The Next Semantic Layer Greenfield: Agriculture, Energy & Construction

Increasing AI adoption and digitalization have led to a surge in data generated across every sector. Industries that were data-shy in the past are now set to steer this massive transformation. There have been many advancements in how they capture, store and analyze data in the past few years. So much so that for sectors like agriculture, energy and construction, increasing investment in advanced analytics might be the key to success in years to come.

However, most of the major players in these industries are still grappling with growing data and the multitude of data sources when expanding the scope of their data strategies. DIY data stacks, where they bring different tools and technologies together to meet their analytics needs, can be both insufficient and costly.

To top it off, these sectors must comply with strict laws, leaving more scope for mismanagement or ignorance of risks. They need robust data architecture that can keep up with such requirements and deliver analytics at the speed of their business.

Let’s explore where these industries stand at this point, what lies ahead for them and how a semantic layer can add value to their existing stack.

Agriculture

Agriculture has been on the cusp of data-driven transformation without actually diving into it for a long time. Historically, it’s driven by intuition and traditional practices. However, increasing use of technology asks and allows for a more data-based approach. Data collected by IoT devices, satellites and digital farm equipment has opened new frontiers for growth and opportunities. This can range from using predictive analytics for better crop yield to maximizing available resources to maintain uninterrupted supply chains.

With all this information comes the need for accurate and holistic interpretation. Agriculture data can be hard to understand due to disparate and unconventional sources used for gathering it. For example, different farming machines depict data about crops differently. The problem lies in the heterogeneous nature of this data and a lack of proper APIs to bring it together. They also need to have a singular view to ensure interoperability. A semantic layer can bridge this gap.

The layer can integrate and interpret this whole set of information from different sources on a common platform to analyze it accurately. Be it weather data, sensor data or horticulture-related data, it can all be amalgamated and analyzed for actionable insights using a unified platform. With increasing compliance requirements for the usage of chemicals in farming, climate change, irrigation practices and water quality—among many other factors—a standardized common view will help bring all users on the same page while avoiding any discrepancies in how a number is interpreted.

Energy

Data convergence in the energy sector isn’t new. The industry has been swimming in gallons of household consumption data, information collected from power grids, equipment performance data and consumer data for many years. While it struggles to compile, integrate and analyze all this data for better customer services and improved resource planning, this is just one side of the coin.

On the other hand, proliferation of AI adoption is driving a burgeoning need for more AI data centers, which brings massive computational workloads. Goldman Sachs reports that data centers will use 8% of US power by 2030. This increasing demand has led to massive readjustments in how power is generated and distributed. The success of all these endeavors—initiated by the energy sector and those relying on it—depends on accurate data analytics.

However, that’s easier said than done. Data generated by the energy sector comes from a myriad of sources, none of them inter-connected in many instances. Companies not only need to collate all this information but also utilize it for making proactive decisions. A semantic layer over their diverse data sources might just be the solution they need. In this setup, the layer delivers consistent data across the business units, and that too, in a language commonly understood by all users. As a result, they can stitch different data types, origins, sizes and users together on a common platform.

Energy companies get access to the data to make accurate predictions about household energy scheduling, changing customer behaviors, system planning, and more. Energy fraud and thefts are another dimension where semantic layers can make a difference. According to several industry estimates—as reported in The Wall Street Journal—in the US alone, energy companies lose USD 6 billion per year due to energy theft and fraud encompassing intentional misrepresentation of power consumption and usage data. To prevent such threats, semantic layer architectures can prove much more accurate. They can detect slippages in data that are often overlooked in traditional setups.

Construction

A Deloitte Access report showed that 80% of the world’s construction companies are not yet completely data-driven or using data as robustly as they should. They analyze only one-third of the data they collect. The report also says that their yearly profit margins will grow by 50% if they invest in comprehensive analytics. All this underutilization is happening while these organizations collect a substantial amount of analytics-worthy data from various sources.

The construction industry has embraced digital technology to improve processes, meet compliances and reduce costs. BIM (building information modeling), project management systems, IoT sensors integrated into equipment and even blueprints offer a goldmine of information that can be accessed and analyzed to make better decisions. Though most construction companies have taken steps in this direction, there’s still some work to do.

There are many different cogs that must work in sync to ensure the success of massive construction projects. For example, equipment failure during a crucial phase of development can affect the whole project. BIMs, despite all the recent developments, have not fully assimilated interoperability or used data effectively within their ecosystem. Most of them support legacy formats and have minimal compatibility with IoT devices.

Digital twinning—creating virtual models of real-world physical buildings and projects by integrating data from BIMs, IoT devices, sensors, etc.—has emerged as a potential solution to counter the shortcomings of these systems. A semantic layer further enhances these models with rich context and enables broader use cases for them. It ensures interoperability with seamless integrations across data stacks, contextual data modeling and consistent views for every user, irrespective of their department or technical skills.

Once equipped with metadata-enriched semantics and cost-efficient scalable architecture, construction companies can predict outcomes more accurately, understand project loopholes, optimize the construction environment with better resource planning and do so much more for a brighter, better future.

Evidently, all these industries—and some others—are still in the process of adopting data analytics with all its bells and whistles. But we’re envisioning a future where a data-driven culture and increasing investments in analytics tech will turn the tide and bring more efficiency to their operations.

Looking Ahead

One major enunciation across these sectors is the focus on sustainability. Reducing waste, adopting environmentally friendly practices and investing in sustainable tech are the ways forward for them.

Advanced data analytics can help them accomplish their sustainability goals while keeping pace with external pressures to reduce carbon emissions, especially since each of the above-listed sectors plays a pivotal role in the ambitious smart city and grid projects. Choosing Kyvos, the industry-leading semantic layer, will ensure that.

Introducing Kyvos

Kyvos offers an advanced semantic layer that accelerates every AI and BI initiative. We deliver lightning-fast analytics at an infinite scale, with maximum savings.Kyvos offers a distributed architecture that can change the way enterprises analyze their data.

Almost every industry today is focused on leveraging AI systems. Their AI initiatives, in turn, depend on the accuracy of data used to train underlying models. Without a trusted source, these models are trained on generic or non-industry-specific datasets that may lead to hallucinations or biased outputs. This is another instance where Kyvos’ comprehensive offering can make a difference. Our semantic layer acts as a bridge between enterprise data and AI models. The platform offers a trustworthy, context-rich data source to train AI models, develop chatbots and personalize offers for customers across domains. Imagine AI models predicting demands to optimize energy costs and reduce emissions.

All these sectors also collect a huge volume of unstructured data—such as construction blueprints, customer reviews or climate reports in the form of PDFs, images, videos, etc.—which remains untapped in most cases. Kyvos offers rich metadata and semantics, as well as advanced RAG functionality, to store and process all this data, delivering comprehensive analytics.

Contact our experts to see how we help global organizations extract maximum value from their data.