The complexity of siloed data landscapes creates huge management challenges for financial services companies. Firms like yours need an environment that connects data from different sources and uses intelligent technologies to derive insights from large data volumes from on-premise, cloud, and hybrid environments. And business users require data that they can trust with transparency that helps them verify data origin, quality, and traceability.
To address these challenges, you need a modern data management strategy that addresses both enterprise data and Big Data assets. It must act at the speed of business, offering real-time insights that can be applied to massive volumes of data.
The strategy should enable data-driven innovations by including tools and methodologies such as machine learning and predictive analytics. It must leverage existing assets and connect data across the whole technology landscape. Finally, the strategy should strive to simplify the landscape while reducing data redundancies.
Key components of change
With these qualifications in mind, here are three key elements you must consider to create a next-generation data management approach:
- A unified logical financial services data model
- A modern data management platform
- A data hub
Figure 1. Key Elements for Next-Generation Data Management
Each of these data management features (see Figure 1) is well-known. Yet most companies implement them through multiple tools and technologies, based on the siloed databases deployed across their IT landscapes. Without a unified approach, traditional data management is often complex and slow.
To develop a modern data management platform, you must implement three essential elements.
A unified logical financial services data model
You need to develop a standardized, multipurpose data model that helps realize a single, consistent version of the truth. Key characteristics include:
- A business-driven conceptual data model that serves all analytical requirements
- A normalized, semantic description of real-world entities understood by business owners
- Granularity, down to single contracts and transactions
- Coverage of all business domains, including customers, marketing, communication, products, contracts, financial transactions, financial instruments, accounting, risk, regulatory reporting, compliance, and market data
- Support for the full history and versioning of all objects
- Extensibility to accommodate change
A standardized logical data model supports data consistency and simple access from analytics applications. It also minimizes data replication and reconciliation efforts. Modeling the business view of data helps business stakeholders take ownership of their data because they are not required to understand the physical implementation on the database level or the complexity of multiple physical data silos.
A modern data management platform
To build your data management platform on trusted, connected data, you need to collect and integrate data in a unified data landscape based on the standardized logical data model. Essential features include:
- A single platform for all data types and workloads, including OLAP and OLTP, with logical data warehouse features that operate on one set of data – with no replication, data marts, or aggregates
- Virtualized views across data assets, supporting replication-free consumption of connected data from different technological environments
- Modern in-memory design, providing high performance and the ability to handle large volumes of data in a scalable manner
- Flexibility to accommodate all deployment options, including on-premise, cloud, and hybrid
- Design based on a native SQL approach, easing development and maintenance of the database platform and easily integrating with analytics platforms
- Support for all usage scenarios, including real-time processes and predictive analytics
A data hub concept
A data hub can help you gain a holistic view of data assets, manage data across the full IT landscape, and integrate data into a unified view. By building the platform around a data hub, you can increase transparency of and access to all data assets, which increases agility and the speed of innovation. Critical data hub functionality includes:
- Open architecture foundation, allowing the hub to connect data no matter where it is physically located – in the cloud, on-premise, in Hadoop, or on cloud object storage
- Data sharing and discovery across the enterprise
- Single view for data asset management, supporting data analysis and governance (including pipelining, orchestration, and monitoring)
- Elimination of the need for centralization of data and mass data movement to a single data store
- Support for complex data processing operations, such as machine learning-based analysis
- Governance and orchestration for data refinement and enrichment
- Metadata catalog management, improving the visibility of data assets across the landscape
As financial services leaders increasingly realize that more trusted, connected, and intelligent data contributes to digital transformation, I’ve noticed a change in perspective. Instead of viewing data as a cost driver, most leaders now see it as essential asset – one that requires certain investments to unlock its true value. Building a modern data management platform – one that enables massive “haystacks” of data to be automatically analyzed for hidden “needles” of actionable insight – is an investment that will pay off handsomely for tomorrow’s digital leaders.
To find out how leading financial services firms that are deploying data-driven analytics, read my next blog and the rest of the series.