We have all seen how more and more companies are moving to the cloud for their data management platforms. Snowflake, Azure Synapse, AWS Redshift, and Google Big Query are leading this charge towards low-admin, instantly scalable cloud database solutions. Accompanying this is a migration to cloud-hosted data integration and low-code ETL solutions like Matillion and Fivetran. It is tempting to assume that with all these low-overhead data management platforms the concept of data modeling may be a thing of the past, relegated to the pile of on-premise databases that this brave new world is supplanting.
In reality, data modeling is more important than ever. A key to understanding this importance is to remember that a data model (particularly a logical data model) is by and large a business model, not a technical model. It is a map of the data landscape and how it relates to business processes and analytical decision-making. It can also serve as a guide to non-technical users who wish to explore the data without hand-holding from IT. Plus it is a cornerstone of governance in that it creates a standard nomenclature and reinforces a common enterprise data schemas.
In short, here is why data modeling is a vital part of enterprise data management:
Data is an asset that must be managed - If we look at a corporate asset class such as finances, we would expect that a chart of accounts, as well as relationships between balance sheet and P&L activity, would be mapped and maintained. The same goes for data . . . your data team acts in a similar way to your finance department in managing and maintaining their assets.
Data modeling provides an aid to understanding and clarity - Some organizations may have terabytes of data from numerous sources; the data model is a “map” of this vast territory and attempts to make sense of it and apply it to business solutions and outcomes.
The data model provides a common vocabulary - As a means of data governance and enhancing an organization’s data culture, a successful data model translates technical field names and joins into common, standardized business terminology and relationships. It changes “data-speak” into “business-speak”.
When pursuing a data modeling solution in the cloud era, falling back on full-client tools that require complex licensing for multiple users and large deployment footprints seems out of step with the “everything on a browser” approach that comes with cloud databases. This is where SqlDBM fits the bill; it is a cloud-based, fully-functional, multi-user data modeling tool. They are also a strategic partner with Snowflake . . . more than 600 companies now use SqlDBM for their Snowflake projects. In fact, the seamless interface between SqlDBM and Snowflake is one of its strongest features, since CREATE TABLE DDL scripts can be generated for SF with the click of a mouse once your model is complete. What’s more, you can directly connect SqlDBM to an existing Snowflake environment and “reverse engineer" the data configuration to create a dynamic model for documentation and reference.
A cloud modeling solution serves the purposes of data modeling in a collaborative, transparent fashion. With the data integration, computational and storage architecture all easily accessible via cloud solutions, a cloud data modeling tool like SqlDBM completes the “soup to nuts” data management strategy that is vital to bring value to an enterprise’s data assets. It allows greater flexibility and agility when mapping the digital transformation journey through modern data applications.