Data as an investment: Why you need a strong strategy and governance has been saved
Data as an investment: Why you need a strong strategy and governance
Performance Magazine - Issue 41 ⬤ Published on 16 February 2023
Data as an investment
Why you need a strong strategy and governance
Partner, ACG IM Leader, Deloitte
Partner, Artificial Intelligence & Data, Deloitte
Senior Consultant, Artificial Intelligence & Data, Deloitte
To the point
Data is an asset and needs to be managed as such.
Assets under management (AuM) are continually growing, reaching a new peak of more than $123 trillion USD by the end of 20211. In parallel, more data is available to investment management (IM) actors than ever. This creates questions around data management at an organizational level, in terms of both technology and governance.
As investment strategies and products expand, regulations are increasingly prevalent, creating a need for strategy; data needs to be secured, managed and structured in a way that provides accurate reporting metrics and information security. A strong data strategy also unlocks real-time self-service analytics, enabling enhanced analytics and AI to predict and simulate previously unconsidered strategies.
Thus, unstructured data is not necessarily the “new gold.” But information from cleansed, secured, structured actionable data is. As part of a strong data strategy and governance, IM firms can leverage this meaningful data as an asset for insights that drive sustainability and increase revenue.
IM firms should reconsider their current data strategy. With digital transformation well underway in an increasing number of firms,1 now is the perfect time to consider a strong approach, as it also provides a means to generate alpha. To fully reap the benefits, decision makers must shift their mindset and adopt a “data is an asset” mentality. As management buy-in increases, so will the successful implementation of an effective strategy.
Data is an asset: the benefits of structured data
Data is an asset and needs to be managed as such. With this mindset, firms are more likely to embrace a data-driven approach, from which tangible insights can be gained. Incorporating a data strategy requires considering the data’s current and target maturity level, and then defining the required steps to transition.
Using an effective data management solution with clear governance provides additional support to firms. This is realized through a clearly defined hierarchy of data needs, underpinned by executive oversight. Strong data governance also increases alignment between the IT and business domains; this ensures the requested use of data is prioritized, improving the quality through this increased transparency and understanding. This also makes certain that back and front office staff can trust the data used, helping to drive customer engagement and increase revenue.
Having a single version of truth is also an asset; this can be achieved by creating a “master data system" that transforms unstructured data into one meaningful reference point, reducing reliance on different data sources. This centralization improves controls between systems which guarantees data consistency and reduces risk. A master data source also reduces overall costs as staff spends less time trying to "correct" data. With master data management systems, internal audit compliance is also improved because data can be traced between systems.
As regulatory compliance becomes increasingly critical, the impact of a strong data strategy cannot be overlooked. Appropriate data protection techniques, in compliance with DORA and GDPR, will make data stronger and less vulnerable to cyberattacks within internal systems.2 This reduces risk and other costs frequently associated with cyberattacks.
However, to ensure the strategy is followed and completely realized, a well-defined governance structure is required. As data affects everyone, its governance should be organization-wide, regardless of seniority. For robust data governance, roles and responsibilities must be clearly defined, with distribution based on the size of the firm. Strong governance ensures accountability at all levels, and encourages greater alignment between the business and IT domains. This is critical, as both must collaborate to produce trustworthy, secure, actionable data, and it provides a solid foundation from which firms can become insight driven. Building this collaboration and governance also allows organizations to benefit from use cases with traceable oversight, so data that adds value is maintained and is an asset to the firm.
Investing in a more sustainable world
Alternative investment funds (AIFs)— especially within the Luxembourg market—are increasingly attractive, with a net asset value (NAV) of Luxembourg-domiciled AIFs standing at €1.4 trillion at the end of 2021, and with €1.3 trillion AuM.3 With the AIF market’s year-on-year growth (Figure 1), there is a material increase of AIFs that’s not expected to slow down anytime soon. For instance, in 2020 private equity and real estate funds accounted for 6% and 13% of the NAV of the AIFs in the European Economic Area’s 30 countries (EEA30), respectively.4 By contrast, in 2021, in the Luxembourgish market, private equity and real estate funds comprised 26% and 15% of the NAV of AIFs, respectively.5
Figure 1: Year-on-year growth of the Luxembourgish AIF market, split by fund type.
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AIFs' illiquidity requires additional ESG considerations. With legislation sparking change in the IM industry, private equity and real estate (PE/RE) fund reporting is especially important. The Corporate Sustainability Reporting Directive (CSRD), for example, strives to improve the quality of sustainable data reported.6 Given that CSRD is aligned with other legislation, including the Taxonomy Regulations (TR), the quality and storage of data calls for appropriate administration and governance.
With Sustainable Finance Disclosure Regulation (SFDR) Level II in effect, additional disclosures are necessary for article 8 and 9 products; this is related to the introduction of the TR, which has increased the importance of ESG reporting within the PE/RE fund market.7 TR requires investor alignment with environmental objectives, as well as the appropriate collection of data to prove said alignment. When a robust data strategy is defined, affected systems (operational or technical) will be able to collect data and verify its quality for internal reasons—reporting or otherwise—boosting its accuracy and trustworthiness.
Since the risks of greenwashing are significant, sustainable disclosures have also prompted increased public and regulatory scrutiny of investments. This has made assurances on long-term data critical, and thus, increasingly in demand. And while the CSRD provides means of reinforcing corporations, a robust data strategy should be the primary foundation from which to increase trust in the data collected, released, and—eventually—audited.
With a continued push towards more sustainable investment practices, more regulations will emerge and evolve over time; this includes sustainable finance related data. And as part of the European Data Strategy, changes can be expected in terms of ESG product offerings for investors. Thus, defining a strong data strategy and governance will help IM firms be prepared for when these changes come into effect.8
Data strategies return more than just the initial investment
Strong data strategies integrate all sources of data and ensure they are traced from creation to consumption, simplifying the integration of additional data sources. A robust data strategy also applies the principle of least privilege, which ensures data is accessible only to those who need it; this further reduces risk and operational failures.
And to turn insights into opportunity, data from vendors and data provided to partners and clients, can be merged and reconciled across reporting domains for a more accurate and detailed perspective that spans functional domains and share classes. Providing self-service analytics is critical to determining such insights, and it’s the consolidated data that helps create a trusted single version of the truth. As a result, end users are more likely to experiment with data, testing and simulating the ideas it provides.
Investing in a strong data strategy doesn’t solely promote trust in the quality of data used; it can also improve reporting. Often disparate, data can have varying levels of quality assurance and poorly defined ownership. This creates issues with data veracity, and—as mentioned before— determining a single source of truth becomes nearly impossible. But as data storage becomes leaner, a single source of truth becomes defined and less dependence is placed on external systems, improving the quality and time spent on internal reporting. This also creates a maintainable audit trail, reducing external reporting time.
A well-known example of a single, consolidated data source in investment management is the Investment Book of Records, or IBOR. Full of positions and exposures for traders and portfolio managers, it supports more informed investment decisions and reporting, and removes lengthy reconciliation procedures between multiple middle and back-office systems.
While high-quality, centralized data can be used for analytics, as a firm’s data maturity increases, the complexity of analytics to be exploited does as well. A strong data strategy leverages the tools needed to effectively manage data, unlocking artificial intelligence and machine learning use cases, which in turn, generate business value (Figure 2).
Figure 2: Levels of analytical complexity for improved business value.
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1: 2023 investment management outlook | Deloitte Insights
2: Exploring DORA | Deloitte Luxembourg
3: AIFM Reporting dashboard – December 2021 – CSSF
4: Risk Monitoring (europa.eu)
5: The Corporate Sustainability Reporting Directive (CSRD) (plana.earth)
6: Global Distribution Conference | Alfi Events
7: Sustainable Finance Roadmap 2022-2024 (europa.eu)
8: Demystifying Data | Deloitte Australia | Deloitte Access Economics
A strong data strategy and governance ensures that firms treat data as the asset it can be. In assuming a focused strategy, data becomes more structured and better managed, both operationally and technically speaking. This helps to unlock existing silos, reduce operational risks and costs, and enable more accurate reporting and customer engagement. With increasing ESG legislation, a strong data strategy also increases the trust in the data reported and provides additional futureproofing for providing and storing data. A robust data strategy and governance also creates a single version of the truth, which users can exploit for more advanced analytics that can generate additional value.
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