How we built a full-fledged data insight solution in under 4 weeks
McKinsey estimates that global data creation will increase by 2.8 times from 2020 to 2025. More businesses are leveraging all kinds of data to optimize their decision-making processes.
Hence, it is more important now than ever to wield the ability to transform raw information into valuable insights. This means combining and analyzing data from various sources to make the best and most optimized business decisions.
However, this digital transformation process is not easy. The massive amount of data collected by analytics software can be overwhelming, and making sense of it is even more daunting.
To use big data effectively, the team engaged with it must first understand what the data means to the organization, and what it can bring to the table in terms of improving operations and efficiency. In this article, we want to share our experience of working with enterprise-grade industrial data and demonstrate how we built a full-fledged data solution in under 4 weeks.
If you’re interested in watching the video walkthrough of our journey, check out our video here:
We chose to use a low-code methodology
Low code is a software development methodology that allows developers to create applications by utilizing pre-built templates, drag-and-drop interfaces, and visual modeling tools. This method allows developers to create applications without having to write large amounts of code, which can help to speed up the development process. We wrote another blog article on why it’s strategic for digital transformation in the IIoT space, you can read about it here - Why Low Code For Internet of Things
Low-code software has many advantages when it comes to building data solutions. For example, it increases the development speed and adoption speed reduces solution development cost and allows for the solution to be taken over by your own employees. It's also easy to create a data-driven culture within your organization with low-code software.
Turning raw data into actionable insights
The specific solution we will discuss in this article is designed for an original equipment manufacturer (OEM) of instrumentation. The solution is focused on collecting data from analytical instrumentation used in a laboratory setting. The data is collected by an edge computing device, which is a type of computer that is designed to process data at the edge of a network, rather than in a centralized location.
Once the data is collected, the edge computing device processes it to generate insights. These insights can include information about the performance of the instrumentation, as well as any potential issues that may need to be addressed. The insights are then stored in a cloud-based database, which allows for easy access and analysis by authorized users.
To make this data easily accessible and actionable, a dashboard is used to display the insights in a visual format. This allows users to quickly identify trends and patterns in the data, and make data-driven decisions.
To streamline the development and deployment of the solution, a low-code solution manager software is used. Low-code solution manager software is a type of software that enables developers to create and deploy applications using pre-built templates, drag-and-drop interfaces, and visual modeling tools. This approach can help to speed up the development process and make it more accessible to non-technical staff.
The low-code solution manager software is used to build, deploy, and manage all the edge computing devices. This means that it can be used to configure the devices, set up communication between the devices and the cloud-based database, and manage the devices remotely.
In summary, this specific solution is designed for an instrumentation OEM to collect data from analytical instrumentation used in a lab environment. An edge computing device is used to collect, process, and generate insights, which are then stored in a cloud database, and displayed in a dashboard. A low-code solution manager software is used to build, deploy, and manage all the devices, making the solution more efficient, accessible, and actionable.
How we built the solution in 4 weeks
This project aims to provide an OEM of instrumentation with a solution that allows them to collect and analyze data from their analytical instrumentation in a laboratory setting. The project is designed to be completed in just four weeks, with specific tasks and milestones for each week.
In week one, the edge computing hardware is shipped to the OEM, who sets it up in their internal lab and connects it to one of their instruments. This step involves physically installing the edge computing device and configuring it to communicate with the instrumentation.
In week two, the data connectors are built, which are responsible for speaking to the instruments and collecting data from them. This step also involves examining the test data to understand its structure, and building the appropriate database schema and structure for storing it.
In week three, the solution is deployed to the OEM's customers, who use the solution to collect real data and build the dashboard. This step includes configuring the edge computing device to communicate with the cloud-based database, and testing the solution to ensure that it is working correctly.
In week four, the dashboard development is completed, generating insights for the customer and verifying the data results. This step includes creating visualizations and reports that allow the customer to see their lab conditions in real-time, and measure the performance of their lab. Once the dashboard is completed, the customer is given access to it, so they can start using the solution to make data-driven decisions.
In summary, the project is designed to be completed in four weeks and involves shipping the edge computing hardware to the OEM, building data connectors to collect data from instruments, deploying the solution to the OEM's customers, building and completing the dashboard, generating insights, and giving the customer access to the dashboard. The project aims to provide the customer with a solution that allows them to collect and analyze data from their analytical instrumentation in a laboratory setting and make data-driven decisions.
Conclusion
This is just a brief overview of how we were able to quickly build a data solution using low-code software. The process is efficient, cost-effective, and allows for easy adoption within an organization. It's a great way for instrumentation OEMs and other companies to quickly implement data solutions and gain valuable insights.
Data intelligence is the keyword for businesses that want to get the most out of their data insights. We wrote a blog article on the 3 types of intelligence you can derive from your data.
Read about it here - 3 Ways Data Intelligence will Transform your Business
