What is your primary use case for Palantir Foundry?

I use Palantir Foundry to ingest data and create visualizations for decisions.

Palantir Foundry serves as our data platform for the company, which means we have numerous use cases and business cases that cross all the different business groups, subsidiaries of the company, and also different support functions and business functions of the corporate. We have more than 200 use cases in the corporate because Forvia is a very big company. The main use case is to enable the data value and data product for our corporate and for our business. The main purpose of the data platform is to have a good return on investment based on IT digital dependencies. From a business point of view, I will give you a good example of purchasing. For the purchasing side, purchasing has two types: direct purchasing and indirect purchasing. Especially for the direct purchasing part, previously, we could not know that all the purchasing data management was quite siloed. With Palantir Foundry, we break the data silo to make all the different data which comes from the purchasing department globally, which have acceleration with the data sourcing assistant and AI sourcing assistant, to help our business accelerate their purchasing business transformation and to achieve excellence in terms of purchasing goods price. This helps us, at the same time, to speed up for the purpose of time saving, and it helps our business to accelerate all the price transformation strategy with our suppliers. That is a good benefit. Not only for the purchasing part, it is also for the processing side in the operation and for the industrial operation, because Forvia is a manufacturing company. We have many data use cases in the plant. Globally, we have 500 plants and factories globally, which have many critical operations on the factory plant side. For example, the predictive maintenance with the data coming from the shop floor from the plant side helps us to have a good level of understanding of the different machine statuses of the plant.

I serve as an integrator with Palantir Foundry, using the application on behalf of my company. In my usual use cases of Palantir Foundry, there are two to three categories, with one being data governance. For that in Palantir, I have administrative purposes and two tools through which I keep control over who can access different data. For data integration, I use a data connector to bring sources from other places, such as Excels or different sources to Palantir Foundry through data integration. I also perform code conversion in the Code Repository, migrating SQL formats to PySpark. We built around 120 datasets, using them to create schedules in Data Lineage by adding the pipeline sequence. Data Lineage helps us schedule and conduct health checks in Palantir Foundry based on data freshness and building statuses. Additionally, I validate data using Code Workbook, where I can import sources for transformation using SQL, Python, or R code based on requirement, though I mostly deal with SQL codes. In terms of application development, I work with Pipeline, Workshop, and Ontology Manager. I prepare the Workshop front end using Ontology and Pipeline to build a back-end application structure, managing what columns will retrieve data from the back end. Ontology Manager intermediates between Pipeline and Workshop, allowing us to design user-relevant widgets for the front end quickly, within an hour if needed, working mostly in a no-code environment.

My main use case for Palantir Foundry is to solve business problems, such as in healthcare. I also worked on a project for a law firm where thousands of PDFs were coming in, and we needed to check the Excel and PDF data to match everything correctly and ensure all data was accurate. I would outline the law firm project in detail. There were thousands of PDFs and Excel sheets which needed to be checked against the PDF data and the Excel data to verify if the data was correct. We used Pipeline Builder to extract the PDF and then run a query to find the extracted data from the PDF and check it against the Excel sheet. This created significant time savings for people and was also faster than manual processes. We are also running our own company's operations inside Palantir Foundry, including leaves, opportunities, leads, accounts, salary, and working times, with everything documented there. That represents another use case.

My main use case for Palantir Foundry was that it served as our go-to platform for business analytics and AI workloads. We mostly used it for analytics and as an operational platform, specifically using Palantir Foundry's Works feature. We used Palantir Foundry for operational needs and analytics, and there was a team using Palantir Foundry's built reports and dashboards to input data and then execute an approval process using Palantir Foundry's services. The reports were created on the platform following this workflow. Our team used Palantir Foundry extensively in conjunction with Azure-based services such as Databricks, Azure Data Lake storage, and Azure Data Factory. There was excellent integration between these two services, which really helped automate many of the manual tasks that the downstream teams and business teams were handling.

My main use case for Palantir Foundry is for building solutions predominantly for different enterprises, mainly for healthcare and manufacturing. For healthcare, I have built a clinical trial management solution mainly helping the enterprise and the R&D sector. Based on different diseases which patients undergo, I analyze what permutations and combinations are possible. The second part is to monitor the overall lifecycle of the trial and drug trial which happens at an R&D center. Palantir Foundry's ecosystem, which includes Ontology Manager, data pipeline, and OSDK, allows us to integrate with different other devices as needed. For other industries, specifically manufacturing, I focus on managing the overall supply chain and analyzing overall operations. I would say figuring out the overall time it takes for a particular task to complete rather than optimizing. For example, if there is a machine malfunction due to any reasons and an issue has been raised, I determine how much time a technical person takes, what challenges they face, and eventually if there is a knowledge gap, I provide them with a knowledge base, integrate with real-time knowledge help, and calculate the average time a person takes to complete a task. Then I monitor whether over a period of time any optimization can be done. In order to build this ecosystem, I use Palantir Foundry.

My main use case for Palantir Foundry is creating data engineering pipelines including ingestion of data, transformation of data to the final dashboarding tools used in Palantir Foundry. A specific example of a project I have handled using Palantir Foundry involves working for a pharma-based company that had data coming in from different vendors. The data was located across different data sources including GCS, BigQuery, Redshift, and Snowflake. We ingested all of them into Palantir Foundry and built three separate layers of transformation: bronze, silver, and gold for cleanup of data, aggregation of data, and post-processing. At the end, we used the final data sets which we thought were ontology-worthy to create ontology objects on top of them and then used them in the dashboard building steps. Those dashboards were used by high-stakeholders to make very critical decisions.

I use Palantir Foundry for my primary use case, which involves building and maintaining end-to-end pipelines and operational data products at UHG for our healthcare analytics team. I work on data ingestion and integration from multiple healthcare source systems into Foundry, and I design ELT/ETL pipelines using Code Repositories. My day-to-day work involves data transformation and optimization using Python, PySpark, and SQL. I spend most of my time developing scalable data workflows, automating our data processing, and collaborating with cross-functional stakeholders to deliver reliable healthcare data solutions within the Foundry ecosystem. A specific project example I built with Palantir Foundry involved building a healthcare claims and member analytics pipeline at UHG. The goal of the project was to consolidate claims, eligibility, provider, and member data coming from multiple upstream systems into a unified operational data model that the business and analytics team used for reporting and care management insights. My work involved designing ingestion pipelines for large-scale healthcare datasets from APIs, SFTP sources, and relational databases. I developed PySpark-based transformation workflows in Foundry Code Repositories and created reusable datasets and modular pipeline components for downstream teams. I also implemented data quality validations and optimized pipeline performance. The overall solution helped business stakeholders get more reliable and near real-time visibility into healthcare operations and reporting metrics. The main use case for that project was to help business stakeholders and analytics teams use this work for reporting and care management insights. One specific improvement I implemented was optimizing a transformation workflow that was processing millions of claims records daily. By redesigning partitioning logic and reducing unnecessary joins, I significantly improved pipeline execution time and reduced resource consumption.

My main use case for Palantir Foundry is defining datasets and working with data for a client in the insurance sector.In the context of that use case, we have to show information about the customers in a dashboard, and for that we have the user datasets, some other information that comes to us in raw form, we have to clean it, and we show all of that using Palantir Workshop in an application that the other users access.

My main use case for Palantir Foundry is that we work on the ETL process and create agents and dashboards, so my work mainly involves ETL, Extract Transformation Load, and data processing. A specific example of an ETL process I worked on with Palantir Foundry involves health sector data where we track various KPIs, such as how much time a patient adheres to a particular therapy. We start with raw data that we process using various transformations, and then we present that data in an aggregated form as a reporting table consumable by dashboards. This is how we used Palantir Foundry for the entire data transformation logic.

For day-to-day operations, this was a data migration project for an ERP rollout implementation where we automated the data pipelines for the migration. We mainly used the low-code solution that they had for the data engineering pipeline, which is a pipeline builder that helped us ramp up the project timelines rapidly. It had automated refreshes from both the source and the target, and everything was automated once it was built. It had everything in place from analytics, data, and solution to BI solution. The low-code solutions made our lives easier because not everybody is too technical to get started and the barrier to entry is very low. In that sense, we had a lot of pre-built solutions offered which helped us ease out our timelines and bring non-technical stakeholders into the work to get things done. Even the integration was mostly pre-built. All we had to do was use point and click solutions. It was quite smooth to begin with and we were able to do a lot of use cases, including building a front-end application, BI application, and troubleshooting. Tracking the lineage of the data is also very robust.

My main use case for Palantir Foundry is pipelining and analyzing data there.I replace the existing pipelines with Pipeline Builder in Palantir Foundry. I have various data flows and production of national reports, and I am replacing that using Palantir as part of an NHS Federated Data Platform. In terms of analytics, I use it to check data consistency and test it against what I have in other systems. People also use Quiver and Contour. That is pretty much everything I have to add about my main use case or the way my team interacts with Palantir Foundry.

My main use case for Palantir Foundry is from the data engineering perspective. A specific example of how I use Palantir Foundry for data engineering involves raw data stored in Redshift AWS, which we are using those tables in the form of a dataset in Foundry. We are ingesting that data into Foundry and using it for cleaning purposes. After cleaning the data, we create Ontology objects and use them for operational applications in the Workshop. One of the use cases that I found with Palantir Foundry is when I worked on the supplier scorecard, which is dedicated to understanding supplier reviews based on the goods supplied. The company assigns ratings to their products through a supplier scorecard, providing scores to their suppliers. We used multiple datasets and created objects, adding our own logic in the Code Repository to check supplier goods by percentage and count, generating aggregated values in the Workshop app. Based on these parameters, business management can make decisions and take actions to update the supplier's score.

There are several use cases that we are working on with Palantir Foundry. The first thing is for data model creation for all our data engineering pipelines. That is one use case. Palantir Foundry also has an ontology, more of a semantic layer, so that we can directly hand over the data model to the end users. That is another use case that we have, creating the semantic layer ontology. Recently, we have started working on some AI use cases as well. Palantir Foundry has very good wrappers such as AIP Agent Studio and AIP Logic, where you can choose any model and build your own chatbot or any AI function or generative AI function. These are a few use cases we are working on. I work with different types of data in Palantir Foundry, including structured and unstructured data. We process PDFs and Word documents, but I have not worked on any use case with video and audio, although there are a few teams in our company that actually process video and audio as well. When it comes to textual information, I have worked on several use cases, and Palantir Foundry has made it very simple. There are some built-in functions, and you can also use Python libraries if you want. Additionally, there are no-code tools to parse unstructured information.

One of the leading European manufacturing plants uses Palantir Foundry for manufacturing interior parts of various car brands such as Honda, Hyundai, Ford, Mercedes-Benz, and BMW. This involves highly secured information that is not supposed to be shared with any competitors.

I am getting into the ontology space using Palantir Foundry. The primary use case is for developing a common business model that includes data, people, and processes, essentially describing how businesses operate. We are applying this model in the utilities sector.

Our use cases are mostly related to data analytics. We are building some dashboards and ETL pipelines on the Palantir side. Palantir Foundry is a low-code/no-code platform with a user-friendly UI. It is better than Databricks, where you need to code. Palantir Foundry has better data lineage. However, Databricks also provides many features with Databricks Unity Catalog.

The AI engine that comes with Palantir Foundry is quite interesting. We have a lot of data from various trials and analyses. We need a machine learning and analytical feature that can push huge amounts of data into the application based on pre-set rules.

Palantir Foundry is being used for multiple hybrid cloud integrations in one of the services we provide for an existing US-based customer. It's all about getting together data from Azure and Amazon and then providing a hybrid platform through Palantir Foundry. We then provide the analytics or insights enablement for the customer.

We use Palantir Foundry for data engineering and self-service tools. Palantir is a great service tool for business users who don't have the necessary IT skills. It helps them to easily draw up their own models and use cases with data by simply using Palantir's drag and drop tool. It's a great tool for us to say, "Here's your data. You can play around it, build models with it, aggregate tables, and check everything on your own." It's a self-service tool. It's deployed on cloud. The cloud provider is AWS. Over 300 people are using this solution in my organization. It's used on a daily basis.

Our primary use case is for data engineering and some data analysis, bringing in data from several sources and using data wrangling and data managing to support the reporting tools we have. We use the reporting apps for some of our basic reporting. We are customers of Palantir.

This is a data integration tool with multiple components that link to multiple sources to create repositories, transform data and make it available for dashboards or management purposes. We're based in the UAE and I'm a senior manager, customer and user of this solution.

I didn't use Foundry, but I went through some training, and my team became certified in it. When I left the company, there were probably 100 research projects that had been added to it. I did it project by project. Around 30 were completed. About 40 or 50 were in progress, while there were 20 more in the queue. You could reuse data and leverage data that had been imported. We imported lots of Epic data. You needed permission to see the Epic data. Someone with a research project approved by the institution could ask permission to join it with other data. In a relational world, you could say, "I'll give you database permissions, but I'll need to mask these columns that are based on those." It's similar to an SQL database. People submitted their project requests to a project review committee. The capacity was limited because people needed to understand the platform, but I'm sure they have trained more people on it since then.

This solution is used more for the analytics available on the platform. The main use was for a COVID-19 White House initiative that was handled by the Vice President, Michael Pence.

We use this solution for everything, including sales. One of our use cases is performing machine learning to gives us an understanding of customer behavior, and which message should be used to target different customers.