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Data Mining and Engineering in Finance

Location: London, United Kingdom
Date: February 19, 2020 12:00 amfloq.to/dDgQ0

This‘marcus’evans’conference will drive initiatives to build an ecosystem of quality data for trade and risk, through embedding data quality efforts within the use of a central data repository, the application of synthetic and legacy data, the role and limitations of machine learning, and use of appropriate data visualisation techniques.

As the volumes of data held by financial institutions increase, so too does the importance of data analytics. The role of data mining within financial institutions has seen a dramatic increase in recent years which shows signs of steadily continuing. Establishing the best practices to facilitate mining and engineering efforts to make your data work for you will be pivotal in ensuring that your institution is the most profitable it can be. With the improvement of data culture and the prevalence of centralised data comes the issues of dirty or noisy data, heterogeneity and the possibility for anomalies, alongside this we are seeing a marked increase in the use of legacy data and external data vendors. These offer some of the biggest challenges for data mining and engineering within financial institutions, but also present the best opportunities for improvement and optimisation. Meeting these challenges and understanding the best way to navigate them could present yet further opportunities to improve your use of data and so increase your overall profitability.

marcus evans‘specialises in the research and development of strategic events for senior business executives. From our international network of 63 offices, marcus evans produces over 1000 event days a year on strategic issues in corporate finance, telecommunications, technology, health, transportation, capital markets, human resources and business improvement.

Above all,
marcus evans‘provides clients with business information and knowledge which enables them to sustain a valuable competitive advantage and makes a positive contribution to their success.

Join us in February to find out more!

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Tags: analytics Big Data big data engineer big data quality data lake data mining data quality data visualisation NLP

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