Company Blog

Toward the end of 2017, Harvard Business Review published its annual list of “Best-Performing CEOs in the World”, based on a number of HBR’s chief executive ranking metrics. 

Maintaining core categories of enumerated reference data is an important aspect of any enterprise data management program. For everything from country lists to entity types, clients need to administer key reference data categories for leverage by the data model domains for entities, people and products.

Managing updates to enterprise data through the governance of a workflow process is critical to any enterprise data management program. A properly designed workflow ensures only the appropriate client users are authorized for performing specific data updates throughout the data management lifecycle.

The management of business rules governing the quality of data is key to any use case involving enterprise data management.  Clients want to manage a variety of data quality business rules and apply them to workflow processes, for providing a consistent approach to data quality management.

Where is Kingland focused within the overall Cognitive / AI / Machine Learning space? Our passion and investment are in two primary areas: 

• Extracting data / important information out of unstructured content
• Learning from vast amounts of data to make discoveries and decisions

Examples of Enterprise Data Management Requests

Regulatory documents, government registries, corporate sites, news and industry trade journals, product documents, you name it – they all represent important sources that contain an incredible amount of valuable data, insight, sentiment and analysis that is ripe for processing. Clients approach Kingland because of a great opportunity for efficiency – they typically already staff teams of data / business subject matter expert analysts to manually extract data and information out of these types of unstructured sources, yet they continue to pursue higher quality, better timeliness, and more productivity from these teams when managing data from such sources.

Historical data from the enterprise data management programs within our clients offer a level of quality management and predictive capabilities, if harnessed appropriately. In one example, legal entity data that has been classified with NAICS codes can offer machine learning opportunities to build models that can verify the classification accuracy of millions of entities, including additionally predicting the NAICS classifications of newly onboarded entities. In another example, retailers benefit from recommendations learned from past consumer behavior, for the benefit of timely product and complementary offerings.

Proper hierarchy data management capabilities are key to risk and compliance use cases. Clients want to manage a variety of complex hierarchy structures, understanding the relationships among data domains such as legal entity and natural person.  

Properly secured and authorized applications are an important foundation to any global enterprise, and clients need to enable this foundation for a variety of data management use cases.  That’s why we’re providing several “how it works” overviews to show how the Kingland Platform solves some of the industry’s enterprise data challenges.  

As the Consolidated Audit Trail (CAT) saga continues to unfold, there is continued uncertainty throughout the industry. When are the reporting deadlines? Are the specifications published and final? Are we reporting personally identifiable information or not? All of these questions are clearly on the table, being discussed by the SEC, SROs, and firms throughout the industry. As the weeks go by in 2018, we expect the answers to these questions to become clear. 

Over the past few years, public accounting firms have invested in having financial institutions provide them files of holdings and transactions, which is commonly referred to as broker data import (BDI) and financial interest integration (FII). These files are then imported into their personal independence system to enhance ease-of-use and robust disclosure. By automating the import of brokerage data, compliance is increased dramatically and user satisfaction with the process goes up dramatically. To the extent that a process can be put in place to reduce the need for a user to log into a system and accomplish the goals of that process, the better. Also, by automating the process, disclosure of financial interests are automated. No longer are there situations where a person forgets to disclose - the system does it automatically every day. Regulators have been pushing organizations to move beyond policy to automated controls that provide compliance with the policy. Automatically importing the data is a powerful response.