Bring your data to the people who need it, where they need it.
Data resides in different parts of your organization, or somewhere in the great wide world.
Your company's ability to integrate that data can determine the quality of decisions your company can make:
- Who to contact
- What to talk about
- When to reach out
- Where to focus your attention
- Why you won (or lost) a deal
- How to optimize your processes or prioritize your actions
Companies that grow efficiently know how to learn from their successes and mistakes. Data feeds that learning process.
We Build Data Pipelines that Last.
In one year, [CorrDyn] overhauled our data pipeline, expanded our BI platform, and unlocked new digital capabilities that drove engagement and revenue. Realized value in terms of productivity, reach, revenue, and ability to sleep at night paid for their work many times over.
- Director of Strategy & Analytics, NBA Team
How We Measure Success
CorrDyn data engineering engagements focus on:
- Time to Value: How quickly can we demonstrate value from the data we integrate?
- Reliability: How can we minimize the time and money your company invests in maintaining the integration?
- Return on Investment: How can we maximize the return to your business from the data we integrate?
We want to prepare you for the next challenge: what to do with your data.
We choose the tools that fit the job. We build on:
- Clouds: AWS, GCP, and Azure
- Pipelines: PubSub + DataFlow, Lambda + Step Functions, Spark, Beam, Hadoop, FiveTran, Stitch, Custom Python
- Storage Technologies: Relational DBs, NoSQL DBs, ElasticSearch, Block Storage
- Data Warehouses: BigQuery, Snowflake, Redshift
- Business Intelligence Suites: Tableau, Looker, PowerBI, Google Data Studio
- End Results: CRM, ERP, Email, Text, Spreadsheet, IoT System, or anywhere you need your data to land.
Our Data Engineering projects have included:
- Capturing data from multiple SaaS platforms and web properties for an online education company
- Integrating mobile ticketing data with Salesforce CRM data for a NBA team
- Combining Amazon, Shopify, Google Ads, Bing Ads, Facebook Ads, and logistics information for an e-commerce company
- Migrating legacy system data into NetSuite, Salesforce, Hubspot, and other enterprise software providers.
- Restructuring convoluted data structures for ease of reporting
- Parsing and migrating millions of text documents for aggregation, search and display to end-users