The data preparation and validation process for enterprise applications is the fundamental requirement for enabling effective data quality and transformation. But managing the data preparation lifecycle is one of the most challenging and iterative processes in any enterprise data management program. It plays an important role in batch-data integration, legacy systems consolidation, application modernization and handling M&A scenarios for regulatory compliance. While data management processes have landed squarely on IT, until the process involves automated data preparation and refocuses on business users, data management will exceed budgets, challenge compliance and defeat the very purpose of transforming business applications to be data-driven.
Building Blocks of Automated Data Preparation
An automated data preparation platform needs to fulfill several minimum objectives to include helping move data owners into the process. To start, it must provide multiple options for data preparation such as manual data construction and direct extraction from data sources or facilitate file uploading while adhering to the target data needs. Next, it must accommodate self-service data construction tools for business users while providing collaboration through web-based user interfaces. Validation of user-constructed data sets should be available through the automated workflow with pre-built reports and dashboards with options for rule-based data enrichment for manually constructed data. And to ensure compliance, the configuration of multiple roles for data access authorization will ensure sensitive data privacy, by reducing raw data exposure to IT teams. In addition to these minimum objectives, there are four mandatory building blocks for any automated data preparation and validation platform.
The construct component eliminates the inconsistency of maintaining multiple spreadsheets and provides a single location where users can update or directly enter data. Construction pages are web-based screens where business users can enter or modify data required by target applications that do not currently exist in the source system(s).
The validate component allows business users to validate data against target mandatory fields to conform with business rules. This component improves the data quality.
The enrich component enables manual or rule-based data enrichment, manipulates and reports on any data and exports the data so that it can be loaded into a target application using native APIs/utilities.
The administer component is the single point of management and administration for data security roles and authorization so that the right users are empowered for data construction, validation or enrichment.
Automating Data Preparation Delivers Business Benefits
Traditionally, data preparation activities have been addressed by IT teams even though data ownership lies with enterprise business users. But automating data preparation and validation processes with business user intervention, allows self-service benefits to be realized more efficiently:
- Self-service: Enterprise business users can manage all data preparation and validation activities on a web-based user interface with minimal training from IT teams after the initial setup.
- Reduced operational costs: Business users can completely take over the responsibility of data construction or extraction along with an end-to-end data preparation cycle in a repeated manner reducing dependency on IT resources.
- Improved turnaround time: When the process is business user-driven using an automated data preparation platform, turnaround time in identifying and fixing data anomalies is improved, translating to better data quality.
- Security and privacy compliance: Empowering business users with more control and accountability for data resolves many confidential/classified access issues by reducing developer access to data.
Simplifying Automated Data Preparation and Validation
There are obvious challenges in the traditionally managed data preparation process. Consolidation and monitoring with different versions of data sets prepared manually on spreadsheets reduce data quality. Conflicts in data validation and enrichment result in back-and-forth iterations between various users wasting time and effort. Unauthorized access to sensitive data leads to legal and compliance issues.Automating data preparation allows companies to resolve all of these issues and put business users at the center of the process, reducing the time and expense traditionally required to maintain data.
Wipro has delivered over 200+ successful data migration implementations while utilizing an automation framework for data preparation. The framework has been successful for large-scale multi-year, multi-country, multi-vendor global rollouts. Moreover, Wipro’s data and analytics solutions team has developed IP accelerators for automated data preparation and validation as well as integrated third-party products for data preparation. These platforms are industry-agnostic, with the intent of providing user-driven data construction and validation features.