easily accessed from anywhere, they aid in improving efficiency and collaboration and in driving better business results. For example, enterprise software Tally was initially accessible only from a particular desktop/laptop. Post modernization, users can now access Tally from anywhere, at any time, and using the device of their choice.

When is the right time to modernize your legacy system?

There are no qualms about the fact that modernizing legacy systems is critical to the overall success of your organization. But the question is, when is the right time to modernize them.

Ask yourself these questions to get a fair idea of a potential timeline; if the answer to most of these questions is a “yes”, the time to modernize is now:

• Hidden costs of legacy systems: Do you find yourself incurring humongous costs in running and managing your legacy systems? Do you spend additionally to enable the flow of information? Are you forced to hire niche talent to troubleshoot problems?
• Maintenance and support: Are maintenance and support Herculean tasks for you? Do you lack the resources or skill set to manage your legacy systems? Do you end up spending endless hours repairing minor issues?
• Technical debt: Do you find yourself submerged in technical debt? Is the cost to make any tweaks or changes to the code a time- and cost-intensive process? Do you find yourself struggling to optimally maintain the system?
• Integration and modernization: Is the process of legacy system integration a nightmare? Do you end up investing in thousands of expensive plug-ins and add-ons, just so you can enable seamless integration?
• Security and compliance: Are the security and compliance issues you’re facing constantly increasing? Is your IT team incapable of patching security loopholes?
• Lost business opportunities: Do you find yourself losing out on business opportunities? Do your legacy systems restrict you from keeping pace with the latest trends and innovations?

Organization agility and efficiency: Do you struggle to drive organizational agility and efficiency? Are your employees taking way too much time in accomplishing day-to-day tasks?

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Introduction

Digital has become a centerpiece of innovation. It is no surprise that nearly every business in every sector has transitioned its legacy software ecosystem into one that encompasses the philosophy of product engineering. Today, at the heart of every business, is a tech company that creates a centralized platform-oriented ecosystem over which every business process is aligned, and every customer-facing initiative is planned.

On one side this approach creates a more manageable and flexible innovation ecosystem, but on the other side, it creates the need for a new approach to software product development- one that is driven and validated by data-centric outcomes.

In the digital economy, data is the new oil (some also call it new gold). Businesses today are exposed to truckloads of data streams flowing in and out of their information systems. They cover areas from internal operations to customer experience and are present in multiple formats. Becoming data-driven is no longer a choice, but an imperative.

In fact, studies by McKinsey pointed out that data-driven organizations have 23 times more chances of acquiring customers with 19 times more profitability prospects than organizations where data is less valued.

In this context, product development requires a paradigm shift from its traditional focus on engineering metrics and KPIs. While those traits need to remain in their best form, the changing reality of every product becoming a data product creates a new data-centric policy or framework that any product development activity must follow.

Let us explore the key changes in product development philosophies that are necessary to bring a data-driven objective to their business use-cases.

Prepare for data

The primary change that enterprise leaders must seek from their product development roadmap is creating an environment that fosters data generation and acquisition. A software product may have several interfaces and integration points adhering to multiple transactional processes and operational policies. At each of these venues, there should be a focus on generating data that validates every activity and directing the data to flow into data lakes or stores that capture and store them for further steps. Every workflow that the product is expected to exhibit over the course of its operations, should be dissected to see how well its progress can be plotted with the right data points and dataflows. Capturing every possible data element within the product is the best way to ensure that all facets of the product are covered when data-driven decision-making is made by leaders.

Set governance for data

We have seen in the previous stage the rush to make data generation and acquisition a priority. The next step is to set governance models and policies for utilizing the data thus collected. A product may be serving multiple business departments and helping them run different transactional processes simultaneously. Hence, it is critical to have controls in place that govern policies surrounding data ownership, access permissions, storage, cost parameters, etc. In short, there should be a clear direction on who owns which data and how they can use it or what data flows into which transactional process, without disrupting other stakeholders. Governance is also necessary to prevent data from losing integrity while at storage or in transit but leveraged by multiple stakeholders.

Train with the data

Artificial Intelligence (AI) and Machine Learning (ML) are fast becoming mainstream in digital services. For any AI and ML model to work efficiently, it needs a comprehensive training exercise which involves supplying it with a constant stream of operational and transactional data. We have already covered efforts needed to make information systems generate the most accurate data sets. There should also be provisions to utilize such data sets for training AI and ML models and empower them to gradually become self-reliant and ultimately enable better decision making.

The fundamental philosophies of product development will always surround quality, performance, and other known traits. But in a data-driven digital economy, it is equally important to have a roadmap to bring a culture of data-centricity to product development.

The four pillars we covered in this blog form the critical parts of a wider roadmap to building a truly data-driven digital product for your business. Get in touch with us to know more about how Pratiti can help build a data-driven product for you.

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Introduction

In the IT landscape, revolution happens rapidly. Businesses and their adoption of the latest trends in technology is a continuous process with no end in sight. The concept of DevOps is one such trend that has been implemented worldwide and helped transition organizational processes. And lately, NoOps has been making quite some noise in the IT sector.

In this article, we will understand the purpose of NoOps, how to start this journey and what makes it stand out from DevOps.

What does NoOps mean?

Before we look into what NoOps is, let’s brush up on the concept of DevOps first. DevOps is a collaboration of the operations and the development teams to work together to shorten the delivery cycles, continuously innovate, and work towards improving the software product quality.

And on the other hand, NoOps or No Operations is a relatively new concept. In NoOps, organizations need to aim at automating the operational processes entirely using the latest technologies like AI, Cloud, and Machine Learning. So much that there should be no need for an in-house operations team performing routine manual tasks. Instead, NoOps pushes the team to work on other innovations and development activities that help the business grow.

The NoOps process aims to reduce the infrastructural maintenance issues that developers or operators look after. This is so that more focus can be on improving the product quality through continuous innovations and delivery alone. If NoOps is implemented successfully, there would be no manual interventions needed with the infrastructure, as it would be taken care of automatically. Thus, NoOps helps reduce operational costs and human efforts significantly.

DevOps vs. NoOps – The Key Differentiators

Since we’ve already looked into what the concepts of NoOps and DevOps mean, let’s briefly look into their key differentiators to gain a much better understanding.

This does not mean that it is the end of the DevOps era. DevOps is indeed a continuous process with an end goal of delivering high-quality applications in time. With NoOps, the goal is to minimize manual interventions and make everything deployable by design through automation.

Shifting to a Culture of NoOps

Transitioning the software development culture in an organization to implement the NoOps model is a whole different ball game. It requires a thorough analysis of the systems and resources, owing to the fact that NoOps is a rather new concept for many.

Here are the mandatory steps to be taken on the journey to NoOps.

1. Using the right tools

To enable smoother automation, the right use of automation tools enables development teams to fast-track their changes and deploy. The IT teams need to be trained to have a clear understanding of how these tools and technologies are used and any change in roles when the new NoOps model is adopted.

Tools based on AI and Machine learning are most beneficial while implementing the NoOps model, thanks to their problem identification and automation capabilities.

2. Shift quality to the left

Shift left is a well-known process followed in software delivery, where defects are pre-identified and prevented from happening in the later stages of development. And shifting quality to the left ideally means making sure the quality of the software under the development stage is as good as the final software product used by customers.

This ensures minimal human intervention in maintaining the software once it’s in production and enables higher customer satisfaction too as the application is available at all times.

3. Use of cloud platforms and managed services

Managed services typically are designed to handle everyday operations of the in-production applications, thus reducing the burden on developers and maintenance staff. A managed service provider or MSP also helps maintain secure systems and network support. Hence, managed services would make a good ally of NoOps.

Organizations transiting to NoOps can choose to work with a suitable cloud platform, provided they support infrastructure management, seamless connections to the cloud network, and monitoring as key aspects.

4. Instilling a mindset of automation

The transition to NoOps is not immediate. Employees are to be trained sufficiently so they are well aware of their role changes and day-to-day task upgrades. The goal of NoOps should be to create a work culture and a mindset where automation is the top focus. And when the process and people are in-sync, the delivery of new services or applications can be accelerated tremendously.

5. Getting the stakeholders on-board

Getting the nod from the management and stakeholders enables an uninterrupted journey to NoOps, as there would be no managemental disagreements to attend to at later stages. Organizations with ambitious NoOps goals need ample support from all the relevant stakeholders so that there is a clear understanding of all concepts and deliverables between all the parties.

6. Find the right vendor for a smoother transition to NoOps

A suitable technology partner can provide IT automation solutions along with numerous features and expert approaches for NoOps. They can help understand the business needs, analyze environments, and design automation solutions based on them. Partners can also provide the right IT automation platforms and suggest the right tools and technology that help further optimize the systems and accelerate software delivery. This helps the development teams to focus more on innovations.

2022 is by far the best year to embark on the NoOps journey since automation is playing a crucial role in the growth of an organization – both in terms of software development and delivery.

With Pratiti Tech, the software product services and solutions expert, get the exact strategies and tactics from our NoOps experts to make an informed decision to execute NoOps.

Contact us today and take the first step towards transformation.

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Introduction

“If you think that the internet changed your life, think again. The Internet of Things is about to change it all over again!” – Brendan O’Brien

In the age of digital technologies, it’s hard to leave out the Internet of Things (IoT) from the digitization mix. As devices get smaller and smarter, IoT is pushing digital innovations well into the future. As of 2022, there are over 13 billion IoT devices globally. This will increase to over 25 billion devices by 2030.

IDC estimates that the global IoT market will rise to $1.1 trillion in 2023. While IoT technology is here to stay and grow, it’s interesting to observe the technologies that will drive its adoption. Let’s look at 5 enabling technologies that will drive the current boom in IoT adoption across business enterprises.

1. 5G & other connectivity technologies

Connectivity technologies like 5G, Wi-Fi 6, and LPWAN are making IoT solutions more feasible. The lack of wireless technologies and wireless data has been among the major challenges to wider IoT adoption. This is no longer the case. Enabled by 5G connections, LTE-based mobile networks provide higher bandwidth for IoT in outdoor settings (as compared to 4G LTE).

Similarly, in indoor environments, Wi-Fi (operating at the 6GHz frequency) has increased the overall bandwidth for IoT devices. Short for Low Power Wide Area Network, LPWAN is another emerging connectivity technology to connect IoT devices over larger areas. Besides, LPWAN is energy-efficient and cost-effective for connecting a large number of IoT devices.

2. Artificial Intelligence (AI)

As more technologies converge, business enterprises are unlocking new applications and use cases. Thanks to improved connectivity, IoT devices are generating more data, which is the “lifeblood” of AI technology. According to the latest statistics, the global market for AI in IoT will grow to $16.2 billion in the year 2024.

IoT-connected sensors are a valuable asset for AI and machine learning data pipelines. For instance, live data from IoT sensors in manufacturing facilities enable AI algorithms to determine when to service factory equipment. Similarly, with the convergence of AI and IoT sensor technology, retail brands can create a completely automated store for their customers.

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