Data & Analytics

Optimize Data and Analytics with a Modern Data Architecture on AWS

As organizations continue to amass more and more data, questions arise on how to protect, distribute, and store your data. Ultimately, the goal is to leverage the data to drive insightful business outcomes, but to achieve that, the underlying infrastructure must be flexible enough to provide analytics on any type of data that is captured or produced by your organization. Financial institutions are collecting more types of data than ever before. Enterprise data is often stored in departmental silos, and many firms have inconsistencies in their client, account, and security master data. Manual operational processes are used to keep data in sync across platforms. Problems include business risks such as trading errors or compliance breaches, and operational inefficiencies like delayed client onboarding, ineffective client interactions, and the inability to scale without added headcount. 

Vertical Relevance provides an information-based strategy to design, build, and manage technology and systems in AWS that adhere to compliance requirements. We place high importance on ease of data management and governance, while also providing business functionality to leverage your data; enabling you to create insights on whatever your organization needs, such as fraud models and recommendation engines. 

Our Approach

The combination of modern managed databases, data lakes, purpose-built data stores, analytics, and machine learning solutions on AWS empower customers to process data faster and make more informed decisions. The flexibility of AWS frameworks enables organizations to use data to reinvent their business, while reducing operational costs. 

Our approach to Data and Analytics encompasses our collective experience and knowledge of the financial services industry as well as AWS best practices. When we approach your data problems, we take detailed steps to examine your current situation to mold our methodologies to your organizational needs. Our data solutions are designed from the ground up to ensure your organization spends less time constructing engineering solutions, and more time driving insights. Every step of the way we apply procedures to protect your data, enhance automation, and build infrastructure with longevity in mind. While every organization we work with is unique and presents its own business challenges to solve, a commonality between most of our clients is the requirement on how to organize data to help drive business decisions. We commonly hear similar questions in the space such as  

  • How can we deliver a clear ROI without taking years? 
  • How do we get started without a large initial investment? 
  • How can the data platform scale to meet additional business needs in later phases? 
  • Can I add new data types and data sources that I don’t have today? 
  • What types of analytics are possible? What insights can I gain across the enterprise? 
  • How can I ensure that my data is compliant? Is there a way to visualize compliance? 
  • How can I maintain control over my data, yet still shared to different stakeholders within the business without the risk of exposing PII? 
  • How can I manage the exponentially growing amounts of data my organization receives and generates? 
  • How do I consolidate various data streams off of existing infrastructure? 
  • What does the data governance process look like within a Data Lake? 
  • How can I assess the current quality of my data? How can I enhance the quality of my data? 
  • How do I catalog and label my data? 
  • I see organizations that are starting to monetize their data, how is that something I could leverage AWS to do? 

Our Solutions

Solution Spotlight – Data Pipeline Foundations

The Data Pipeline Foundations provide guidance on the fundamental components of a data pipeline such as ingestion and data transformations. For data ingestion, we heavily leaned on the concept of data consolidation to structure our ingestion paths. For transforming your data, be sure to utilize our step-by-step approach to optimize architecting your data for end-user consumption. By following the strategies provided, your organization can create a pipeline to meet your data goals.  

Solution Spotlight – Data Mesh Foundations

The Data Mesh Foundations provides guidance on buidling a modern architecture to ingest, transform, access, and manage analytical data at scale. A Data Mesh approach enables lines of business (LOBs) and organizational units to operate autonomously by owning their data products end to end, while providing central data discovery, governance, and auditing for the organization at large, to ensure data privacy and compliance.

Solution Spotlight – Lake House Foundations

By implementing a Lakehouse, an organization can avoid creating a traditional data warehouse. Organizations are enabled to perform cross-account data queries directly against a Lake Formation Data Lake through Redshift Spectrum External Tables and/or Athena. Table and Column-Level access granularity achieved through Lake Formation Permissions. Data Lake Governance enabled through Lake Formation Resource Shares. Multi-regional, parameterized, infrastructure-as-code deployments. Full data flow and processing pipeline with Glue Jobs, orchestrated by a single Step Function.

Key Outcomes

Automated Processing Pipelines
Extract, transform, and load processes of your data are automated.
Governance and Access Control
Move away from data silos – Easily view and manage access to your data in one place.
Data Protection & Classification
Identify, classify, and tag sensitive information.
Perform at Scale
Analyze all data and query faster with no adverse impact on performance as data usage increases.

Thought Leadership

Use Case
Use Case: Building a Retail Data Lake

How one of the world’s largest investment companies is migrating data to AWS using a Lake House.

Use Case
Use Case: Financial Risk Data Analytics Pipeline and Lakehouse

How a leading financial services institution obtained a carefully planned, scalable, and maintainable testing framework that dramatically reduced testing time for their mission-critical application and enabled them to constantly test the applications releasability.

Modernize Data and Analytics by Building a Lake House Architecture on AWS

Financial institutions are collecting more types of data than ever before, to better understand their customers, assess risk, comply with regulations, and drive innovation. This data is often stored in silos that cannot scale to meet enterprise needs.

Use Case
Use Case: Building PCI Compliant Cloud Infrastructure

How a multinational payments company achieves PCI Compliance on AWS. By engaging with AWS and Vertical Relevance, the Customer was provided with a mechanism to create new AWS environments quickly and ultimately decreased their onboarding time for partners which materially improves their business. Additionally, the solution enabled the Customer to pass internal and external audits.

Drive Financial Services Innovation

Financial Services institutions want to become more agile so they can innovate and respond to changes faster to better serve customers. Without speed, institutions begin to lose momentum which is why Vertical Relevance has developed tools and resources to accelerate your digital-first journey.

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