Data Ingestion, Data Engineering, and the Potential of AI

Geoff Aucoin Tech

Data is an incredibly powerful tool; but for data to be meaningful, it has to be usable. Raw data on its own is without purpose, and therefore without value. It is only through data science: particularly, ingestion and engineering, that we can glean from our data hindsights, insights and foresights. Data ingestion and data engineering are all about using large sets of information to answer valuable questions. Simply put, this is what makes data usable. Through these processes, specific data is pulled in at regular intervals from predefined sources (ingested), cleaned, formatted and transformed (engineered) into accessible information. From here, the possibilities open up. Run analytics and correlations. Seek out patterns and trends. Make predictions. Below are three examples of ways in which Lixar has helped organizations embrace the power of data and leverage the predictive power of AI in forward-thinking ways.

Use Case 1: Operational Efficiency in Airport Traffic Prediction

Lixar works with airports to address a specific challenge: predicting when passengers will arrive to ensure adequate staffing is present at security, check in, gates, and retail outlets. Sudden spikes in passenger arrival result in longer security lines, which lead to a significant decrease in revenue from an airport’s post-security retail stores. In one case, Lixar took historical data from three years worth of spreadsheets, along with future flight and staff schedules, and developed an AI-powered solution that predicts passenger arrival profiles. Built on our Hero Path architecture, the solution provides passenger arrival predictions that are measurably more accurate, and allows the airport to better anticipate spikes throughout the day. Airports are able to make better decisions about how to staff, when to open additional gates, and how to arrange for overflow between terminals. As a result, they are better equipped to handle passenger traffic, customers and staff have experienced increased satisfaction,  and airports realize increased sales at post-security retail stores.

Use Case 2: Connected Car Experience

Lixar built the first aftermarket connected car on-board diagnostics (OBD) device. The device generated millions of data points every day, which meant real-time processing and reporting of data from many sources, as well as the ability to pipe specific data to individual external stakeholders. Lixar built a scalable data lake platform to ingest and engineer mass amounts of data, and to perform real-time functionality and machine learning, all of which was required to make this product possible. The success of this resulted in a cloud platform that is currently active in tens of thousands of vehicles, managing world-class calibre amounts of data. The next logical evolution of this kind of solution would be to use the data pipelines set up by Lixar to manage fleets of autonomous vehicles across the world.

Use Case 3: AI-Enabled Adaptive Cabins for Auto and Air

Lixar works with Automotive and Aerospace clients to transform the cabin experience and offer a more personalized experience to their customers. Using data with a contributing factor of time, Lixar has deployed algorithms that is capable of adjusting cabin settings accordingly, creating a truly personalized experience for the most discerning customers.

In all three use cases, we see that data ingestion and engineering are not only key to making data usable in a meaningful way, but also imperative to the transformation of industries for the future.

To learn more about what data engineering can do for you, please contact us at