While we continuously develop Irys and release significant improvements each month, it’s crucial to recognize our current achievements. Our core product is now in a stable state, a significant shift from the beta phase over the last five months.

Version 1.0 Highlights

In Version 1.0, we've launched our core product, featuring a robust backend data service, end-to-end security, and comprehensive navigation support within Irys. For detailed release notes, visit our documentation.

Looking Ahead: What's Next? With many infrastructure challenges resolved, we're excited to focus on enhancing the experience for data teams. Our next release will introduce three major updates:

1. Multi-site Live Dashboards

We're expanding our PI System connector to support multiple PI servers. Whether connecting multiple servers from a single connector within the same network or multiple connectors across domains, this reduces the complexity of building multi-site live dashboards dramatically. No more extensive data engineering or PI-to-PI connections – setting up a multi-site dashboard has never been easier.

2. Eliminating Data Requests

A new data download feature will allow users to access and download data directly from their browser. This aims to eliminate tedious data requests, enabling your team to focus on high-value tasks.

3. Enhanced Context with Event Data

We'll support events from Event Frames from PI AF, allowing you to view event data alongside live data for better context and understanding. Our goal is to provide a comprehensive solution where all data types are integrated for improved decision-making.

Get Involved Do you manage real-time data at your company? Have concerns that keep you up at night? Click the schedule button – we want to hear from you!

For those who follow our newsletter sharing major product updates (subscribe), this month we mentioned the implementation of a high-performance data architecture, and we claim our new architecture accomplishes:

• Extremely Fast – The low overhead of websockets, push style data subscriptions, efficient data management, and data caching make for a great user experience with very fast data loading and updating.
• Scalable – The efficient data management significantly reduces the load on your data sources and allows for usage to scale without impacts to performance.
• Secure - On-premises connectors have a one-way secure outbound connection to Irys Hub with an OAuth 2.0 client-client credential flow.

This blog post will detail the technical decisions we made along the way to get to the current data architecture, but before I dive into the technical details, it is worthwhile to highlight why we are obsessed with performance.

The user experience deteriorates with prolonged loading times for websites or dashboards. Seconds, even milliseconds, matter significantly in retaining user engagement. Most people won’t continue using a mobile site that takes more than 3 seconds to load, and the loading time in ideal scenarios needs to be significantly better if you want to deliver vital information to a distributed workforce who are working in areas with no WIFI and poor LTE connections. This scenario is where existing solution on the market completely fail.

To meet the need for a high performance mobile real-time visualization platform, we have gone through many iterations of our data architecture.

In the earliest days of Irys, for our proof-of-concept in October 2023, we took the quickest path to get data from a data source, direct polling from the UI of the data source. This is a quick and easy way to get data into a dashboard and is commonly used by other products on the market. This approach uses a timer to query the data source for updates. For example, a product which uses this approach is Grafana, where users can select how often their data is updated, between 5 seconds and one day, and all data is refreshed at the same time and the same interval.

There are many challenges with the direct polling approach (and why we quickly moved away from this approach):

1. Optimization is left to the dashboard creator, which is time consuming and will never result in an optimal result as comprehensive data management is impossible and impractical to do within a dashboard.
2. Every instance of a dashboard directly queries the source at regular time intervals causing higher query loads on the data source.
3. Poor loading times, many round trips are made between the dashboard and the data source to load all the data, for example why do 100 different queries need to be made for 100 display widgets all from the same data source?
4. Poor refresh rates, the user is left to set the refresh rate which will either be too fast or too slow. If the dashboard is refreshing every hour, there is a lot of data that can change within that timeframe, and alternatively if a data point only updates once a day, there is no need to continuously query this point.

In our next iteration of our data architecture around December 2023, we added a middle layer we call Irys Hub which separates the queries from the UI to the data source. The main benefit from this separation is that it allowed us to add in-memory caching to the architecture so the most recent values from the data source are stored in the cache and quicker to return to the UI. This appproach significantly reduces the query loads on the data source when the same point is requested by multiple dashboards.

Up to this point, all the queries in our architecture had been made using REST calls, you pass a query to the data source and get the results back. This approach has significant overhead with each query and requires constant requests to check for updated values.

In the next iteration of our data architecture around February 2024, we added secure websockets, which create a connection and allow for updated data to be pushed to the requestor. Our data architecture changed to implement the following:

1. Websockets between the UI and Irys Hub are used to push data updates to the UI.
2. Caching still exists for new requests of the same data points.
3. Websockets between Irys Hub and the data source are used to push updates from the data source to Hub.
4. Data points are registered by the UI in Irys Hub in an observer pattern to more efficiently manage the data that needs to be updated.

This last iteration was a significant improvement, however the loading times were not optimized, leading to a poor user experience. The reason for these slow loading times is that the UI was waiting for all data to return before updating, thus further optimization of the data management was needed.

In our most recent iteration of our data architecture released at the end of March 2024, we optimized the data management to asynchronously update different types of data. In other words, there are no dependencies between the different data to rendering in the UI. Thus, the configuration data and meta data from Irys Frame are instantly returned and used to populate displays, then whenever the individual data values or trend data series are returned, they will populate the dashboard. This resulted in a much faster loading experience, and combined with websockets which have very low overhead compared to REST calls, this makes for an incredibly fast loading time.

In addition to the data management optimization, we also added end-to-end security to this release, which uses an OAuth 2.0 client-client credential flow.

We are very happy with the performance of the latest release and the data architecture speed is obvious when using Irys. With this latest release we have addressed the four main challenges from the original direct polling approach:

1. Top data performance out-of-the-box so users can just focus on building their dashboards.
2. Optimized loading on data sources, each data point is only queried once for updated data, regardless of how many requests come in from clients and when they come in, this will allow for much larger systems and eliminate complex data architectures and work arounds for your IT teams.
3. Optimized loading times making data instantly available, thus when you are viewing dashboards in Irys you don’t lose your train of thought because you are wasting precious time waiting for data to load.
4. Data updated in the UI as soon as it is available, so you never have to click refresh, or ask someone else for the latest data.

While we are very happy with the current iteration of our data architecture, there are still further improvements and optimizations that we have planned, for example:

1. Additional data sources - Our current implementation only supports a single data source, thus as we expand our data architecture to cover many additional data sources, we will re-evaluate our current data management capabilities and optimizations to continue to deliver the same high performance.
2. Source discovery and meta data management – For a better user experience, we plan on providing source discovery and meta data management so users can know what is in their source databases without having to leave Irys.
3. Scaled caching – Our current data architecture uses in-memory caching, which works great in its current implementation, however for very large systems we will likely move to an in-memory data store (like Redis).
4. Trend caching and optimization – As we build out the capabilities of trending, we will also build out the data management capabilities of trend data and optimize around caching and updating trend data.
5. API Access – There is a lot of value in accessing the data sources and data modeling made available through Irys for use cases outside of data visualization, thus we plan on building out the capabilities to make this data available to developers for data analysis, custom applications, etc.

A real-time wind energy dashboard enables both executives and engineers to make data-driven decisions. This blog post explores some key metrics for building and understanding such a dashboard, focusing on the operation and maintenance phase of wind energy portfolio management.

Executive Summary

At the heart of wind energy portfolio management is the objective to maximize annual energy production (AEP) while minimizing maintenance costs. To maximize AEP, you need to diagnose areas where there is underperformance either due to unplanned downtime, unscheduled curtailment, or lower than expected energy production. The core metrics revolve around the expected vs. actual energy production, with the power curve playing a pivotal role in determining expected outputs.

Understanding the Power Curve

A power curve represents the relationship between wind speed and a turbine's electrical output. If you have seen a plot of actual wind vs power measurements, there are significant variations in the power output for a given windspeed depending on many factors, such as the pitch of the blades, the air turbulence and density, and the accuracy of the windspeed measurement. Each model of wind turbine will have its own power curve, which can be used as a baseline for calculating expected power.

Calculating Expected vs. Actual Energy Production

By integrating the power curve with real-time wind speed data, we can estimate the expected energy production for any given period. This estimation, when compared against actual production data, provides a clear performance indicator. However, in scenarios where power curve data is unavailable, alternative metrics such as the Capacity Factor (CF) and Energy Yield can offer insights into the wind farm's efficiency.

Performance Ratio

The Performance Ratio (PR) distills the effectiveness of wind conversion, using the expected energy calculated based on the power curve. It’s a critical measure of how well your installations perform under real-world conditions. A PR close to 100% signifies that your systems are operating near their expected efficiency.

PR = (Actual energy output / Theoretical energy output) × 100%

Capacity Factor

The Capacity Factor (CF) measure of how much energy is generated compared to the maximum that could be generated at continuous full power operation during the same period.

CF = (Actual energy generated in a period / Maximum possible energy if operated at full capacity throughout the period) × 100%

Detailed Metrics for Engineers

Engineers drill down into the specifics of each site to optimize performance. Key metrics include:

Power Curve Performance
As mentioned above, the power curve represents the relationship between wind speed and the turbine's electrical output. Engineers analyze deviations from the expected power curve for specific wind turbines to identify performance issues such as suboptimal blade angles or mechanical faults.

Availability Factor
The availability factor indicates the percentage of time a turbine is available for operation, considering downtime due to maintenance or failures. It's calculated by dividing the actual operational hours by the total hours in the period being considered.

Availability Factor = (Actual Operational Hours / Total Hours in Period) × 100%

Building Your Dashboard

A successful dashboard integrates data from various sources, including wind turbine generators, meteorological stations, and substation measurements. For executives, summarizing this data into key metrics like the performance ratio or capacity factor provides a quick health check of the wind energy portfolio. Engineers, meanwhile, require access to detailed operational data, with the ability to zoom in on specific sites for troubleshooting and optimization.

Leveraging modern data visualization tools like Irys can simplify this process, designed to offer easily accessible portfolio level visibility and equilly important capabilities to zoom in for site troubleshooting and optimization, ensuring that both executives and engineers have the insights they need to drive success.

A well-crafted dashboard is more than just a tool for monitoring; it's an essential component of strategic management and technical optimization. By balancing the needs of executive overview and engineering depth, your dashboard can provide a comprehensive picture of wind energy performance, ensuring your portfolio not only meets but exceeds its potential.

Introduction

In the evolving landscape of renewable energy, utility-scale solar power plays a pivotal role. As executives and engineers alike seek to maximize efficiency and output, the need for comprehensive, real-time monitoring becomes essential. The foundation for this is to have the right data measurements collected and a well-designed dashboard to enable insights ranging from high-level performance overviews to troubleshooting with granular operational data. Here’s three levels of solar metrics used to monitor and maximize the efficiency and output of your solar portfolio.

Executive Summary: Portfolio Level Metrics

Capacity Utilization Factor (CUF)

CUF offers a snapshot of your portfolio’s efficiency, comparing actual output to theoretical maximum output. Capacity is typically measured in megawatts (MW) or gigawatts (GW), and is the potential output of your solar assets. A higher CUF indicates that your solar assets are closer to their peak performance, signaling operational excellence and higher return on investment (ROI).

CUF = (Actual energy generated in a period / Maximum possible energy if operated at full capacity throughout the period) × 100%

Performance Ratio (PR)

The Performance Ratio distills the effectiveness of solar conversion, adjusting for environmental variables. It’s a critical measure of how well your installations perform under real-world conditions. A PR close to 100% signifies that your systems are operating near their expected efficiency.

PR = (Actual energy output / Theoretical energy output under ideal conditions) × 100%

Energy Yield

Similar to Performance Ratio, Energy Yield measures the electricity generated per unit of installed capacity, providing insight into the project's efficiency. Energy Yield is typically measured in kWh/kWp (kilowatt-hours per kilowatt-peak) for a specific period.

Energy Yield = (Total Electricity Generated in a Period / Installed Capacity)

Levelized Cost of Energy (LCOE)

For financial health, LCOE is indispensable. It calculates the cost per kilowatt-hour (kWh) of electricity produced, factoring in installation, maintenance, and operational expenses. In competitive markets, a lower LCOE can be the edge your portfolio needs to outperform.

LCOE = (Total costs over the system's lifetime / Total energy produced over the system's lifetime)

For Engineers: Detailed Operational Metrics

Irradiance and Insolation

Understanding the solar resource, through Irradiance and Insolation data, is crucial for diagnosing performance issues. These metrics help engineers assess whether a system’s output aligns with available sunlight, pinpointing potential areas for optimization.

Irradiance is measured in watts per square meter (W/m²) and can be measured in real-time using pyranometers installed at the site.

Insolation, or solar exposure, is usually measured in kilowatt-hours per square meter (kWh/m²) per day and calculated based on irradiance data collected over time.

AC/DC Capacity Ratio

The AC/DC Capacity Ratio or Inverter Load Ratio (ILR) highlights the balance between the solar panels' DC output and the inverter's AC conversion capacity. An optimized ratio ensures maximum energy transfer without overloading system components.

The AC/DC Capacity Ratio is calculated by dividing the inverter's maximum AC output capacity by the solar PV array's total DC capacity.

AC/DC Capacity Ratio = Inverter AC Capacity / PV Array DC Capacity

Availability and Downtime

Availability metrics reveal the operational status of solar assets, accounting for maintenance, grid issues, or equipment failures. High availability rates indicate reliable performance, whereas frequent downtimes suggest areas needing attention.

Availability is calculated by taking the ratio of the actual operating time to the potential operating time, often expressed as a percentage.

Availability = (Actual Operating Time / Potential Operating Time) × 100%

Downtime can be derived by subtracting the system’s actual operating time from the total potential operating time within a given period.

Degradation Rate

Solar panels lose efficiency over time; the Degradation Rate measures this decline. Understanding degradation at the site level allows engineers to forecast long-term performance and plan maintenance or replacements proactively.

The Degradation Rate for solar panels is typically determined through annual performance tests, comparing the initial rated capacity to the measured capacity over time.

Degradation Rate = [(Initial Capacity - Measured Capacity) / Initial Capacity] / Years of Operation × 100%

Performance Analytics

Drilling down, engineers monitor Voltage Levels, Current Flows, and Temperature data to ensure optimal operation. Variations can indicate issues such as shading, equipment malfunctions, or suboptimal configurations.

Voltage Levels and Current Flows are directly measured through the solar PV system’s inverters and electrical equipment. Analyzing these measurements helps in identifying mismatches or inefficiencies.

Temperature of solar panels is monitored using temperature sensors. Since solar panel efficiency can decrease as temperature increases, it’s important to track this parameter closely.

The impact of temperature on panel performance can be assessed using the temperature coefficient, which is specified by the manufacturer.

Building Your Dashboard

A successful dashboard integrates data from various sources, including inverters, meteorological stations, and monitoring systems. For executives, summarizing this data into key metrics like CUF, PR, and LCOE provides a quick health check of the solar portfolio. Engineers, meanwhile, require access to detailed operational data, with the ability to zoom in on specific sites for troubleshooting and optimization.

Leveraging modern data visualization tools like Irys can simplify this process, designed to offer easily accessible portfolio level visibility and equilly important capabilities to zoom in for site troubleshooting and optimization, ensuring that both executives and engineers have the insights they need to drive success.

A well-crafted dashboard is more than just a tool for monitoring; it's an essential component of strategic management and technical optimization. By balancing the needs of executive overview and engineering depth, your dashboard can provide a comprehensive picture of solar asset performance, ensuring your portfolio not only meets but exceeds its potential.

I recently delved into the concept of the data flywheel, a notion that resonates deeply with our mission at Irys, albeit with a twist distinct from the practices of major online entities.

Imagine a flywheel, a wheel designed to store energy, becoming increasingly efficient as its momentum grows. This imagery perfectly illustrates the data flywheel strategy in data management. It's about creating a self-reinforcing cycle: data enhances products or services, which in turn draws more user engagement. These users then generate additional data, propelling the cycle forward.

This principle is the bedrock upon which large internet corporations stand. The more users engage with their offerings, the more data they produce, making the platforms even more captivating and encouraging further user interaction.

While the implications of this cycle can be double-edged, particularly in the context of the endless browsing encouraged by big tech, I believe that, under the right circumstances, it can yield positive outcomes.

At Irys, we adapt this concept to the realm of industrial data, aiming for increased involvement across all levels of a company's core operations.

The foundation of Irys is the principle that data should be universally accessible within an organization, distilled into three main tenets:

1. Eliminate access barriers to data sources; Irys simplifies the process by managing connections to various data sources, ensuring users don't have to concern themselves with the origins of the data.
2. Remove obstacles to data comprehension; by offering a context framework to model data, Irys makes it straightforward for all users to understand their data.
3. Ensure data access throughout the organization; Irys is crafted as a user-friendly, responsive website that showcases real-time data in well-organized layouts, enabling every company member to effortlessly view a live snapshot of the company’s operations from any device.

So, how does this translate into a data flywheel for Irys?

By connecting and modeling a data source with Irys, you establish a well-contextualized data foundation. This enables the swift addition of new visualizations as your data insight deepens. Irys' commitment to frictionless data accessibility across your organization means that when various departments gain access to high-quality operational data, they become more engaged with the business's day-to-day functions. They start contributing requests and feedback, thus initiating a positive feedback loop. Sharing more data company-wide not only boosts engagement but also enhances data utilization, which in turn, fosters further involvement and engagement.

Inheritance in PI Asset Framework (AF)

In PI Asset Framework (AF), the concept of templates is central to asset modeling. A template acts as a rigidly defined data structure, encompassing a configuration of attributes. When an instance of a template is created within an element, it inherits all the template's attributes. This results in a strong coupling between the element and its template. However, this approach has limitations when flexibility is required.

One significant challenge arises when attempting to use a single template for multiple, similar asset types. This scenario often leads to oversized templates, burdened with all possible attributes for every asset type it represents. While a workaround exists in the form of attribute exclusion for non-applicable attributes, this process can be cumbersome and inefficient. Moreover, the strict inheritance model means that any change to an element requires modification at the template level, impacting all instances derived from that template.

AF addresses this complexity by implementing inheritance within templates themselves. Users can create a base template to establish common attributes, and then additional templates to define specific differences. However, managing these layered templates can be complex and daunting, especially in large-scale implementations.

Composition in Irys Frame

In contrast, Frame adopts a composition-based approach to asset modeling. This method offers a significant departure from the tightly coupled nature of AF's template inheritance model. In Frame, instances of an asset (which we call nodes) are constructed by combining groups of attributes from templates (which we call tags). When a node is tagged, the attributes from the tag are copied into the node and a relationship to the tag is created. A node can have multiple tags in order to compose the right set of attributes for the asset.

The key advantage here is the loose coupling of tags from the nodes. After a node has been tagged, users can freely modify the attributes without affecting the tag, allowing for greater flexibility for unique assets or one-off scenarios, and users can decide how modifications to a tag are propagated (or not) to related nodes. Beyond copying attributes, the relationship to the tag is very useful as is can be used for templates, filtering, and other advanced functionality.

A notable similarity between AF and Frame is in template (or tag) updates. In AF, updating a template automatically reflects across all corresponding elements. In Frame, users will be given control over how updates are applied when updating a tag, such as choosing whether to update all related nodes or when removing an attribute from a tag users may decide to remove or not remove the attribute from the related nodes.

Conclusion: Flexibility and Control in Asset Modeling

In conclusion, while PI AF and Irys Frame share functional similarities in asset modeling, the methodologies they employ differ significantly. Frame, with its composition-based approach, offers a more flexible and user-friendly system, particularly advantageous for managing large and complex systems. This flexibility, combined with the user's ability to control updates and modifications, positions Frame as a potentially more effective solution in dynamic and evolving operational environments.

Asset Framework (AF) previously from OSIsoft and now part of the AVEVA product portfolio is a key component of the PI System, which is a suite of software products designed for real-time data management, often used in industries for monitoring and controlling equipment and processes. AF serves as a powerful tool for organizing, analyzing, and visualizing the vast amounts of data collected by the PI System. Here are some of its core features and capabilities:

Asset-centric Data Model - AF allows users to create a structured, hierarchical representation of their physical equipment and systems. This structure makes it easier to manage and analyze data related to specific assets or groups of assets.

Contextualization of Data - By associating real-time and historical data with specific assets, AF helps users understand the data in the context of their equipment or process. This is crucial for meaningful analysis and decision-making.

Templates and Reusability - AF allows the creation of templates for common asset types, which can be reused across similar assets. This feature saves time and ensures consistency in data modeling.

Advanced Analytics and Calculations - AF supports the creation of sophisticated analytics and calculations. Users can define equations and rules that automatically process data to generate insights or trigger alerts.

Event Frames - AF can be used to capture and analyze events, such as equipment start-ups or shutdowns. This helps in understanding the performance and behavior of assets during specific periods.

Asset Framework can be regarded as the highlight of the OSIsoft product portfolio, and its use led to a decade of innovation. At Irys, we are a big fan of AF, which led us to incorporate the core concepts of AF when building Irys Frame.

Read more about how we are taking the concepts of AF to the next level in the post Why Build Irys

We're developing Irys to build the tool we wish we had while building highly customized solutions for industrial data visualization. Our team has extensive experience with the PI System and PI Asset Framework by AVEVA, and while the PI Asset Framework is an amazing tool, we believe we can significantly improve upon the design and use this newly designed data framework to provide extremely powerful tools to everyday users in the industrial data space.

Irys is our solution to solving the challenges that any large company will run into while trying to build modern software for real-time industrial data applications. And the first challenge we are addressing is focused on building modern, responsive, and consistent visualizations to display real-time data to everyone in an enterprise.

Understanding Asset Framework

Asset Framework is a versatile and powerful tool for organizing and analyzing real-time operational data, providing a structured, contextual view that enhances decision-making and operational intelligence in industrial environments. At a high level, it provides:

1. An asset-centric data model for otherwise difficult to manage data within the PI System.
2. A platform for value added services such as Advanced Analytics and Event tracking.
3. An API for connecting third party tools and services.

Asset Framework can be regarded as the highlight of the OSIsoft product portfolio, and its use led to a decade of innovation. But as good as AF was and is, it’s starting to show its age and hasn’t grown alongside the changing needs of our community.

The push to expand on the idea of AF led us to build Irys Frame, taking many of the best concepts of AF, but building it in a way that allows for future growth and innovation. We are building innovative concepts into the product development, and more importantly we plan on enabling the community to be innovative and creative with how they build with Irys. For starters, here are some of the changes that Irys Frame will bring in comparison to PI Asset Framework:

1. A cloud based solution that is operating system agnostic and accessed through a REST API.
2. Designed to easily connect to virtually any data source. Where AF has this ability (custom data references), it is technically difficult and only used by the most advanced users.
3. Moving away from templates with inheritance, to a composition of interfaces, which allows for more flexible modeling of a dynamic and varied asset space that is typical for industrial use-cases.

The Vision for Irys

In embracing the strengths of Asset Framework, we've designed Irys to be the next step forward in industrial data management. Our vision for Irys is to offer a transformative tool that simplifies, accelerates, and enhances the way businesses interact with real-time data. Here's what Irys aims to achieve:

1. Introduce a flexible and clear data modeling system that can adapt to complex and evolving scenarios.
2. Create a versatile platform that integrates with any data source, that provides powerful tools for automation, customization and extending its capabilities.
3. Create user-friendly visualization tools for immediate, enterprise-wide data access and plan to roll out more advanced services progressively.

Embarking on the journey of starting a company is a thrilling yet challenging endeavor. It requires more than just business acumen and resources; it demands a deep connection to your interests and a commitment to lifelong learning. But what does it mean to align your entrepreneurial journey with your passions, and how does this influence the path to success?

The Power of Passion in Entrepreneurship

Starting a company around something you are genuinely interested in has a profound impact on your journey as an entrepreneur. When you build a business based on your passions, it infuses your work with enthusiasm and creativity. This passion becomes the driving force that propels you through challenges and setbacks. It keeps you motivated during tough times and makes the successes even more rewarding.

When your work aligns with your interests, it doesn't feel like a chore. You're more willing to put in the extra hours, delve deeper into your industry, and become a true expert in your field. This depth of knowledge and dedication can set your business apart in a competitive market.

Embracing the Learning Curve

Entrepreneurship is a constant learning experience. From the basics of business operations to understanding market needs and consumer behavior, there's always something new to learn. By following your interests, the learning process becomes more enjoyable and engaging. You're more likely to stay abreast of the latest trends, technologies, and best practices in your chosen field, giving your business a competitive edge.

The Importance of Finding Your Niche

In the journey of entrepreneurship, finding the right path often means identifying and focusing on a niche that excites you. This niche should be at the intersection of what you love, what you're good at, and what the market needs. When you find this sweet spot, your business is not only viable but also a true reflection of your personal interests and strengths.

The Role of Adaptability and Openness to Change

The business world is dynamic, and so are your interests and passions. A key aspect of successful entrepreneurship is being adaptable and open to evolving your business as you grow and learn. This might mean pivoting your business model, exploring new markets, or integrating new technologies. Staying true to your interests doesn't mean being rigid; it means being open to where your interests lead you, even if it's in a direction you hadn't initially anticipated.

Building a Network of Like-Minded Individuals

Collaborating and networking with people who share your passions can be incredibly rewarding. These connections provide support, inspiration, and opportunities for collaboration. They can open doors to new markets, partnerships, and resources that can help grow your business. Building a network of like-minded individuals can also be a source of valuable feedback and insights that can help you refine your business model and strategies.

Conclusion: Crafting Your Entrepreneurial Journey

In conclusion, starting a company is more than just a business venture; it's a personal journey that should be aligned with your interests and passions. This alignment not only makes the entrepreneurial process more enjoyable but also drives innovation, commitment, and resilience. By embracing constant learning, finding your niche, and staying adaptable, you can chart a path that not only leads to business success but also personal fulfillment. Remember, the most successful businesses are often those that are built not just on market opportunities but on the passions of their founders.

In the ever-evolving world of software development, businesses are constantly faced with a critical decision: should they build a custom solution from scratch or buy an off-the-shelf product? This decision is pivotal, as it can significantly impact the efficiency, scalability, and ultimately the success of the organization, and there can be many influencing factors such as the talent pool within the company and how critical the software is to the core business. At Irys, we understand this challenge and are tailoring our product to offer a unique solution that bridges the gap between building and buying.

Understanding the Build vs. Buy Dilemma

Building a custom software means developing a solution from the ground up. It offers full control over the features and allows for a perfect fit to specific business needs. However, this route is often resource-intensive, requiring significant time, expertise, and investment.

Buying an off-the-shelf solution, on the other hand, offers the advantage of immediate deployment, often with lower upfront costs. Yet, these solutions may lack the flexibility to meet all the specific needs of a business, leading to potential compromises in functionality and performance.

Building custom software on infrastructure In recent years, a hybrid solution has emerged as a popular alternative, blending the benefits of both building and buying. This approach involves using a robust, pre-built infrastructure as a foundation upon which companies can develop their custom solutions. The hybrid model offers a middle ground, where businesses can leverage the reliability and scalability of an established platform while still enjoying the flexibility to tailor specific features and functionalities to their unique needs.

The Irys Advantage: Customization with Ease

At Irys, we believe you shouldn't have to compromise between the tailor-made capabilities of a built solution and the convenience of a bought one. We're developing a distinct offering in the industrial data visualization space, providing the necessary framework and infrastructure for an extendable solution. We're collaborating with early adopter customers to enhance these capabilities and position ourselves uniquely in the market. Our approach emphasizes extensive customization through API usage, custom visualizations, and tailored workflows. Irys Frame, our innovative context framework, will support all these features.

Irys Frame: A Revolutionary Context Framework

Irys Frame is a transformative element of our platform. It enables scalable visualizations far surpassing the capabilities of basic drag-and-drop solutions. By integrating various data types from one or multiple sources, Irys Frame facilitates valuable insights through layered data context. Understanding your data within its context is crucial, and Irys makes it possible to reflect your business's complexities and nuances effectively.

Conclusion: The Best of Both Worlds

Opting for Irys means more than just acquiring a software product; it's an investment in a platform that grows with your business. You gain the reliability and speed of a pre-built solution along with the customization and specificity of a built system. Whether dealing with multiple data sources, seeking innovative data visualizations, aiming to streamline operations, or working within a complex context, Irys delivers a tailored solution. This saves the time and resources usually associated with developing custom software.

In today's business environment, where adaptability and efficiency are paramount, Irys emerges as a solution adeptly addressing the build vs. buy challenge.