Why Build Reason Logic?

In today’s business landscape, it’s well understood that organizations cannot rely solely on data from their internal enterprise IT systems. Expanding the business database by incorporating new data sources is an essential first step. However, to truly unlock the potential of this data, specific subsequent actions must be taken. Let’s explore the critical steps that ensure your new data is both useful and actionable.

For any data to provide valuable insights, it must be relevant to the business context. Simply adding new data from external sources isn’t enough. It needs to be enriched with information that makes it relevant for the business.

For instance, consider data coming from sensors installed on machinery in the field. The sensor might measure parameters like temperature or pressure, but this raw data on its own is not enough. To derive actionable insights, you need to enrich the sensor data with additional details, such as the machine’s make, model, age, and the specific department or project it belongs to, the location where the machine is deployed etc. This enriched data enables better reporting, grouping, and more relevant analytics

Data collected without understanding the context under which it was gathered can be incomplete or misleading. Contextual information is crucial to interpreting data meaningfully.

Let’s say you’re tracking the performance of a machine; knowing the operational conditions—such as load, temperature, and humidity—at the time of the measurement can explain why the machine performed a certain way. This context applies not only to machines but also to other types of business data. For example, sales data may vary based on whether the sales occurred on a holiday, or vehicle performance data may differ depending on road conditions during a trip.

This additional context can be highly valuable, both for reasoning with the data and for training AI/ML models. It’s important to capture context as the data is being collected, as it is challenging to obtain accurate contextual information retrospectively.

Data often contains raw parameters that, when combined, can reveal deeper insights. By deriving new metrics from existing data, you unlock more value and create a richer understanding of your operations.

For example, just by collecting data on the ignition status (On/Off) and fuel levels of a machine, you can derive key metrics such as run hours, fuel consumption, fuel efficiency, and even fuel cost analysis. These derived metrics provide a clearer picture of operational performance and help identify inefficiencies or opportunities for optimization.

Aggregating data over specific time periods is critical to understanding the nature of operations and performance trends. These time-based metrics—often referred to as Key Performance Indicators (KPIs)—allow businesses to assess whether operations are improving or underperforming.

For example, aggregating machine performance data over the course of a day, week, or month can highlight trends in efficiency, downtime, or maintenance needs. These time-based insights are essential for management to evaluate business operations and make informed decisions.

While the above steps focus on the technical aspects of data integration, they do not require in-depth knowledge of the business domain. However, adding domain-specific semantics—insights provided by Subject Matter Experts (SMEs)—can significantly improve the relevance and applicability of the data.

For example, an SME in manufacturing might understand which machine performance metrics are most critical to production efficiency, or an SME in retail might know which sales trends are most indicative of customer behaviour. Incorporating this expert knowledge into your data can guide the interpretation of results and ensure that the insights derived are both practical and aligned with business priorities.

It is possible to collect and report data without incorporating the steps we’ve outlined above, but that would leave the full potential of the data untapped. By enriching raw data with relevant business information, adding operational context, deriving new parameters, performing time-based aggregation, and incorporating SME insights, you transform raw data into actionable intelligence.

Once the data is enriched and contextualized, it becomes not just a collection of numbers, but a powerful tool for driving informed decision-making and strategic initiatives. This is how data can truly become useful—by enabling reasoning that uncovers insights and guides business actions.

In short, building reason logic into your data integration process allows you to move from simply collecting data to truly harnessing its value for business growth.