The key to success is the adoption of new technology within an organization. It is essential that the Business can utilize the capabilities to meet the Business needs at the time and scale. A strong data-driven Enterprise must now expose data quickly and cost effectively using a single self-service Data Virtualization architecture. This must be across multiple disciplines from internal and external fragmented data source systems.

Data needs to be collected based on a targeted function using live automated Cloud, Rest API and OData data feeds to maintain data lineage back to original source. Schema mapping of columns to common unified data tables of each data type allows data cleansing challenges to be identified.

For columns that contain the same data type of content, identify different attribute names, values or different formats to produce consistent attribute naming through the use of machine learning and augmented intelligence. Harmonization then allows combined federalized data in a corporate data pond to have divided data control between Central IT and Business groups. Data is quality controlled and once trusted data can then be analyzed by the end user.

The Subject Matter Expert of the data type works with the Data Scientist to apply transformations to the unified dataset to clean, reformat, or change the unified dataset without affecting the source values. This enables better selection and filtering using curated enriched tables using clean data. Helper machine learning projects will aide data mastering. Security can then be restricted to particular data views using common attributes within a column. Agile development with grass roots support drives high user confidence and satisfaction if organically grown by customers using fast Cloud based front end displays on the web.

Prioritization on user workflow requests must be supported by communication. A successful and trusted relationship is built through good listening, understand the business problems, and deliver exceptional customer experiences. The continued aim is to move from 80% time collecting to 80% time creating value from the information through interrogation and analysis of trusted data. This has led to breakthrough analytic insights, reduced waste and drives consistency on various workflows to be shared.

Traditional approaches cant keep up with todays speed and scale of changes…. The art of what is possible will only be achieved with increased organizational data literacy, data democratization, and data ownership.