长期以来，金融市场的一切都与数据有关. 投资者有更好的数据——更完整, timelier, in more digestible and actionable form – have an advantage over those that are less well-informed. Data helps decision makers pursue alpha, provide insights or manage risk. It is essential to negotiating and confirming the terms of proposed trades, 执行事务, 结算交易. Indeed, 这是交易策略的重要组成部分, 风险管理, 合规, and regulatory oversight depends on data about historical transactions and client activities.
最近加速采用现代技术, 尤其是云, 增加合作意愿, 以及业务需求的演变, all have the potential to drive large-scale change for the industry. Data will remain vital – but how it’s stored, organized and exchanged is changing rapidly.
我们新的白皮书， 数据策略 & 金融市场管理，审查与数据交换有关的痛点(如.e., how organizations make data available to one another) and data management (i.e., 组织如何处理数据), looks at how key technologies can address these in the future, 并考虑对未来的假设. The paper also discusses what data providers and data consumers should focus on to maximize benefits.
评估当前痛点
Based on our analysis and discussions with our clients and partners, DTCC believes that today’s data infrastructure is inefficient for four primary reasons:
- 重叠的标准: Data exchange is dictated by overlapping standards and formats, 需要高度维护的. These standards typically assume point-to-point communication with asset class specific and inflexible formats, as well as bespoke data models which has the unfortunate consequence of limiting the ability to explore the interlinkages of data.
- 缺少元数据: The way data is stored in prevents users from exploiting it fully. 元数据形式的信息(例如.e., descriptive data that captures attributes of the underlying data) is often missing or embedded in specific data stores of applications, which significantly limits how broadly the data can be used and re-used in new ways.
- IT & 操作的复杂性: Today’s data infrastructure contributes to significant non-financial risk. Inefficiencies in the way data is exchanged and stored mentioned above have all contributed to IT and operational complexity, with substantial implication for operational risks (and costs).
- 数据质量不足: Many data sets are not of desired data quality to support decision making, 更不用说自动决策了. For all the reasons outlined above, data quality is often difficult to ascertain.
推动下一个十年的变革
在论文中, we distill our views about the future for data exchange and data management into four hypotheses on trends that we believe will drive the next decade of change in how data is used in financial markets:
- 可访问的 & 安全数据: Data users will have unprecedented flexibility in choosing what data to receive and how they receive it, breaking free from the constraints of exchanging fixed sets of fields at pre-defined time intervals. To enable this, data governance, privacy, and security will need to take center stage.
- 互联数据生态系统: Industry participants will successfully free their own data from legacy systems and will not only master pooling it into their own data ecosystems, but, 有用且可扩展的地方, will connect them with others to create a new infrastructure layer. This will reduce duplication of data and allow for co-development of innovative data insights.
- 专注于洞察:更高效的数据管理, rationalized data related technology stacks including cloud computing, 以及日常数据任务的自动化, will free up capacity to focus on deriving data insights from the vast stores of data. 创建数据vnsr威尼斯城官网登入和洞察将变得更加简单, not harder, with the right tools in place that will require less specialized resources.
- 开源数据标准: We expect that the industry will continue to adopt more standard data models, i.e.理解和描述数据集的方法. The most viable use cases will be in reference data and transaction reporting. The benefit for the industry would be less redundancy and better quality in data across the financial industry.
为明天奠定基础
要启用这些更改, the whitepaper suggests institutions that produce and consume significant amounts of data embed key principles into their data operating models, 包括:
- 建立健全的基础数据管理能力: These include having a thorough understanding and catalog of data, breaking down data silos and implementing robust data quality practices.
- 构建强大的数据治理: including the right set of data privacy and security standards to enable data collaboration with partners.
- 发展到行业范围的数据模型: Institutions will need to work together to establish trusted venues for experimentation and co-creation. Firms should explore where there is mutual benefit from collaborative data environments across firms and the industry to advance interoperability.
We are looking to build the future together with our clients and partners. 这需要协商和协调. Many of the ideas here will be supplemented – and a few will probably be superseded – by insights we gain from our partners and clients, 通过与他们的合作.