Compounding the advantages of a financial services smart data lake

The financial services industry faces a perfect storm of business, regulatory and technology factors that are forcing organizations to gain control of their disparate and inconsistent enterprise data.  Legacy approaches to data management and analytics are insufficient and a new approach is required: the smart (semantic-based) data lake (SDL). Unlike traditional siloed approaches, the SDL solves the enterprise data challenge holistically, enabling data to be easily reused for multiple applications, lowering operational costs and dramatically increasing ROI.

Regulatory compliance can lead to reusable data

The list of accords accompanying BCBS 239, Basel II, and seemingly countless others grows dailyas do their requisites. Subsequently, compliance for traceable data governance and data management is no longer an option but a requirement for successful business operations, enforceable by the strictest of penalties.

However, there is a valuable opportunity emerging from the rash of financial mishaps (including the bankruptcy of Lehman Brothers) which erupted at the end of the last decade.  The ever-increasing demands of regulatory compliance are giving forward-thinking organizations the opportunity to get their house in order when it comes to their data.

The rationale is as singular as it is conclusive, and a call to action among those shrewd enough to heed it. If you must gain control of your data assets to ensure compliance, why not do so in a way that simultaneously produces value?

The old approach is to build yet another silo for each accord. The new approach is to harmonize all data for any accord—or any other enterprise use, for that matter—in a standardized format. Making your data reusable increases RoI with each new application of the data.

The new approach involves a financial services smart data lake, a sustainable means to enhance organizational value by harmonizing data management across sources with common business models for improved analytics, data discovery, data governance, provenance, and regulatory compliance. Moreover, the smart data lake features a consistency far surpassing the fragmented capabilities of conventional silos while supporting unlimited use cases with the same data.

Simplified IT architecture

The benefits of the smart data lake include reuse of your data. Since data is brought together based on its business meaning, it can be easily repurposed for multiple uses. So, the data used for regulatory reporting can also be used for other needs such as fraud detection, customer 360 and new product development.

The smart data lake also supports emerging industry standards such as the Financial Industry Business Ontology (FIBO), developed by the Enterprise Data Management (EDM) Council, to provide a common language and universal data meaning for financial organizations and regulators.

The value of standards like FIBO has been demonstrated through practical implementations such as the pilot conducted by State Street bank, Dun & Bradstreet, Wells Fargo, Cambridge Semantics and the EDM Council last year which successfully applied FIBO to interest rate swap analytics and corporate hierarchy data.

This example demonstrates the effectiveness of the smart data approach that is the foundation for the longstanding sustainable reuse of data applicable to much more than just regulatory reports. In particular, this utility is noteworthy to financial services not only for the tremendous quantities of structured data it is accountable for, but also for the increasing quantities of unstructured data becoming more vital throughout the industry. Whether encountered by analysts attempting to monitor commodities or companies conducting trading surveillance, uniform smart data standards harmonize disparate data for reuse across any use cases.

One of the most powerful deployments of this capacity is the optimal implementation of data governance and an improved proficiency for tracing data provenance. The responsibilities of denoting just how data was deployed, by whom, and where, are critical when demonstrating compliance to regulators. Smart data capabilities enable organizations to create interactive models for tracking data lineage across emails, varying trade systems, telephone communication and other mediums because of the linked data approach that’s the foundation of a financial services smart data lake.

Championing the business with self-service

Harmonizing enterprise data in a smart data lake creates a single, authoritative source of all data essential for the growing need for self-service business access to data. These linked data repositories not only deliver answers for impact analysis and provenance, but also are suitable for spontaneous, ad-hoc questions of an organization’s entire data assetsat scale.

This point is pivotal to organizations used to relational technologies with their rigid schema requirements, and long change cycles required to make adjustments to accommodate new data and new analytics questions.

The ability of the smart data lake to support questions not anticipated in advance enhances business agility and lowers the cost and time of delivering data for analytics purposes.

Of equal worth is the adherence to governance and security policies implemented by the financial services smart data lake, which transforms the data discovery process into an expedient, orderly task aligned with organizational needs. The governed nature of this self-service data discovery process is facilitated by role-based access control, managing which users have access to which data and how, in agreement with governance policies.

Furthermore, organizations can leverage popular, and oftentimes already existent, tools for reporting, visualization, machine learning and predictive analytics, supporting business user self-service access to data on demand.

This single, self-serviceable repository of all data is not only sustainable over the long term (resulting in a decreased total cost of ownership, improved ROI, and a reduced need for expensive systemic overhauls of infrastructure common to other approaches), but also supports a variety of use cases across departments.

Perhaps the best example of the systematic reuse of data throughout organizations that smart data lakes enable is the proverbial 360 degree view of customers. All departments can go to the same repository of data to determine each point of interaction a customer had with an organization and its various services. Marketers could determine how this information helps them better advertise, analysts can see which trends and services are most important to a particular customer, and fraud detection mechanisms can see what behavior is truly reflective of clients and their monetary habits. The examples are innumerable.

Heightened data management

Implicit to the preceding advantages of the reuse of data for multiple purposes across business units and the better implementation of governance is the improved data management characteristics of a financial services smart data lake. The improved capability for cataloging data sources is enabled by uniform models and vocabularies allowing organizations to register all their sources in a consistent manner.

Whether these include disparate banking systems or business glossaries, each source is registered based on common business models. A considerable advantage in this regard is that the models themselves are predicated on definitions relevant to the business as opposed to obscure terms significant only to IT personnel. Thus, even metadata is captured and configured according to business definitions, which helps denote critical facets of governance related to owners, data types, and lineage. The granular method of this approach enables extremely targeted mapping for both greater efficiency and effectiveness of data management. It is responsible for the creation of actionwithout the writing of manual codefor purposes such as transformation, a tedious task which can consume the time of data scientists and IT personnel. Similarly, it can recommend integration approaches and automate them. Moreover, any business or governance requisites can be linked to the semantic models to ensure that such action adheres to both business and governance needs.

Surpassing regulatory reporting

The principal driver for the financial services smart data lake is undoubtedly the severe regulatory requirements extending throughout the finance industry. The provenance and traceability benefits it delivers are invaluable in this respect, as is the collection of data in a centralized repository for compliance purposes.

Improved regulatory reporting, however, is merely the first in an endless number of use cases and advantages provided by these hubs. The uniform, semantic modeling for harmonized data of all enterprise information assets, standardized with shared business meanings across applications and deployments, provides a rich tapestry of data pertinent to virtually any business unit. The reuse of this data and its broad array of use cases provide an idealized backdrop for a self-service ecosystem of data, effective data governance, and data management efficiency for almost every data-driven requirement including impact analysis and customer 360s. What begins as an investment for compliance rapidly becomes a hub of harmonized data that creates an increasing ROI as new use cases are deployed.

 

The views and opinions expressed in this article are those of the author and do not necessarily reflect the official policy or position of Banking.com or NCR Corporation.

Image: iStock/spainter_vfx

Written by Marty Loughlin

Marty Loughlin

Marty Loughlin is Vice President, Financial Services at Cambridge Semantics Inc. Prior to joining Cambridge Semantics, Marty was the managing director for EMC's consulting business in Boston. His 25-year career has focused on helping clients leverage transformative technologies to drive business results, most recently in cloud and Big Data. Prior to joining EMC in 2005, Marty was co-founder and COO of Granitar, a web consultancy that grew to 250 people in four years and served clients such as State Street Co., The New York Times and Standard & Poor's. Marty began his career in Ireland with Digital Equipment Co. He holds a bachelor’s degree in English from Dublin City University and a high-tech MBA from Northeastern University.

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