Why the C-Suite Needs to Care About Data: A Capital Markets Sell-Side Impact Assessment Survey, commissioned by InterSystems and produced by Aite Group, highlights the impact of poor data support on business processes, including financial, regulatory, and risk management. This white paper is based on conversations with executives with knowledge of their firm’s data architecture and data management strategy at 19 global capital markets firms. It examines why firms need to invest in data architecture to improve their competitive and operational capabilities in the era of digital transformation. Key takeaways from the study include:
- Three of the top four data architecture challenges are around integrating, cleansing, normalizing, and transforming data for use by the business. These challenges will only increase as the volume and number of data sources needed increase.
- All of the sell-side respondents have a problem with operational and technology data silos, but many have plans to tackle these silos via technology investments and strategic governance programs.
- Investment in robust and scalable data support can enable the front office to avoid the reputational damage caused by outages and scalability issues. Consequently, trading teams place a great deal of emphasis on data architecture support because of the need to maintain a competitive edge.
- An effective data management team is focused on demonstrating the “value” in data and emerging business cases—the priority is gaining business buy-in and support across the enterprise for improvement of data architecture and data delivery.
- The majority of sell-side respondents view faster time to market as the most important benefit and goal of data architecture improvement. Responding to client requirements and trading opportunities in a timely manner by supporting new asset classes and geographies is vital in such a competitive landscape.
- Compliance is also at the top of the list for sell-side firms because of the increased importance of reporting and data transparency post-crisis. For example, trade and transaction reporting are predicated on accurate and timely data aggregation, which can be exceptionally challenging across internal silos, especially at scale.
- The sell-side firms are either implementing an API strategy or are in the process of considering one. The focus is on external API strategies that allow clients to connect internal platforms, improving real-time data transfers and the provision of analytics.
- Aite Group estimates that Tier-1 sell-side and buy-side firms have less than 10% of their total technology stack hosted in a public cloud environment. This is due to change as multiple banks seek to improve balance sheets and C-suites have a strong desire to adopt more flexible approaches, avoiding huge one-time expenditures.
- Though it is early days for machine learning (ML) technology overall, all sell-side respondents are either actively considering ML’s application or are piloting this technology in areas such as fraud and financial crime detection, and trade analytics