The sheer amount and variety of data that enterprises have and continue to generate today, are unprecedented. While this growth in data offers many opportunities for enterprises to gain insights and run their business smartly, it also poses challenges in organizing, harmonizing, cleaning, and making the data discoverable and available to the right person at the right time.
How can Fresh Gravity Help?
We help you with your journey from raw data to actionable insights by applying the technologies that will help you leverage data to run your business more intelligently.
In this journey, we help you with –
- Enterprise Data Strategy and Roadmap
- Creating Data Fabric using semantic (knowledge) graphs
- Building multi-cloud Enterprise Data Pipelines
- Building multi-cloud data warehouses, cloud platforms, and migration from legacy EDWs
- Master Data Management Strategy and Implementation
- Data Quality and Data Governance
- Data Discovery, Semantic Search, Data Catalog, and Metadata Management
- Knowledge Graph and Ontology-driven semantic data warehouse, Semantic Search, and Analytics
We take a technology-agnostic approach and can work with our clients’ preferred technologies and platforms. Once the technology is selected and implemented, we focus on building and transferring the capabilities and skills, thereby ensuring that Data and Analytics becomes a sustained competitive advantage for our clients.Our technology expertise includes Azure Data Factory, Google Cloud data platform, Snowflake, Databricks, Informatica, Reltio, Semarchy, SnapLogic, Talend, and Collibra.
We come prepared with various templates, frameworks, technology solutions, and accelerators to help you with your enterprise data project. These include –
- Framework for current state data assessment and enterprise data maturity model
- Industry-specific master data management and domain data models for Life Sciences and Healthcare.
- Rapid Migration utility to migrate from legacy MS SQL Server and Teradata EDW to Snowflake and Databricks.
- Data Governance framework for enterprises.
- Data Quality assessment framework and dashboards.
- Metadata Management Engine that offers metadata repository, data lineage, catalog, metadata discovery tools, and metadata management UI. Download Datasheet to know more.
- Industry-specific accelerators and solutions to expedite implementation and achieve faster results.
- Raw Data Quality (rDQ) – This is an in-house tool to continuously monitor and detect any anomalies in raw data as they appear in the critical tables of the data warehouse and enterprise data pipeline. rDQ can detect anomalies in any table that holds either raw or transformed data due to complex ETL pipelines.
Client Success Stories
Clinical Research Organization (CRO) and Contract Commercial Organization (CCO)
Our team streamlined the client’s CRO and CCO operations by providing a single source of truth for the Investigators, Facilities, Studies, Sites, and Addresses. The data was mastered in Reltio MDM. The other technologies used were Oracle EBS, Google Cloud Pub/Sub, Hortonworks DataFlow (HDF), Enterprise Java, and Reltio Utilities. Batch and real-time data federation were implemented from MDM. The solution successfully drove business process improvements, thus optimizing costs and increasing revenue in Clinical Study operations.
Healthcare Data Platform Technology Provider
Our team helped enable a holistic and accurate view of the various personas (Members, Patients, Providers, and Organizations) for a payer-centric healthcare data platform. This assisted in driving improved and proactive analytics and in laying the foundation for lowering of healthcare costs while improving the experience for patients, providers, and other stakeholders. The core of this system was built using Reltio MDM and SQS, AWS Architecture (Kinesis Data Streams, Lambda Services, S3 repository, Firehose, CloudWatch, Glue crawler, Systems Manager Storage Parameter), Serverless Framework, Core Java, Python, TestNG, and Mokito.
A Large Fast-Food Chain
We implemented a comprehensive domain data model that covered all master data domains, including Suppliers, Products, Freight Lanes, Product Cost, Restaurants, and Distribution Centers data. This mastering is the key to creating a consolidated and holistic view of data. Going forward, it serves as a single point of reference for supply chain optimization for the organization. Semarchy xDM was the selected tool for implementing the Master Data Management (MDM) solution, along with AWS, SSIS, and SSRS.
Networking Solutions Provider
Our team participated in the client’s organizational transformation efforts to become a data-driven business by implementing the Data Lake solution. Amazon Redshift Data Lake was implemented to accumulate, aggregate, and correlate business data and provide a single view, as well as offer advanced reporting capabilities and predictive analytics. Informatica Intelligent Cloud Services (IICS) was used to pull data from different operations systems, such as SFDC (CRM), Oracle EBS (ERP), and Zyme (POS), to seed and continuously update the Data Lake. The primary objective achieved in building the Data Lake solution was to create a comprehensive dataset that aggregates multiple customer touchpoints across various distribution channels. It also provides a foundation for holistic and accurate analytics.
A Large Real Estate Infrastructure and Construction Company
We built a modern comprehensive data platform for the client to streamline their assets and property management. The client is one of the largest global infrastructure and construction companies in the world. It owns a vast diversity of assets and properties that employ various devices like sensors, IoT, meters, etc. We built a data platform with a UI interface as a solution to manage their assets and properties.
The data sources consisted of 3rd party APIs providing meter, equipment, zones, sites, buildings, and sensor data (metrics and parameters) coming from IoT and various other devices. All these were ingested into the Google Cloud Storage (GCS) platform through GCP Composer and Airflow DAGs. The data transformation and data quality operations were performed on the GCS data through Apache Spark, and the transformed data was loaded into Google BigQuery, from which business insights are generated using Data Studio reports. We implemented data classification and cataloging in GCP using the GCP native Data Catalog. We aggregated the logs for debugging and auditing purposes. The UI platform was enriched with various connectors for easy access to the GCP resources, to be accessed by the different asset managers.