by Krishna Jyoti Das, Data Solutions Architect
How can asset managers, investment banks, brokers and other capital markets organizations ensure data accuracy, integrity, and security to drive informed crucial decision-making? This blog explores data assurance and its potential to mitigate risks and maximize returns in capital markets.
Data assurance defines methods of handling data and building trust around it. According to the Open Data Institute, the definition of data assurance is:
‘The process, or set of processes, that increase confidence that data will meet a specific need, and that organizations collecting, accessing, using and sharing data are doing so in a trustworthy way.’
Data assurance tools and practices can help to provide asset managers, investment banks, brokers and other capital markets organizations with confidence that data is trustworthy throughout its lifecycle.
Individuals and businesses are more trusting and more willing to share data and use data that is shared by a third party if strong governance and assurance mechanisms are in place. In recent years, with the advent of various mandated data security measures/compliances, and with the increased uptake of cloud, data assurance has become nothing short of a business-critical issue in capital markets.
At a high level, there are many reasons for capital markets to embark on a data assurance journey. Data assurance is the support pillar to tackle industry dynamics such as regulatory needs, diversified wealth demography, data security, public data, big data, data complexity, legacy, poor quality and missing data.
This complex landscape makes processing and maintaining data to be suitable for capital markets a high priority. The challenge is maintaining the highest quality data, making it readily available and keeping the integrity of the data intact.
‘There is strong evidence that data assurance is key to trustworthy data management.’
Data Governance helps to establish control and policies around data. It helps in process improvement around data access and data sharing, which is key to capital markets. Keeping data at the core of processes, data governance helps embed data assurance between data and its usage.
Here are some specific examples of how data governance can help capital markets:
With the advent of cloud and open data, capital markets need to handle varied types and a huge volume of data day to day, both structured and unstructured, at rest and in transit. Data quality assessments and data profiling helps bridge the gap between stale data and trusted data.
Here are some of the ways that data quality can help capital markets:
The introduction of various global data privacy laws (GDPR, CCPA etc) has mandated compliance for capital markets. Failure to adhere can incur huge damage both financially and reputationally. Data compliance assurance helps in identifying sensitive data, builds policies around it and creates a strict control regime to safeguard it.
Data compliance can also help to enhance the reputation of firms involved in the capital markets. This is because it demonstrates that they are committed to following the rules and regulations. This can make them more attractive to investors and other stakeholders.
Here are some specific related to compliance failure which could have been avoided by implementing robust compliance processes:
Data trust helps institutionalize the data. Although it’s a new concept but for capital markets where the organization could be both consumer and producer of data, data trust is a way in which data could be shared keeping both by maximizing the economic value of data and keeping it under strict stewardship to building trust on top of it.
Here are some specific examples of how data trusts are already being used in the capital markets:
Capital markets players need to utilize various types of data. Knowing the nature of your data is key to get the most out of it. With the increased use of both structured and unstructured data (heterogeneous data sources) a well-defined data catalogue and metadata management system can reduce the time taken to manage, maintain and optimize data use across the organization.
Here are some specific examples of how data catalogues can be used in capital markets:
Investment banks can use data catalogues to help their analysts find the data they need to make investment decisions. This can be done by providing search capabilities and by organizing the data into categories and tags. For example, an analyst who is looking for data on a particular company can use the data catalogue to find all of the data that has been collected on that company.
Hedge funds can use data catalogues to help their traders make trading decisions. This can be done by providing information about the data’s provenance and lineage. For example, a trader who is looking to buy a particular stock can use the data catalogue to find out where the data on that stock came from and how it was collected.
Regulators can use data catalogues to help them monitor the financial markets. This can be done by providing information about the data’s ownership and usage. For example, a regulator who is looking to investigate a particular transaction can use the data catalogue to find out who owns the data and how it is being used.
Slowly but steadily, data assurance practices are gaining traction within many large organizations who are seeing the potential and benefits of implementing data assurance, for example:
The American bank holding company State Street Corporation launched a big data governance program in 2021 to foster a data stewardship program among the business user community, and to create processes to support the ownership and ongoing management of the firm’s data. The consolidation of the firm’s data assets has also led to huge increases in application performance and the timeliness of data delivery. Instead of focusing on maintaining a heterogeneous set of data across the firm’s systems, the uniform data infrastructure means that the data management team can focus on ensuring reliability of performance and deal with any issues as they arise. Though the initial focus of the work is on structured data, the team plans to extend its support for unstructured data in the future. The program of work should therefore put the firm well on the road to establishing a truly digital enterprise.
NAB is increasing its focus on data management and governance strategy in order to improve the quality of customer data, decision making and reporting. While taking a long-term view, the NAB team aims to ensure ‘day-to-day’ customer and operational data is accurate at source across its various systems and engages actively with people from across the entire organization to this end.
Their goal is to better leverage data for the benefit of customers, while reducing operational risk through improved data accuracy and ongoing modernization of technology and data assets. While taking a long-term view, the NAB team aims to ensure ‘day-to-day’ customer and operational data is accurate at source across its various systems and engages actively with people from across the entire organization to this end. Their goal is to leverage data better for the benefit of customers while reducing operational risk through improved data accuracy and ongoing modernization of technology and data assets.
For asset managers, investment banks, brokers and other capital markets organizations to ensure data accuracy, integrity, and security and drive informed crucial decision-making they need data assurance. Successful data assurance implementation requires a perfect blend of technology, people and process. At Qualitest, we have worked with some of the world’s leading brands to provide data assurance solutions. Our data expertise and center of excellence can help your organization on its data assurance journey.
