Achieving Access to Reliable Data

Asset managers are increasingly turning to their data to help inform investment decisions, ensure compliance with regulations, and evaluate performance, yet many continue to face challenges related to accessing reliable data. These challenges can range from:

• Data Quality: Due to the use of non-traditional data sources and many manual touchpoints on ingested data, managers must ensure a high level of data governance and quality. A lack of an enterprise data quality foundation greatly hinders managers’ ability to respond in a meaningful, cost efficient and scalable way to regulatory, analytical and other data management demands.
• Lack of Standardization: Asset management firms must maintain various forms of information on individual asset classes. Because there are variations in the way data is ingested and maintained by different firms, there is an inherent lack of standardization in the industry and a resulting impact on reference data integrity.
• Increased Data Volume: With data coming in all forms and formats, both structured and unstructured, the amount of data firms ingest eventually tips the scale at which modern systems can operate. The increasing inflow of unstructured data demands urgent resolution and quick distribution to both upstream and downstream systems for analytics, reporting and decision making.

Together these challenges – coupled with data’s rising importance for uncovering a competitive edge – are pushing asset managers to accelerate their digital transformations as they look to effectively navigate the current data management landscape. By implementing the proper solutions and services to assist along their transformation journey, managers will find themselves better positioned to access reliable data and use it to their advantage. This begins by leveraging tools and methodologies of data governance, data lineage and data cataloging.

• Data Governance: Governance ensures that multiple checks and rules are applied to data to maintain a level of acceptable quality. This is essential in today’s environment because it’s the only way to maximize the value of large and diverse data assets. Combined with a self-service ability, governance forms a collaborative environment that empowers a robust data ownership model to contribute to data quality.
• Data Lineage: Increased data volume must be addressed on two separate fronts. One is the ability to scale and quickly ingest data to make sense of it and the second is understanding where the data came from and how it has changed. Data lineage certifies that a particular dataset came from a trusted source, and it can also pave the way for higher data quality by providing an in-depth understanding of data flow.
• Data Catalog: With a lack of standardization and variation in data attributes, a system must have a data repository that can be referenced and used to organize data. Exceptional data quality can only be maintained when a robust data catalog assists with automatic classification and tagging, thereby enabling a better understanding of the data used.

IVP’s award-winning master data management suite of solutions possesses competent data quality checks and governance rules to provide reliable data and make it suitable for downstream distribution. This is combined with the native capability of integrating with multiple data sources, such as ESG data, to produce a blended golden copy that can be distributed to various applications for their consumption. It can also run advanced analytics and report on top of that data with both forward and backward lineage to help users gain access to reliable data.

Learn more about IVP Master Data Management, or contact sales@ivp.in.