data engineering apis

Data Engineering Use Cases with Interzoid Cloud Data Connect

by Interzoid Team


Posted on February 9th, 2022


Data Engineering Use Cases Cloud Data Connect

Product categories such as Analytics, Artificial Intelligence, Machine Learning, Business Intelligence, Data Science, and dozens of others have emerged around the unleashing of data as a competitive advantage. There are seemingly endless products available in each category.

Yet, very little attention is paid to the actual data content that serves as the foundation of these applications, including data preparation, data availability, ease of access, data completeness, data accuracy, data consistency, data usability, elimination of data redundancy, and fitness of purpose.

We have built an easy-to-use, Cloud-based Data Engineering Platform to help with all of these things: Interzoid Cloud Data Connect

It is a 100% Web application that you can easily connect to Cloud data platforms such as AWS Aurora/RDS, Azure SQL, Snowflake, Google Cloud SQL, SkySQL, CockroachDB, and other Postgres, SQL Server, and MySQL-based systems to quickly and easily "engineer" your Cloud databases.

Here are some use cases where this can be beneficial:

- A Business Intelligence vendor is able to deliver significantly better Analytics outcomes for its customers using data engineering techniques within accessible Cloud Database Platforms for more consistent, less-redundant, enriched data. This provides maximum effectiveness for their product offerings and better results for their customers.

- The Accounts Payable organization of a large financial services firm was able to identify inconsistently-represented company names in their vendor database, enabling them to recover significant amounts of over-billing they had incurred.

- An Online Advertising firm was able to cleanse and prepare multiple datasets prior to the utilization of Artificial Intelligence algorithms to maximize the effectiveness, including increased revenue, of online interactive advertising campaigns.

- The Customer Support division of a large telecommunications firm was able to utilize fuzzy data matching capabilities to more quickly retrieve customer records during customer support calls for better performance and customer service scores.

- A Database Marketing firm was able to identify redundant leads caused by inconsistent and redundant company names for a client to significantly reduce their marketing spend.

- A University was able to improve query results through its donor management information system by standardizing data elements across various internal database tables, enabling a more effective donor communication campaign.

- An Entertainment company was able to quickly undergo data cleansing processes for multiple datasets prior to embarking on a data science-driven effort to identify their best performing artists across multiple media channels.

- A Financial Institution was able to match customers across different lines of business and their respective customer databases through name and address matching algorithms, enabling them through a single customer view to identify valuable customers with accounts across different lines of business, and also those who do not and are prime candidates for multiple products through specific profile targeting.

- A Healthcare Product Company was able to enrich a customer database with third-party geographic data to build better profiles and identify customer clusters and their attributes.

- A Retail Outlet with hundreds of stores was able to capture misspelled email addresses in real-time at the point-of-sale, ensuring better receipt delivery and better ongoing customer communication.

- A Financial firm was able to ensure a high level of data quality prior to experimenting with new financial models on top of large amounts of data within a self-built data warehouse.

- A Marketing firm was able to identify in real-time to identify customers experiencing inclement weather, determining more effective Web site advertising offers.

- A Data Lake Consulting Firm is able to help customers on an ongoing basis prevent their data lakes from becoming "data swamps", utilizing effective data standardization, data profiling, and data matching/redundancy removal techniques.

- During a CRM migration to a new vendor platform, a Lead Generation firm was able to ensure clean, non-redundant prospect information was loaded into the new CRM system, maximizing effectiveness and ensuring a much greater likelihood of success for the new company-wide internal system roll out.

- During an M&A transaction, a large technology firm was able to determine a large number of customer duplication in the customer database of a firm they were acquiring, enabling a significant cost reduction for the transaction.

- A large Holding Company is able to quickly merge customer databases of acquired firms to maximize cross-selling activities across their subsidiary organizations.

- A Direct Mail firm was able to significantly reduce physical mailing costs by identifying prospect redundancy in client databases.

- A B2B Ecommerce firm was able to identify contacts in its CRM system with no-longer-working email addresses, enabling a re-engagement campaign with customers who had not purchased recently from its Website, providing an immediate significant revenue increases.

- A large sales organization for an Insurance company was able to reduce time wasted, as well as potential embarrassment, using matching techniques to prevent members of the new-business sales organization from calling on existing customers.

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