New Operating Models for Reference Data Management Managed Service & Utility Models

Transcription

1 New Operating Models for Reference Management Managed Service & Utility Models Author Ankur Bareja Financial Services Practice, Tech Mahindra

2 Objective In the challenging economic environment of increased regulatory monitoring post financial crisis, financial services firms have been evaluating their reference data operating models. Two such operating models which have emerged over the last couple of years are Managed Service and Reference Utility. The objective of this paper is to discuss the key business drivers, business benefits, potential challenges and strategic roadmap associated with the vision of reference data utility. Synopsis Beyond cost reduction, the top three business challenges to reference data management are: Regulatory Compliance Keeping abreast of new regulatory requirements. Operational Efficiency - Providing more efficient support for business units. Quality - Continuous improvement of data quality on a sustainable basis. (ReferenceReview.com, 2014) Emergence of new digital technologies, especially cloud based hosted solutions, are driving the development of new reference data platforms which are giving rise to new operating models for service delivery. A clear trend towards increased collaboration amongst data providers, software vendors and IT/ITES service providers is emerging with a number of alliances being formed to develop new service delivery models. Managed Service and Industry Utility Objective is to drive process, data and technology standardization Deliver business value, leveraging economies of scale and Capital Expenditure (CAPEX) to Operating Expenditure (OPEX) conversion over and above traditional labour cost arbitrage Beyond cost optimization, the key benefits comprise operational efficiency, effective governance, risk management and regulatory compliance Reference The term Reference is interpreted to include the following data sets in the context of this paper Security reference data, such as security master elements and identifiers Prices and FX Ratings (Issue as well as Issuer) Corporate actions (only reference data excludes entitlements processing) Indices and benchmarks data Issuer and counterparty data Fundamental data like Company Fundamentals, Funds data, Research, Economic data Other reference data such as countries, currencies, and exchanges The following are the key components or Reference management operations: subscription licenses management operations team Software application(s) for ETL, business rules Hardware and software infrastructure based repository and downstream distribution interfaces Business liaison, program management and vendor management team IT team for development and support of software application 2

3 Various Operating Models for Reference Management Traditional Outsourcing Traditional resourcing model usually on Time and Material contracts dedicated to a single client covering IT and/or ITES. Managed Service Co-Managed Service End-to-end reference data solution on client owned infrastructure and systems. Fully-Managed Service End-to-end reference data solution on a hosted platform owned by a vendor onshore, near-shore or off-shore. Utility Model Source the data once, process it once and distribute to multiple organizations by collaboration amongst reference data providers and IT/ITES providers. Key Business Drivers for Reference Management Operational Efficiency 79% Organizations require manual intervention 55% FIs consider data integration as the biggest hurdle 62% Firms struggle to meet reference data delivery SLAs (99% On-time delivery) Governance 58% Financial services organizations consider maturity of data governance model among their top 3 challenges Regulatory Compliance 54% Organizations are grappling with regulatory mandates (DFA, FATCA, EMIR, MiFID 2) in their data models Cost 36% Organizations face high total cost of ownership as one of their primary pain areas (FIMA, 2013) (FIMA, 2014) 3

4 Cost Structure of Reference Management The analysis of total cost of ownership of reference data management could be structured into two major categories viz. Cost and Cost of Management Operations. Cost The licensing cost of reference data varies considerably across organizations according to several factors such as: cost for a publicly traded and widely held security is significantly less than that of, for example, a complex derivative Vendor selection plays an important role in helping overcome cost versus quality conundrum. Judicious selection of data sources, not just at market or asset class level but down to data attribute level has significant impact on timeliness, cost and quality Most importantly, firms with efficient data governance in place are able to eliminate redundancy and duplication within their subscriptions The correlation between AUM and data subscription costs is less (largely depends on the nature of data, security types and commercial terms of data licenses) and firms have been seen to be spending between less than 1 million USD up to more than 25 million USD. (Cutter Associates, 2014) Cost of Management Operations It has been observed that the cost of managing reference data usually is 3-5 times the cost of reference data itself, though this varies significantly from firm to firm. People and Process Personnel cost pertaining to data management operations which includes acquisition, validation, aggregation, enrichment and exception management could be attributed to the following : operations Oversight of preparation, resolution of data exceptions, data governance, data quality management and reporting IT Developing and maintaining reference data management systems as well as supporting daily data feeds and resolving production issues Vendor management Working with data vendors to resolve data issues and change management pertaining to ongoing content and format changes of data feeds Business liaison and support Catering to data needs for critical downstream systems such as Risk Management, Portfolio Management, Performance Measurement and Attribution, Reporting, etc. Program Management Management resources required for Program Management, Change Management and Governance Technology Cost of developing and operating IT systems required for reference data processing and distribution which covers the following : Reference data software application In-house built or commercial off-the-shelf software (COTS) which incurs capital expenditure in the form of development cost and/ or license cost ETL framework Flexible and scalable framework for extraction, transformation and loading of reference data feeds into reference data application copy data repository -hub/ data-warehouse storing and maintaining the single source of truth for enterprise-wide reference data distribution Multiple types of data distribution interfaces to cater to requirements of hundreds of downstream consumers of Indirect hidden costs inaccuracies lead to errors such as regulatory compliance issues, trading errors which could result in severe financial and reputational losses. 4

5 More than Cost A Business Case for Reference Utility IT and operations outsourcing seems to be entering a maturity phase after most of large financial institutions have developed established operating models which allow them the benefits of labour cost arbitrage. However, there is still a lot of value which could be unlocked by adopting more matured models such as managed services or industry utility. As the maturity of operating models evolve, financial institutions move up the value curve and thus unlock much more business value. UTILITY MODEL Industry Utility Multi-Tenanted Hosted Solution MANAGED SERVICE Fully-Managed Co-Managed Generic - Business Process and IT Outsourcing Value Delivered Early Adoption Phase Increased Maturity Driving Business Drivers Vertical Integration Driving Standardization of, Process and Technology Though reducing the Total Cost of Ownership (TCO) is the dominant theme in discussions pertaining to reference data operations, increasingly organizations are realizing that data management could be a source of competitive advantage as well. This is a paradigm shift where firms are increasingly focusing on data management. The key levers which are shaping up the trajectory of reference data in financial industry are Regulations With a spate of regulations coming through since financial crisis of 2008, the financial institutions have been spending an ever increasing amount of resources to ensure adherence to regulatory requirements. Quality High quality of data is of paramount importance. quality could be a two edged sword as over emphasis on data quality (e.g. expensive though necessary practices such as manual 4-eye-validations) results in diminishing returns beyond a certain point which would only spiral the operating costs without appropriate business benefits. 5

6 Process Standardization Reference data essentially encapsulates information which is required by a plethora of business functions within a financial institution and yet the process which goes into preparation of this data repository fundamentally lends itself to standardization to a great extent at an organization level and to a significant extent at the industry level. Economies of Scale As articulated by several thought leaders in financial technology, the overarching vision towards which the industry seems to be driving is Source the data once, process it once and distribute it multiple times. This is likely to help organizations reap the benefits of economies of scale both within the organization as well as the industry level. CAPEX to OPEX Conversion Utility model imposes minimal requirements for building systems, which requires significant capital expenditure. This cost structure results in high upfront fixed cost getting completely transformed into an ongoing operating cost. Also, utility providers have been working on outcome based pricing models such as pay-per-usage pricing which enables clients to optimize their cost of data subscription based on their usage. Operating Model Traditional Outsourcing Managed Service Industry Utility Business and Process Standardization Pros Primary driver of value is Labor Cost Arbitrage Could be an important initial step in a longterm strategic transformation IT Management, Project Management and Program Governance performed by vendor IT Management (including Infrastructure), Operational Management, Project Management and Program Governance performed by vendor Business Liaison retained by the client or shared with vendor Economies of Scale CAPEX to OPEX conversion Lower operating cost via Labor Cost Arbitrage IT and Operational Management as well as Project Management and Program Governance Business Liaison is retained by the client or shared with vendor Cons Engagement scope is usually limited to a given project Project Management and Program Governance is retained by the client In case of co-managed service - Business liaison and operational management is retained by client. Infrastructure managed by client The model is in conception and yet to commence implementation in the industry Business Liaison is retained by the client Baggage of legacy architecture results in lower value delivered 6

7 Potential Roadmap to Utility Model The key capabilities firms expect from prospective reference data utility provider are: Cost Effectiveness Almost 100% organizations have cost savings as their topmost expectation from utility/ managed service model More than 60% companies are looking for models which will help in optimizing their '-costs' itself Operating Model More than 90% organizations are looking at strong operational and risk management capabilities in their outsourcing partners In the increasingly regulated industry, over 50% of organizations prefer a regulated entity for reference data utility Over 60% of organizations have expressed flexibility in terms of adding new data sources and customizing services as one of the key requirements Agility Over 50% organizations prefer a phased transition to a managed service/ data utility where IT/ITES partner does a lift and shift on their existing systems and then gradually transitions over to the new platform which could be hosted near-shore as well (ReferenceReview.com, 2014) A Vision for Strategic Roadmap - The journey towards a reference data utility could be envisioned to comprise three phases- Business Process Standardization In this phase service providers will standardize reference data sourcing, validation, aggregation and golden-copy dissemination processes across multiple clients. Multi-tenanted Hosting Cloud enablement of platforms will enable the service providers to establish multitenanted platforms thus achieving economies of scale in data processing. True Industry Utility Strategic collaboration amongst service and data providers will help realize the vision of Source data once, process it once and distribute multiple times. 7

8 1) Business Process Standardization Multi-instance platform Processing Standardized processing Processing Processing 2) Multi-Tenanted Hosting Multi-tenanted single instance platform Standardized processing Economies of scale in data processing Processing 3) Reference Utility Multi-tenanted single instance platform Collaboration with data-providers Standardized data and processing Economies of scale in data processing Key Challenges for Reference Utility Processing Proving economic benefits to senior executive management Investments in the utility model would have a certain gestation period. Therefore a strong business case for senior management buy-in is required to establish a strong commitment to the cause. Transition The transition from traditional operating models to reference data utility needs to be wellcoordinated in a phased manner to ensure smooth risk-free execution. Resolving potential conflicts of interests To foster collaboration amongst various players in reference data ecosystem, resolution of potential conflict of interests (especially commercial) between various parties is very important. Ensuring regulatory compliance and risk management The reference data utility would be a strong ally in standardizing the approach to address risk and compliance requirements, but the onus should continue to be on the clients. Our journey so far - Introducing Tech Mahindra Managed Service Managed Service, MDS, is an established reference data platform that provides instrument static, pricing, corporate actions and taxation data covering both standardized and non-standardized financial products based on all major asset classes across all the major financial markets and caters to users located in any time-zone across the globe. The platform sources the data from major market data providers, which is then subject to an extractiontransformation-loading (ETL) process using customizable business rules that enhance data quality and prepares the golden copies for clients. A team of data management operations experts ensure necessary manual validations, exception handling/ reporting and vendor management are performed to conform to strict SLAs. 8

9 A specialized product management and development team continuously works on future development and enhancement of the functional and technical features of the platform to ensure continuous regulatory compliance for its customers. People Process Technology Domain experts, leads with thorough business understanding Periodic training and e- learning module that keeps staff abreast of best practices/ process updates Quality assurance team with well defined audit procedures 4-eyes priciple for validation & workflow management Standard operating procedures which are reviewed with client regularly for updates Capturing evidences for manual edits for accountability Complete before and after audit trail for history analysis and procedural improvements Embedded checklist for BAU controls In built rules and high level of automation - 96% STP Foolproof mechanism/alerts disallowing incorrect updates during exception management Multi vendor data comparison Integrated document management system for price management and evidencing Integrated control flow tool (TOPIC) for price management References Cutter Associates. (2014). The True Cost of Market. FIMA. (2014). FIMA - Risk and Compliance in Management. FIMA. (2013). FIMA Management Survey. ReferenceReview.com. (2014). A Case for A Reference Management Utility. DTCC. (2013). DTCC Quality Survey, Industry Report. Acknowledgements. Ravi Vasantraj, Global Practice Head of Banking Financial Services and Insurance, Tech Mahindra Jonathan Clark, CEO of Citisoft, Fully Owned Buy-side Consulting Subsidiary of Tech Mahindra Abhijit Bhate, Practice Head, Financial Services, Tech Mahindra David Renn, Practice Head, Reference, Citisoft Haresh Gowri, Product Owner, Managed Service (MDS), Tech Mahindra Navin MV, Head Operations, Reference, Tech Mahindra Rune Lervik, Subject Matter Expert, Reference and Pricing, Tech Mahindra 9

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