Asset managers worth their salt are continually on the look out for new ways to make the right investment decisions. Benchmark and reference data remains the key input for such decisions, but forward-looking investment firms are increasingly looking to alternative data sets to give them a competitive edge.
The term ‘alternative data’ covers a wide spread of information sources and includes that generated by mobile devices, satellites, CCTV, social media feeds, public records and the internet. By analyzing these ‘big data’ sets, asset managers hope to uncover hidden insights that will enable them to make the best possible investment decisions at any given moment.
However, as discussed at the recent Buy-Side Technology European Summit, the industry faces something of a skills gap that could act as a barrier to the uptake of alternative data. During a panel debate on the topic, Javier Rodriguez-Alarcon, European Head of Quantitative Investment Strategies at Goldman Sachs Asset Management, commented: “people need to have the specific skills to evaluate the data. Not everyone has the skills to build that infrastructure to evaluate big data”.
This is an important point, as the total spend on alternative datasets is projected to exceed $7 billion, by 2020, according to Opimas. Without the right data science skills, this investment will be largely wasted. Moreover, firms that have not been able to recruit people with the right skills may find they churn customers to asset managers that have improved their product performance by using alternative data successfully.
The good news for asset managers is that this skills problem can be overcome. However, to do so, firms will need to be willing to embrace new partnerships with technology and data providers. What is required is a cultural shift away from the traditional approach to technology and IT, where everything was built and run in-house, towards a managed data services model.
By working with a managed data provider, such as RIMES, firms can tap into a pre-existing source of data science expertise. This approach is significantly more cost effective than increasing in-house teams, as managed service providers can leverage economies of scale to keep costs low. Moreover, instead of having to buy new alternative data sets outright, firms can simply pay for access to datasets owned by managed service providers. This allows them to keep capital costs low while still being able to benefit fully from alternative data – and traditional benchmark data too, for that matter.
The data management challenge is only going to increase over the coming years as asset managers search for new ways to differentiate and drive cost efficiencies. Managed data services are increasingly showing themselves to be the best data solution for these requirements; providing the easiest and most cost-efficient way for firms to source the high-quality data they need to improve decision making.
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