Data on Demand. Client Advisory Board update. 16 November 2012

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1 Data on Demand Client Advisory Board update 16 November 2012

2 Data on Demand The Future Operating Model; Recap from May In May 2012, Citi presented its vision for a future data on demand data strategy for clients: Data governance model converging at the Citi ICG level Moving to an enterprise level meta data solution Common data dictionary / schemas Extending delivery options: Include private cloud delivery to complement data stored at a client s site Extending scope of data managed for clients: Aggregation of client and 3 rd party data used more than once Right time delivery: Introduction of ODS (Operation Data Store) to support close to real time data availability to clients Optimised set of data stores matched to purpose e.g. Mobile, Performance etc. Tools to provide a single client mechanism to access data, abstracted from Citi data store EMEA CAB reaction: Generated a great deal of interest due to challenge of managing data

3 Deliberate Management of Innovation Lifecycle A gated and standardized incubation process to identify and develop commercially viable solutions that address unmet and emerging needs providing consistency in execution Find the future first Leverage key strengths Deep customer insights Challenge Orthodox s focu s explore prot o- type pilot scal e Great Ideas Well Executed Scaled Globally Creates a clear statement of problem to be solved Expansive research to understand opportunity spaces Build concepts, solution blueprints and commercial models Fast prototyping and testing with target market to optimise investment Launch initiative and formalise planning for rollout and secondary adoption

4 Summary Seed Ideas from De Bono 6 Hats The De Bono Opportunity Statement: How would you design the optimum data solution??? unlimited real time access to all data from any location by any access protocol with 100% quality System Architecture You cannot build a solution build the framework Centralised data repository with maximum flexibility on interfaces in and out Contributor data model Data Model Lack of industry standards Mapping tables Think of data as alive Homogenise data Standardise Inclusion of market data suppliers Role of enterprise vendors Industry data dictionary Flexibility Ease of access to data in and out Time to market with new products / services New product introduction Number of users Number of locations Rapid response to new requests Flexibility in the business model Data access everywhere Automation How many people dealing with data problems How many spreadsheets / manual interfaces Reduce human intervention Performance Response times Availability of systems / data Dataflow throughput KPIs for all stakeholders within the data ecosystem Data capacity Cloud technologies Data Quality 3 rd party data normalisation Data aggregation Measure of data quality Trust Single source of the truth Golden copy Trust, trust, trust Requirements Use of subject matter experts don t make it up yourself Number of entities / domains Size of dictionary Documentation of requirements Change control of requirements (manage scope creep) Responding to changes in the business environment Dealing with changing priorities Implementation Maintaining the BAU Rapid on-boarding of new clients Specifications data model and user requirements Focus on the framework the requirements will never stay still Build for growth - elasticity Strategic Priority Cost effectiveness Changing market / regulatory environment Franchise risk (RBS retail) Requirement for long term commitment Build incrementally (don t boil the ocean) Engagement with the business Key man dependencies Data Sources / Feeds Number of source systems Number of data feeds in Market data vs client data Data volume Data explosion (twitter, social media) Number of feeds out Must include all sources for the whole picture Timeliness Easy Usability Frequency of data delivery Speed of delivery Data transfer protocols Batch vs realtime Must synchronise data timing Think about the timing Business Imperative Happy clients Quality service delivery Product flexibility / customisation validation of business utility of solutions Secret to commercial success [Note: This are a summary of the data solution clustered into idea spaces which were generated during the De Bono exercise and have not been validated. They should be used as a jumping off point to tackle the Opportunity Statement]

5 Data on Demand Value Proposition The participants identified the stakeholders who are users of data, the purpose to which that data is put and then assessed the relative value of the three key themes presented: Content, Storage and Access. Stakeholder Uses Content (Data model) Sales Portfolio details Performance RFPs Flow data (new and incremental) Client Management Technology Marketing Fund managers Trading desk Portfolio risk Segregated portfolio performance Sales metrics Distributors Cost Integrity Security Service Speed Trends; Asset class Client type IBOR Research data Market data Solvency II indicator Capital flows Middle office Cash forecasting MiFID / best ex Key risk indicators Value at risk Risk budget Value Proposition Storage Access (Timeliness) High Low High High Low High Low High High High Low Low High Low Low Medium Low Low High Low Medium Stakeholder Uses Content (Data model) Risk and compliance Performance End clients The board Operations Finance Regulations Fund compliance Mandate compliance Returns Attribution Segmentation Reporting Statements Trading Cost Agility Executive dashboard NAVs Controls Prices Feeds Reporting Fee calculations AUM General ledger feeds Financial statements Board reporting Capital management Value Proposition Storage Access (Timeliness) High High Medium High Low Medium High Medium Medium High Medium Low High Low High High Low Low Total High s

6 Executive Summary of Innovation Lab Key Message CTS Investor Services identified the emerging need for Citi to provide a common data model for the asset management industry. The message from our clients is if you can provide us with a data model by which we can perfect data content and accessibility, you will have a winning technology model. The data model can be employed by our customers to enable more simple integration between Citi and its clients, consumption of data on demand and a firm-wide solution to data. While Citi s Data on Demand concept was strongly endorsed, it s evident a common data model and dictionary would greatly enhance the success of that strategy. Summary CTS held an innovation lab with three Investor Client Advisory Board members on the subject of Data on Demand. The clients were Standard Life Investments (Sarah Robertson), Pioneer Investments (Christoph Lauth) and Goldman Sachs Asset Management (Daniel Nevill). Respectively they are a fully outsourced client of Citi, a middle office only outsourced client of Citi and a prospect with little business today. The agenda followed a tried and tested client collaboration format. The De Bono thinking hats tool was used to broaden perspectives on the possibilities afforded by Data on Demand. Citi s concept for Data on Demand was explained and then three aspects of that concept were explored in more detail: Data Content; Data Storage/hosting and Data Access. Finally an inventory of the stakeholders in the client s office who are users of data was completed, and the value proposition of the Data on Demand concept was explored for each. When the value proposition was examined for the stakeholders, it became clear that the first priority for all is content (accuracy, timeliness, lineage and integrity). The second priority is access, with a wide variety of needs from mobile apps for the sales force to standard data feeds for financial reporting. However, the discussion for every phase of the De Bono thinking process, and every element of the concept, repeatedly came back to the topic of a data model. Enterprise Data Modeling is the practice of creating a graphical model of the data used by an enterprise or company. Typical outputs of this activity include Entity Relationship Diagrams, XML Schemas and an enterprise wide data dictionary.. This echoed comments from CAB members in May 2012 when the need for an industry data dictionary was also mooted. It should be understood that the Innovation Lab represents collaboration in its strongest form. Citi has not committed to delivery on the data model, nor the full Data on Demand concept but it is instructive to have had the feedback and endorsement from our valued clients. Next Steps The Citi team will assess the feasibility of publishing a common data model, and the phasing of our delivery of Data on Demand. A further update on this assessment will be made at the November Client Advisory Board and will be communicated to the Client Innovation Lab participants.

7 Data on Demand - update IBOR / ODS We have completed a planning stage for an initial phase covering IBOR data delivered via an ODS model Cash flow forecasting needs have been factored in to the IBOR work We are entering design and build phase for IBOR / ODS Multi year project with client live date in 2014; potentially first component of common data model Data Governance SFS Chief Data Office aligned with SFS Technology to build technical solution that has governance and data quality principles at its core Leveraging Citi data standards, Citi reference data model and Ab Initio Meta Data Hub framework SFS strategy for data is end to end and core to this is the provision of data to clients in a different view to how Citi needs data internally SFS client data view to be generic, but with the ability to provide customised solutions for clients that need it Securities of Interest Continue investigation in to client demand for a securities of interest data feed Benchmark securities Securities previously held Investible securities universe

8 Citi s Data Strategy next steps IBOR / ODS Enter the design and build stages for this phase This will be a multi year build, with first client live in 2014 This could potentially deliver the first component of a common data model Securities of interest Continue investigation in to client demand for a securities of interest data feed Benchmarks Previously held securities Investible securities universe