The Kingland Data Refinery Platform
The Kingland Data Refinery Platform is a cloud-based data & analytics platform that allows enterprises to manage end-to-end data refinement activities.
Platform Capabilities
With the Data Refinery platform, enterprises can aggregate and analyze data, remediate and enrich data for higher-value use, and operationalize these end to end data refinement activities.
Key Platform Outcomes
Accelerate
Data Refinery accelerates project timelines by streamlining your teams' access to critical data.
- Enables projects to start faster with automated schema inference and faster loading of data sources.
- Start new data projects and begin improving data quicker via our user-friendly interface.
- Connect existing data tools with easy integration for rapid access to critical data.
Orchestrate
Data Refinery orchestrates technical wins to provide quick, trusted, and efficient outcomes to the firm.
- Scales up and down dynamically to support processing needs while controlling costs.
- Orchestrates, via templates, efficient solutions to common industry and data problems.
- Supports governance, data re-use, and project collaboration across the enterprise.
- Deploys to your AWS environment, giving you more control over your data and cloud spend.
Improve
Data Refinery improves and protects the end-to-end usefulness and value of the firm’s critical data.
- Improve critical data with purpose-built enrichment and data remediation capabilities.
- Automates repetitive and laborious data science, engineering, and reporting activity with background tasks to increase the value of your data experts' focus.
- Increases the usefulness of critical data and safeguards sensitive data with built-in data governance.
- Data Refinery’s available services bring real, hands-on industry expertise and data refinement skill sets to your critical data project.
Improving Regulatory Compliance with Data & Enhanced Controls
Financial services regulators expect that to effectively manage risk, financial institutions and securities firms must have mature quality assurance practices in place to manage adherence to regulations and an institution’s policies and controls. Regulators are asking for access to the data flowing throughout the business to allow them to examine the bank’s controls from AML & KYC, to credit and operational risk, to liquidity, transactions, and resiliency. Foundational to their examinations, regulators are improving their skills, tools, and techniques to look more broadly and more deeply at the critical data elements in use, in addition to interviews and traditional examination techniques. The result: banks are prioritizing modernization initiatives to enhance controls, data quality and their use of data enhance controls.
Assessing Critical Data In The Financial Services Industry
In 2023, Kingland partnered with DIACSUS to survey executives from the world’s top banks, investment firms, and data suppliers about their business and data priorities. While business priorities may vary institution to institution, these executives all agreed to one common priority across their most important initiatives in 2024: Critical Data. Rather than focusing on the growing volumes of data throughout the enterprise, these executives have realized that 2024 is a year of focus, but as we enter 2024, their focus isn’t necessarily on managing all of the data flowing through their enterprise.
Data Refinement for Entity Classifications
In the world of financial services data, clients and products are two of the most important categories of data. A client or product record may be a simple as a handful of attributes, or as complex as 1,000 attributes to describe all of the characteristics of a client. When we focus on classification data, we are focused in on the data attributes used to group, compare, contrast, and represent the same qualities or characteristics for a specific entity or product. Put another way, classifications typically answer two types of questions:
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What is the entity or product?
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What are we allowed to do with the entity or product?
Data Refinement for Customer & Account Data
Every transaction a financial institution completes traces back to some sort of account, and inevitably, a client or many clients. Client and account data continues to be the lifeblood of banking and capital markets activity, required for operational processes, critical for risk monitoring, and mandated by regulators. As new risks emerge and new regulations are enacted, many times 1 to 2 simple attributes are missing from customer and account information. Additionally, as a customer relationship expands from the products and services they use today, to new products and services, another 1 to 2 simple attributes are missing. The problem of missing customer and account information may seem fairly straightforward, but when the largest institutions have tens or hundreds of millions of customers, even just one simple attribute can be a $10 million problem.
Data Refinement for AI
Everyone is investing in AI. The ability to automate and improve so many business processes makes AI a game changer for most businesses. Advancements in generative AI have simplified the process to experiment and many firms are running pilot programs, proof of concepts, and full-blown initiatives to roll out AI applications to their business unit or enterprise.
A problem is lurking though: reference data. While the most useful predictive and generative AI models will leverage whatever data you feed them, if the reference data within those data sets is not fit for purpose, the models simply won’t perform.
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