Global spending on artificial intelligence (AI) systems is expected to leap from $37.5 billion in 2019 to $97.9 billion by 2023, as firms seek to harness the capabilities offered by AI software and platforms, notes International Data Corporation’s Worldwide Artificial Intelligence Systems Spending Guide.

AI and machine learning (ML) can potentially revolutionize data analytics and business intelligence. By enabling financial services organizations to get their arms around the vast lakes of data they’re accumulating, the technologies can help firms detect market trends, spot investable patterns, make predictions, gain deeper insights into client behaviors, and better respond to customer needs.

But as Accenture points out, “AI is only as smart as the insights that fuel it.” Garbage in, garbage out, in other words.

Data usability, and thus the real-world value it can provide, depends first and foremost on its reliability and integrity. And this emphasis on data quality is not limited to feeding AI and ML solutions. Systems throughout the enterprise—from portfolio modelling and risk management to accounting, reporting and compliance—need more accurate, complete and timely data … a demand that continues to grow.

Which is why effective data governance has become a business imperative.

Modern data demands

Investor and regulatory scrutiny of investment firms’ data management and operational processes is becoming increasingly more rigorous. Clients are calling for more detailed performance analysis, and real-time portfolio and transaction monitoring. Regulatory reports require greater granularity on a wider array of activities. Errors put reputations on the line and can be punished with sizable fines.

Internal portfolio management teams likewise, need faster, more reliable information, showing real-time exposures and the latest cash positions to help improve investment governance and risk management.

For fund administrators and prime brokers, ensuring the huge data volumes they handle are accurate immediately in order to catch any problems and breaks—and reduce the effort in remedying them—is equally crucial to meeting their SLAs and compliance obligations. Any breach can risk financial penalties and/or loss of business. Robust data governance processes serve as a powerful plus point during operational due diligence assessments too.

Yet we see many organizations struggling to manage their data effectively. They waste time trying to validate data quality and resolve discrepancies (often manually), hampered by inconsistent workflows across disjointed applications. And the sub-standard data that inevitably results poses significant operational risks and acts as a drag on efficiency.

Data governance benefits

A sophisticated data governance tool can change the way data is managed and employed. Rather than a mishmash of processes—with business lines or operating silos working off their own spreadsheets and following different procedures—adopting workflow-based processes for data examination, repair and resolution will enhance data consistency across the enterprise.

Effective data governance also allows corrections to be made earlier in the lifecycle. It ensures data is more accurate and can be used with confidence sooner, in some cases without needing to be reconciled. Risk management and investment decision making are a case in point, enabling firms to track positions and exposures without having to wait for the reconciliations to be completed.

Take the effort out of reconciliations

Improving reconciliations activities and minimizing exceptions management offers further big wins.

Reconciling positions, transactions and cash is absolutely crucial to ensuring books and records are complete and accurate. Yet the work is mundane and often labor-intensive. Having to identify and fix errors that could have been caught earlier is a huge wasted effort.

An automated data governance tool can save that pain. By surfacing errors and exceptions in the data, it can limit the number and seriousness of breaks that emerge during the reconciliations process, and reduce the time and trouble involved in fixing them. Analytics able to identify inefficiencies and the root causes of data problems then allow for rules to be created around those exceptions to pre-empt them.

Bringing intelligence to data governance

At SS&C Advent, we’ve incorporated machine learning and predictive analytics capabilities into the data governance tool we’ve built into our industry-leading portfolio management platform to further extend these automated efficiencies. By learning how staff handle various types of exceptions, the data governance technology starts to predict users’ actions and compare them with what actually happened. Once it reaches a sufficient confidence level, the solution can take on those tasks for the particular datasets.

The tool’s user-friendly dashboard views also provide transparency and oversight into data issues, with firms able to monitor exception handling and their resolution status, and prioritize approvals. Alerts about problems or breaks help users proactively address issues that emerge. Plus flexible, three-way maker-checker workflows reduce the potential for errors. This minimizes costly remediation and operational risks, and helps firms to meet client deadlines and deliverables with accurate and reliable information.

In today’s high-speed, globalized investment environment, preventing poor quality data from seeping into any part of the operation can make all the difference to a firm’s risk management, client relationships and regulatory compliance. Intelligent data governance serves as an increasingly essential weapon in that battle.