Alpha FMC – 2023 Asset Management Data Survey

Alpha FMC - 2023 Asset Management Data Survey

Thank you very much for participating in our Asset Management Data Survey. Please fill out the following questions below - it should not take you more than 15 minutes to complete.

Background information

Please tell us your full name *

At which organization do you work? *

What is your role? *

Please provide your work email address *

What geographical scope does this response cover?

What is the approximate AUM (bn) managed by the organisation? (please align with the scope of your response if not Global)

Please confirm the currency for the AUM figure above:

What is the approximate total company FTE size?

Survey Questions

How would you rate the organization's current data capability along the maturity curve:

Living with the Basics - Low level of capability. Data not considered a strategic priority and policies are impractical

Frustratingly Fragmented - Some data initiatives and governance, but in pockets and lagging behind. Limited ability for data driven insight

Getting Organised - Establishing good data foundations, availability and governance. Policies and procedures work in the ‘real-world’ and not just theoretically

Fast Following - Enterprise-wide model to access and visualise data. Structure and governance in place to allow experimentation with new data techniques

Data Innovator - Enterprise-wide operating model generating significant insights and using advanced analytics. Market leading and data used as an asset

What aspects is the current data strategy focused on? (please select all applicable)

Data Vision - setting of a clear vision for data as a business asset including internal data ‘culture’, skills, strategy for operating model, customer experience and innovation

Data Governance - controlling how data is governed including roles & responsibilities, policies & procedures, enterprise data architecture, data acquisition and vendor management rules, and operational risk & compliance

Data Accessibility - availability and generating value from data including management of data sources and core systems as well as the tooling and approach to data analytics

Data Management & Operations - considering how data is stored, structured, managed / mastered and quality controlled including associated infrastructure, data quality management and underlying data structure / model

Is the data strategy achieving any of the following objectives? (select all applicable)

Comments

What functions does the data strategy cover? (select all applicable)

For any functions out of scope for the data management function, please comment on how their data is managed? e.g. owned by respective business function

A data strategy can be categorized into people, process and technology. For the technology aspect of the data strategy, what is your approach? (select all applicable)

In-house build - utilising foundational technologies e.g. Snowflake, Azure, Google Cloud

Buy - using industry aligned platforms including EDM / MDM / data quality tools e.g. Markit EDM, Finbourne, Golden Source etc.

'Data as a Service' (DaaS) - using outsourced data services including those from Asset Servicers e.g. State Street Alpha, BNYM Data Vault, Northern Trust etc.

Comments

What services would you value most from an outsourced data service? My outsourced data provider...

... manages and stores my data

... monitors data quality and resolves issues

... provides reporting and KPIs

... manages and maintains integrations with third party data sources and platforms

... provides and maintains an industry specific data model

... offers a licence model

... offers a true SLA driven service experience

OtherOther

Many Asset Servicers have introduced DaaS offerings alongside their traditional services (for example State Street, Northern Trust, BNYM, JPM, Citi) - what is preventing you from adopting 'DaaS' solutions as part of your data strategy?

I am unclear on their proposition

I dont feel their solution is mature enough yet

I want to keep ownership and control over my data

Their solution is too expensive

I am tied to an existing Asset Servicer from a wider outsourcing relationship (e.g. Middle Office, Back Office)

N/A DaaS solution is being used

OtherOther

Alpha observes a strong 'data culture' as an important aspect of a successful data strategy - what does that mean to you? (select all applicable)

Data is seen as an asset

Data metric KPIs (quality, timeliness, completeness etc.) are embedded into team/function objectives

Data quality is the responsibility of teams/functions that own the data

Data quality is a priority within a business and technology strategy

There is multi-year / long term investment in data foundations

Data skills are distributed evenly across the organization

Data roles are full time responsibility

Data skills are being embedded into business teams

Recent news on ChatGPT has ignited discussions around the use of AI - are you currently using or planning to use AI in your organization?

AI/ML techniques are currently deployed in production

Exploring or running PoC

Considering

No plans at the moment

Comments

How is current usage of AI managed? (select all applicable)

Using external vendor software / models and external data sets only

Using external vendor software / models but trained on our own data sets

Developing our own models trained on our own data

Developing our own models trained on external data

OtherOther

If generative AI is being used, please select the use cases deployed, planned or in testing from below:

To assist in the research and investment decision making process

To optimize and automate operational processes

To generate client insights

To assist in client service

To assist in the delivery of change, including technology development activity

To support corporate functions such as HR, Finance etc.

OtherOther

Recent geopolitical events have exposed various data challenges at investment managers. From a client relationship management perspective, how long would it take you to identify exposure to a specific country and be ready to communicate at an individual client level?

Same day

Same week

Longer

Comments

How are you growing the capabilities needed to deliver front office data science/analytics?

Leveraging existing investment team - either with existing skills, or through the training of new skills

New investment team hires with the skills and experience needed to build the capability you need - e.g. hiring quants or technologists

Working in new ways across the firm - e.g. building cross-functional investment and technology teams or pairing technologists directly with investment teams

Comments

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