Foundation: Three Layers
To obtain the greatest amount of insights from data, many types of data models and analytical methods are required. Data Scientists request raw data extracts to build powerful predictive models. Analysts prefer tables in a relational database to query & aggregate millions of rows of data. Business users need up-to-date curated models to visualize key financial and operational information.
Historically, a data estate robust enough to serve all these users was only obtainable by large enterprises after years of work. However, with TimeXtender's low-code/no-code interface, even small organizations can quickly build a rich data architecture supporting any type of analytical need. The TimeXtender platform builds & maintains three distinct layers:
|Operational Data Exchange (ODX)||Modern Data Warehouse (MDW)||Semantic Models|
|Ingest hundreds of sources into a single format & location.||Model, cleanse & consolidation disparate data into a single version of truth.||Deliver simple, relevant datasets to multiple visualization tools.|
|Experienced analysts & data scientists can use powerful tools such as Databricks to uncover new insights from raw data.||Analysts & data scientists can benefit from curated & cleansed data to quickly answer critical questions.||Business users can easily use these models to build dashboards answering known questions.|