Golden Copy: The Extremes of Data Technology Performance

Machine learning and fintech promise innovation, while European systems dictates raise questions

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Tracking the highs and lows of data technology innovation, from Tokyo's FinTech boom to European regulator's missteps

This month, I am excited to be returning to Tokyo for the first time in four years to attend our Tokyo Financial Information and Technology Summit. In our "Interview With" story, a featured speaker at the conference, Makoto Shibata, head of the global innovation team in the digital innovation division of Bank of Tokyo Mitsubishi UFJ, shares his thoughts on how fintech's advances in the Japanese market are affecting the handling of data analytics and blockchain data.

Shibata identifies a wide open field in blockchain data in Japan, and there could be an equally big opportunity globally. That's the need for organized storage and archiving of blockchain or bitcoin transaction data, plus the need to create standards for such archives and, most importantly, managing appropriate access to this data.

The next topic covered in depth in this issue could eventually offer solutions to that challenge. In two features, "Learning Machines" and "Intelligent Evolution," I examine aspects of pairing technological intelligence-whether that is artificial intelligence (AI) or merely smarter uses of technology-with data management. The common thread in these stories is that it's unwise to just set AI or technology loose. There has to be a plan or goal in mind.

AltX's Roy Singh sums this up in the first aforementioned feature, stressing that in order to succeed, machine learning must be directed toward a defined outcome. Merely producing interesting information isn't enough. And, as David Blaszkowsky, a respected data governance expert, says in this story, precision and correct definition of data matters when deploying machine learning, because just feeding disorganized big data into advanced systems can make these systems smarter, but not necessarily more precise. For financial firms, precision is the necessary foundation of any smarter analysis of data. In addition, moving from data to intelligence requires well-designed predictive processes, as Olmstead Associates' Robert Hegarty says in the second feature.

This issue also contains reports about recent actions by the European Securities and Markets Authority (ESMA). "MiFID Gets Personal" explores issues that firms have with provisions in the revised Markets in Financial Instruments Directive requiring traders to submit sensitive identifying personal information into records of trading activity, regardless of whether their counterparties have adequate security safeguards. There are already plenty of identification and recordkeeping mechanisms in place to uncover questionable trading activity and generate data that can chronicle suspect actions. So this seems like a misguided overreach by ESMA.

There has also been debate about ESMA's fine against the DTCC for data access failures. In last week's column, I wrote that the size of the fine seemed small, while others say it's a strong signal that ESMA is vigilant about such problems. Responding to the column, an ESMA spokesperson explained that the authority is statutorily prevented from levying a higher fine, raising the question of whether the European Commission should take another look at the penalties.

On another front, however, ESMA appears to be making progress on its Instrument Reference Data Project intended to help firms complete reporting required by MiFID II. For more on this, see "Shifting Into Gear."

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