Leading capital market firms are rapidly embracing new data-centric technologies like cloud platforms and AI. These technologies are largely supported by hybrid cloud models that leverage both public clouds and on-premise/captive servers connected by numerous data pipelines. Currently, the majority of core transaction data remains in-house and a good part of non-transactional data sits in a cloud environment. 

Given these complex data estates, any large capital markets firm already has access to more data than it can possibly use. This data, used for analytics and in AI models, allows firms to generate insights, manage risk, and create new investment products. However, a common unanswered question is: Can firms trust this data to be accurate or reliable? The inability to confidently answer this question — and, furthermore, quantify it with a Data Trust Score —results in poor decisions, compliance issues, and sluggish responses to rapidly changing market conditions.  Often, the very data pipelines that link the entire data ecosystem together are the problem. As these pipelines transmit, transform, and quality check the data, breakages compromise the business value of that data and pose regulatory concerns.

Capital markets firms need to pair their emerging data and cloud capabilities with robust data observability. A data observability program asks: How can I actively manage and monitor my data and proactively respond to data quality issues? How can I ensure the continuous health of my data pipelines? And, ultimately, how can I build trust in my data?

Architecting best-in-class data observability will provide capital markets leaders with full confidence that they have the data they need to support their customers, comply with regulations, and make bold strategic decisions.

The Capital Markets Data Ecosystem

The hybrid cloud environment common to most capital markets firms is further complicated by the many data streams that capital markets firms are juggling: transaction data, trade data, market data, regulatory reporting, client communications, internal business intelligence, and much more. The volume of this data continues to accumulate. Increasingly, third-party data is also an important part of the equation.

Each firm’s client base and application/platform landscape is distinct, so there is no one-size-fits-all solution to these data quality issues. For better or worse, solving these problems requires a highly customized approach to tool alignment.

Furthermore, new tools are being added all the time — which means that a data observability program isn’t a one-time engagement. Data observability needs to continuously ensure that incoming apps and platforms will be supported by trustworthy data pipelines and processes.

How Data Observability Changes the Game

In order to ensure data accuracy and the real-time reliability of data operations, firms need to take a holistic approach to data observability. The first step is an assessment phase that examines data reliability (quality, drift, recon, and freshness) at each stage of the data journey. This assessment should consider data pipeline efficiency, data quality, and system performance. 

Next, firms should ascertain which data tools align with their objectives, considering tools for monitoring, logging, alerting, and insights. At this stage, they can also define the key metrics and KPIs that will serve as indicators of the health of their data ecosystem.   

Implementing a data observability solution is an ongoing process that requires collaboration across different teams and a commitment to continuous improvement. By enabling operationalized alerting across process, data reliability, and compute environments, firms can ensure that proper governance is implemented on a granular level in order to support compliance requirements and proactively respond to data quality problems.

Data observability inevitably improves data quality, which in turn has positive downstream effects across numerous business-critical functions, including portfolio optimization, risk management, trading cost analysis, and regulatory reporting. When leaders lack trust and confidence in the firm’s data landscape, they are forced to take a cautious approach to change and innovation. Once leaders achieve a high level of data observability, those concerns evaporate, and the firm is empowered to make quick, decisive, data-driven course corrections that improve the customer experience and the firm’s fundamental performance.  

About the Authors

Gaurav Singh

Practice Director, Data Analytics & Insights, Wipro
Gaurav brings more than two decades of sales and solutioning experience for data and analytics in the financial industry. Along with deep client-centricity in his work with securities and investment banking accounts, Gaurav’s unique mix of technology and front-to-back domain experience enable him to position and frame transformative digital and data solutions including data management, data platforms, cloud solutions, AI, and analytics. His domain, consulting, and data engineering experience in capital markets includes market/reference data, data services, enterprise risk management, model risk management, compliance, finance, and regulatory reporting technology solutions.

Mahesh Kumar

Chief Marketing Officer, Acceldata
Mahesh Kumar is the CMO at Acceldata, the creators of Data Observability category.  His professional experience spans over two decades in product, sales and marketing roles solving data oriented problems in the IT, DevOps, Security and Business domains. Prior to Acceldata Mahesh held marketing and sales roles at GitLab, Mercury Interactive and Loudcloud/Opsware.  He has an MBA from The Wharton School.