David Csiki

Proper analysis of big data can give asset managers the edge, but robots don’t necessarily hold all the answers, says INDATA’s David Csiki

How can asset managers best use data analytics to their advantage?

The latest technology offers asset managers big data analytical tools, which can give them access to a superior set of capabilities to those they have had previously. In the past, technology focused on relational databases, which required a lot of database administration and data warehouses, all of which was very labour-intensive.

For INDATA clients we have used big data technology to integrate and unify a wide variety of data sources, which is essential for investment managers. Big data tools can do this more effectively because of the way they’re constructed and configured, and the end result is that asset managers can have their data at their fingertips, unlocking it from the silo structures that were previously in place, and actually being able to use it. If an asset manager has access to accurate, high quality data that is up to date, then it could gain real advantages over its competitors.

How important is automation and streamlining of processes?

It’s a key part of it. Where asset managers are using various data sets, systems and everything else, they end up with a lot of breaks in the chain, so to speak. They end up constantly trying to round up, validate and utilise data when it’s all locked up in different systems. These processes could be automated and integrated and stored centrally in what we call the ‘data model’, where it can be accessed quickly and reliably, and used for various different things.

The whole concept of the model is to make these huge data sets easily accessible, and to put the data in the hands of the end users that need it. For example, if a fund marketing professional has current data and historical data at their fingertips, he or she has a distinct advantage over someone who has to round up that data, screen it and make sure it’s accurate. There are still some big gaps in terms of automation here.

Where does cloud technology come in to it?

INDATA was an early adopter of cloud technology. Our solution came out in 2011, so it’s pretty mature from an industry standpoint. We have a private cloud, as opposed to using a public cloud utility, because privacy is very important to our clients, as is their fiduciary responsibility to their own clients. We’ve rolled out this cloud model where clients’ data isn’t co-mingled and that largely satisfies their concerns over privacy and security.

Fundamentally, the cloud eliminates the technology requirements for asset managers. Instead of worrying about upgrading servers and maintaining their own systems, they can outsource that burden as part of a service level agreement. That’s attractive to them—their business is managing investments, not managing technology.

Over the long term, this can save them money as well. With use of big data taking off, the technology infrastructure that contains the big data tools can be stored in the cloud. That is a huge advantage, as trying to keep it all together in-house can be a huge undertaking to say the least.

What is the idea behind ‘robo-advisors’? Will they be effective long-term?

This is an emerging technology and there has been a lot of talk about it. Overall, however, at least among our client base, which is for the most part the institutional money-managers, I don’t see an encroachment of robo-advisor solutions or technologies in our space. What these managers are doing is very difficult; they actively pick their securities and their investments and a lot of the time their clients wouldn’t have it any other way.

Perhaps for certain segments of the wealth management space robo-advisory does have an appeal, for example in the context of passive investment advisory firms that are not necessarily chasing a superior return but rather just trying to keep up with the market. There are also some people who would prefer to manage their investments and monitor growth over time on a mobile device rather than talking to anybody, but I don’t think they will get the same returns or service that they would from going through a professional institutional money manager.

There are interesting applications to robo-advisory technologies, though. They utilise big data and artificial intelligence, and they use other back-end tools that are worth exploring.

Is there a balance to be struck between robotics and human advisors?

It makes a lot of sense, when you consider the goal of the technology. Technology is not there to replace the humans, it’s just a tool to help them. The trick is to get a good mix. There is also an element of trust to consider—an institution or a business is likely to demand the expertise of an experienced institutional adviser, and the relationship that comes with it. Even if the robo-advisors become statistically superior, it is human nature to want a relationship with the person or institution they’re trusting with their investment, and no machine can really provide that.

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