Michael Shashoua: Looking For the Cutting Edge

Chief data officers, or any data management leaders for that matter, have to confront data governance planning, a task not previously known for inspiring cutting-edge advances. Still, executives at financial firms and technology innovators are finding new and possibly innovative ways to address data governance and data management issues.

Julia Bardmesser, global head of business data management at Citi, and Joseph Spinelli, data scientist at TIAA-CREF, are applying standardization and organization techniques, including the use of metadata, to track data within their firms, and avoid duplications. Metadata is a must for working with any kind of big data, Bardmesser says, while Spinelli believes that metadata is indispensable for finding correlations between investments and investors' demographics.

Big data can be chaotic and also too mammoth to completely replace or correct if there are issues. Data management service providers that focus on big data offerings have said that focusing on specific layers of data rather than a firm's whole data fabric is an easier way to leverage resources and produce improved data functions.

In addition, a slight recovery in the global economy may be boosting a strategic shift toward cost control, and therefore more support for data governance in asset management firms, according to Giles Arbuthnott, benchmark data service manager at Rimes Technologies, speaking about an annual survey on data governance approaches conducted by his company, whose results were issued in May.

"The firms profiting most now are those that are agile and able to adopt new strategies," he says.

Combining artificial intelligence with what humans can bring to problem solving can be more potent than artificial intelligence by itself.

Artificial Intelligence

For financial firms trying to go further than novel applications of metadata, some possibilities could be found among new start-up technology companies applying artificial intelligence (AI) to data applications, with particular relevance for the financial industry.

AltX uses machine learning applied to data to make suggestions to portfolio managers. Dataminr applies machine learning to social media to produce information that is useful for the financial industry in real time-this one is more akin to real-time market data than reference data. Lastly, Verafin applies fraud detection techniques to address know-your-customer and anti-money laundering issues, particularly for customer risk management.

These companies are among those highlighted by Neil Jacobstein, co-chair of artificial intelligence and robotics at Singularity University, in the Exponential Finance conference the university presented in New York recently. They are joined in the financial industry by WorkFusion, which has evolved into AI, notably for corporate actions data. Combining AI with what humans can bring to problem solving can be more potent than AI by itself, which could in turn allow AI to alter the balance of power between big companies and start-ups.

Proving Useful

AI could also prove useful for firms trying to manage multiple counterparty relationships and derive actionable insights about those relationships, based on applying AI to the data being produced.

If AI can be coupled with human guidance, it would be meaningful to the industry because smaller asset managers, firms and funds would be able to take more sophisticated views of data, perform more complex analysis with fewer resources, and better manage dealings with more counterparties at once, leveling the playing field with systemically important firms.

This is where the same cost control concerns that are spurring advances in data governance come into play. An exorbitantly expensive AI service will be out of reach for most small firms, but if the start-ups that Jacobstein cites can keep the price down, the industry could have an AI revolution on its hands.