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What Is Data Standardization?
In data standardization, the software algorithms that execute data transformations are decoupled from the very systems that house the data. These algorithms are not persisted in code; rather, their logic is maintained in human-readable rules that non developers can maintain on their own using visual interfaces without relying on IT. Data standardization abstracts away all the complex semantics of how the data is captured, standardized, and cobbled together. It offers aggregators the agility to onboard new partners quickly, enhance the rules that logically blend the new provider’s data with existing data, and provide the business with faster and more accurate analytics.
Why Is Data Standardization Important?
Data mapping is here to stay as the world is not about to adopt a unified way of defining every business data element any time soon. The good news, however, is that mapping doesn’t have to be painful. A modern strategy for handling data mapping is to virtualize the entire process.
Organizations often hard-code their standardization logic in code that resides in the systems that house and move data around. Such strong coupling meant that organizations had to spend significant time creating, maintaining, and debugging standardization code that was spread around several locations, with limited ability to ensure its quality and reusability. With complex standardization logic, organizations have struggled to onboard new partners quickly, causing them to miss onboarding milestones and new revenue opportunities.
A unique approach to data transformation is through virtualization to decouple and abstract away standardization code, enabling business users to define standardization rules using a visual interface that converts the logic to code at query time. With this type of virtualization, organizations increase their business agility, and onboard new partners faster.
The Data Standardization Process
When a new data provider is on-boarded, the analytics automation platform uses its proprietary Data Scanner to understand the source data, regardless of the format or the system it’s in. The platform builds a universal and virtual data layer that is automatically enhanced with pointers to the new raw data elements and includes all the transformation logic that the business requires.
These virtual data columns and their transformations allow the platform to query the raw data at any time, eliminating data moves and copies, and ensuring that query results reflect the latest changes in the raw data. When schema changes are detected, the platform makes the necessary adjustments in the data layer to point to the raw data elements correctly.
With the virtual data columns added, business users define virtual rules to standardize and blend the data. The rules are virtual since they’re not persisted in code. They are kept in human-readable form that business users maintain. It’s only at query time that Alteryx automatically creates the necessary code that it executes to create tables and views.
There are three types of rules that business users maintain for data transformation:
Taxonomy rules: These rules map the columns and values of the partner’s data with the aggregator’s. For instance, a partner can describe their transactions as having two columns: a settlement amount and a type, where the type can be one of three options.
Reshape rules: These rules specify how to pull data elements together from the partner’s side, and how to distribute them on the aggregator’s side. For example, a retailer might provide all transaction data in a single file, but the aggregator needs to split it into three tables, one for transactions, another for retailer data, and yet another for consumers.
Semantic rules: These rules articulate the meanings of data elements and how the business uses them to describe its domain. For example, what constitutes a successful transaction? And how should its final settled amount be computed after accounting for refunds? Each data provider has its own semantics that makes sense in the context of its operations, but one that the data aggregator must reconcile with all other providers’ data definitions.
You can define these rules declaratively using a visual tool. It has a rich set of transformation functions that make standardization easy. For instance, users can map columns and translate values to a standard set, or, pull data together from multiple files including XML, CSV, JSON, EDI etc.
Common problems such as a different order of columns, renamed columns, changes to the values or types of columns can be handled automatically. Users can also use a SQL console to describe more complex logic. In addition, users can build data validations and reports to monitor and check that all the standardizations happened correctly.. As soon as a new file or record is added or changed, a Data Scanner detects it, applies the relevant standardization rules — by dynamically generating relevant SQL code and executing it — and exports the data to a standard format.
The Future of Data Standardization
Standardizing business data from multiple partners is a critical and common task that is only to become more important and frequent as economic developments offer the opportunity to partner with more stakeholders, and as these data providers continue to shape their datasets according to their own business logic. Given the impact that data standardization has on business agility and performance, organizations that aggregate data from multiple sources should consider carefully the infrastructure and workflows they put in place, and their ability to onboard new partners.
Getting Started with Data Standardization
Organizations often face a similar challenge: how to ingest datasets that come in each formatted differently according to its provider’s custom business logic, and have these datasets standardized so that they can be compared, aggregated, and otherwise analyzed consistently.
The Alteryx Analytics Automation platform helps companies prepare data across disparate sources without the need for engineering to build ETL and data pipelines. Customers unlock the full value of their data by empowering business users to work with datasets that are hard to understand, reconcile, and blend, enabling customers to instantly capture and validate business logic in support of a wide range of use cases.