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A-Team Insight Blogs

Alveo Research Highlights Need to Integrate Data Management and Analytics to Improve Data Quality and Business Decisions

Data quality remains a force to be reckoned with among capital markets firms, with two thirds saying data analysts and quants in their organisation spend between 25% and 50% of their time collecting, preparing and quality controlling data – time they could otherwise have spent on analysis and modelling.

Problems with data quality are most severe in risk management, meaning risk managers can’t optimise their use of analytics even though risk is the area where analytics are most commonly used, ahead of finance and operations. These stark realities are revealed in an Alveo report, Integrated data management and analytics –why it’s the future for financial services’.

The report is based on a survey of data scientists in financial services firms including banks, investment companies, insurance firms and hedge funds across the UK, US and Asia. It considers data quality issues and how the divide between data management and analytics capabilities can be closed to reduce these issues, increase time to insight, and drive decision making for the business.

Alveo’s research shows how wide the divide is, with only 35% of data scientists saying the data management systems and processes they use are ‘very effective’ at supporting business and operational decision-making. Some 63% say their organisation is not currently able to combine data and analytics in a single environment, and 38% cite ‘the need to integrate structured and unstructured data’ among the main challenges they face in bringing analytics to data and using the combination to drive decision-making. The growing number of data sources used in decision-making processes exacerbates the problem.

The survey also found that just 37% of financial services organisations have the capability to incorporate innovative data science solutions such as AI and machine learning into market analysis, investment processes and operational workflows. Overall, this lack of integration leaves data scientists and quants facing logistical issues in accessing data for decision-making.

Mark Hepsworth, CEO at Alveo, notes that if financial services firms want to use analytics successfully, they need to take an integrated approach to managing and provisioning data. He says this requires AI, machine learning and related technologies to prepare the right data, and comments: “Highly skilled data analysts and quants should not be held back by having to spend hours improving poor quality data when the technologies are there to complete the task for them.”

As firms move to the cloud and adopt cloud native technologies, Alveo is focusing on open source solutions that can help to integrate data and analytics. And it is not alone, with 88% of survey respondents saying they make ‘active use of open source technologies within their data management and analytics processes’.

The report concludes that by using these technologies to bring data management and analytics together, firms can gain greater control over data quality and improve data access, achieve ‘data alpha’ and, in turn, improve productivity of data scientists and quants, drive user enablement, make faster decisions, and optimise data costs while maximising return on investment.

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