Manual, human-scale processes, are ripe for the picking and it's really a dereliction of duty to "do" MDM without significantly streamlining processes, much of which is done by eliminating the manual. As data volumes mount, it is often the only way to not watch process time increase over time. At the least, prioritizing stewardship activities or routing activities to specific stewards based on an ML interpretation of past results (quality, quantity) is required. This approach is paramount to having timely, data-infused processes.

The ability of machine learning (ML) to stream, analyze and learn from data sets without explicit programming can be extremely valuable, allowing data scientists to develop and improve ML models on the platform where the data resides. Effective data management should enable data from various sources--including on-premises, cloud and data lakes--to be input into ML models. Workloads such as cross-analyzing unstructured data from external sources with historical structured data to uncover patterns can allow you to predict changes in customer demand, identify potential problems before they occur and more.

IT operations analytics scan all the data that has been gathered using different tools and provides resolutions to problems and does root-cause analysis.

The field of knowledge engineering has been one of the most visible successes of AI to date. Knowledge acquisition is the main bottleneck in the knowledge engineer's work. The benchmark centers on the knowledge engineering viewpoint, covering some of the characteristics the knowledge engineer wants to find in a machine-learning tool. The proposed model has been applied to a set of machine-learning tools, comparing expected and obtained results.

Model Risk Management has recently become a very hot topic in regulatory and compliance-rich industries. Join DataRobot on Mar 29, 2018 for a webinar titled "Model Risk Management with Automated Machine Learning."