With digitalization on the rise, customer demands are becoming increasingly complex. Digitalization demands a “Golden Master Profile'' not just “Golden Data” to provide a 360-degree view from a variety of platforms like Cloud Computing, the Internet of Things, and Big Data. These technologies feed into process restructuring along with outcome-focused business models that are adaptable and scalable across the enterprise. Data is not simply confined to structured sourced datasets but spans heterogeneous, machine-generated, unstructured, and externally sourced datasets.
Here are some common data challenges associated with increasing customer demands:
- Difficulty creating a data governance framework and measuring and resolving data quality issues
- Scaling Master Data Management (MDM) solutions to deal with the volume and complexity of data, especially with the increased use of unstructured, digital data
- Coordinating business and IT teams to achieve the desired end state
- Implementing contextual and analytic solutions over traditional solutions to handle new approaches for multi-dimensional and complex hierarchical data
- Transforming model-driven approaches to be more flexible and agile so they can work with existing business processes to deliver business value
HCL's Data Hallmarking is the process of certifying a Golden Profile that can provide confidence and trust across the entire data lifecycle. Businesses can improve data quality by introducing data quality rules per business process—determined by identifying high and low-performing critical data elements. Data Hallmark can pinpoint low-performing data elements and improve to the next quality level by inputting missing values, correcting wrong values, and more in an automated fashion, using artificial intelligence (AI) and machine learning (ML) technologies.
Built-in automation identifies critical data elements through end-to-end governance processes by analyzing metadata that encompasses gold-level data quality. By leveraging multiple data profiling, integration, and governance tools, the offering helps enterprises create a trusted data view, providing them with a formal way to manage and master data assets—hallmarking them into categories such as gold, silver, and bronze. The advent and infusion of AI and ML techniques into these data quality tools can go beyond measuring by adding intelligence to data quality analysis and improvement.