In this special guest feature, Alexey Utkin, SVP of Financial Services Practice at DataArt, discusses how buy side financial services firms are able to enhance capability for risk management using big data. Alexey joined DataArt as Systems Architect and Team Leader in 2004, and has been in charge of leading major financial enterprise accounts since. With over 14 years in the IT industry, eight of them in the financial services sector, he brings a wealth of industry expertise to DataArt and has become a core member of its Financial Services Practices. With a dedicated focus on solution, technology, regulation and process consulting, he now leads DataArt’s most seasoned industry practice from its London’s office. Alexey holds a MS in Applied Mathematics from St. Petersburg State University (Russia), with a specialization in Computer Science.
Big data began its hype cycle several years ago. We now see entire industries embracing the technology and reaping its benefits – retail, media, travel, IoT. The financial services world is often behind early adopters of new technologies. The buy-side sector, which includes private equity firms, hedge and pension funds and other investment managers, certainly has not been transformed by big data just yet.
The main reason is that buy-side firms traditionally do not have pressing problems where big data would be an obvious solution. If we take the four V’s of big data perspective – velocity and volume for most of the buy-side firms are manageable with relational databases technology paired with substantial infrastructure investment; usually variety of data isn’t huge compared to other industries, and there is no apparent benefit to veracity in big data adoption.
With relatively few exceptions, most buy-side firms traditionally have technology as a supporting function, not a source of a competitive advantage. They tend to compare themselves to peers and most firms do not rush to invest in technology unless they feel they are falling behind. Lack of skills and costs are additional hurdles for early adoption.
In the last few years, big data use with buy-side firms has been limited, covering a single aspect of the business, primarily in the areas of revenue generation and seeking alpha (research and analytics for trading). For example, with ever increasing amounts of open data and new data sets available, we see more successful cases where valuable insights are derived including the use of customized open data to predict performance of particular companies or sectors.
In a reality of tough competition between buy-side firms, commoditized investment strategies, questionable performance in some sub-sectors (e.g. hedge funds industry has been under-performing market indices recently), we see more demand for innovative strategies, research and analytics, underpinned by technologies and solutions, associated with an umbrella term ‘big data’, such as data processing platforms, data lakes, semantic and graph databases, unstructured data and text analytics.
Investment firms showing consistently good performance are faced with a challenge of growing their business, namely assets under management. Investor relations and marketing are primary focus areas, with systems analyzing CRM (customer relationship management) data, combined with a wide range of new data sets including media and social networks to derive high priority investors and leads. These are all areas where big data analytics technologies are often involved.
Buy-side firms also see that data is a valuable asset. As the data management function matures, it gets more recognition from business stakeholders. Ever growing regulators’ and investors’ demand for timely, accurate and granular reporting, as well as the ability to handle multiple delivery mechanisms, certainly plays a role here.
Businesses get more involved in owning and governing the data. Data lineage and machine learning helps them achieve higher data quality. Business users, more often than not, are able to quickly obtain new skills and are mostly limited only by the data management solution their technology department has put in place. Today, they are confident in using data visualization frameworks sitting on top of a data warehouse, and are getting more interested in things like data platforms, enabling self-service business intelligence, flexible data exploration and discovery; powerful, sometime ad-hoc, cloud or in-memory, analytics.
We are seeing success in early applications of big data to buy-side, and even see some cases being productized by software vendors. As big data and analytics technologies mature, they will become more accessible, stable and cost-efficient. Although most of the drivers mentioned in the article, can be addressed with traditional data technology stack within buy-side firms, there is certainly a case to be made for incorporating the best from the big data tech in the next generation scalable data platforms. These types of platforms will likely be required to level the playing field within buy-side.
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