Shoreline presents at the Investment Magazine’s Investment Operations Conference

Investment Operations Conference Key Takeaways - Featured Image

On 23rd March, Dominic Dowd participated in an insightful panel discussion on the need for streamlining data flow within the Investment Data ecosystem and extracting a single book of record to present facts in an age of opinions.

Laurence Parker Brown from Conexus Financial moderated the session; another panel member was Zion Hilelly from IHS Markit.

Here are five key takeaways from the panel discussion:

1. Data Management plays a pivotal role in supporting the operational functions of investment managers and is extending to a wider variety of other stakeholder needs.  The core investment datasets, including holdings, transactions, cashflows, benchmarks and market available reference and pricing information, is consumed by operations groups who are primarily responsible for investment operations, investment risk, performance, and reporting. This core data set is also widely consumed by groups across investment managers, including the whole of fund portfolio management teams, a range of public market and private asset investment departments and executive management teams. In addition to core investment data sets, the investment departments and total portfolio teams also use a range of other data sets such as macroeconomic, fundamentals, foreign exchange, and new alternative data such anonymised point of sale and credit card data, carbon emissions and other ESG data, satellite images and geo-location data to manage exposures and risks and identify new investment opportunities.

2. Data should not be siloed across different parts of an investment management business. Several versions of similar investment datasets can prove to be detrimental to the health of any investment firm. If a portfolio manager is trading using a different dataset to the team calculating performance, the measurement of risk and reward will be misleading and inaccurate. Mismatched data can lead to the reconciliation of a different cash balance to what is traded, leading to overdrafts or uninvested cash balances. It can also have serious implications for compliance and the prevention of violations.

3. There is a strong desire to implement robust data governance within the investment data ecosystem.  Investment firms should ensure that data stewards and data owners are empowered with tools and business processes aligned to the fund’s guiding principles for data governance. To ensure appropriate capture, analysis, and protection of data, we should ensure they are involved throughout the governance process. First, by helping to define the governance strategy, framework and associated policies and controls, they should be integral members of Governance Committees at the executive level right through to operational working groups. Second, given their expertise in their areas of data ownership, by helping to define what’s possible with the data through contributing to the definition of appropriate controls and reporting metrics to meet consumer needs. Third, data owners should take proper steps to ensure they are “producing” the best quality product, rapidly and continuously refining, updating, enhancing their product (best practice). The optimal outcome is to “fix at source for all to benefit”.

4. The application of alternative data in the investment management business has achieved increasing traction in recent years, but alternative datasets also present different challenges from a data management perspective.  Currently, in most organisations, core investment data is centrally managed/operated, while alternative data is often managed in a decentralised way – within the investment departments. However, many investment managers are working now to extend their core enterprise data operating models to enable new types of data.

There are four major challenges in handling alternative data. First, data variety: alternative data comes in many different forms, frequencies, formats. Second, data relevancy: there is so much alternative data out there, and it is growing, but only a small amount is relevant. Third, commercial and corporate: many alternative data providers are small companies that aren’t used to dealing with large asset owners and asset managers, which creates commercial and contractual gaps. This has changed over the last two years with maturity in the industry. Fourth, operational: for each stage of the data delivery lifecycle, there are differences from core investment data, including scouting, data engineering, data management, data delivery and consumer usage.


Investment firms can overcome some of these barriers by defining a tiered operating model which blends agility and the ability to innovate while also trying to achieve operating efficiencies and maximising firm benefit and value through broad distribution of new data sets. Leveraging new technologies for scouting, engineering, and new data pipelines helps in this process as well.

5. An evolving data culture is a key determinant of success in the Investment Data Management function. Data culture starts with understanding the value of data. Most companies have realised over the last decade that they are, in fact, information companies. Successful companies use information — both internal and external — to operate their business and drive every decision.

In terms of understanding the value of data, it is widely talked about now, but this is a relatively recent phenomenon.

In practical terms, data culture best practices include the following:

  • Recognising the value of data to the organisation
  • Ensuring data has the appropriate profile with the executive members of the organisation
  • Organisations are valuing data expertise and data employees through appropriate roles and compensation structures.
  • Allocation of funds and resources to data programs and establishing/maintaining world-class operational capabilities.
  • Developing business-driven data governance frameworks and operating models
  • Leveraging appropriate technologies such as data search and discovery (using natural language search) and data catalogues.

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