Asset management has always been an information-based business and as such, managers are not immune to facing innovative and disruptive forces making inroads in other industries.
Such is the pace of technological innovation, and data volume, that fund managers now have the chance to leverage the same predictive capabilities as used by Google (and other search engines) into their organisations to better understand investor behaviour and derive insights across all facets of their business. But it’s no easy task. To do so requires having a clear data strategy in place to build value and ultimately improve the client experience.
“We live in an age of data abundance,” says Paul Schaeffer, President of Alphahut and former senior adviser at SEI’s Investment Manager Services division. “To some extent, all businesses are becoming ‘Googlised’. The challenge for asset managers today is not how to gather the data but rather how to extract value from all the data they now have at their disposal.”
What do we mean by Googlisation?
As SEI points out in its white paper, “The upside of disruption – why the future of asset management depends on innovation1”, the power of search algorithms, coupled with the plunging costs of bandwidth, data storage, has us all a limitless window on the world.
As fund managers attempt to respond to the pace of technology, the key question they need to ask themselves is: How do we extract value from all this data?
“Fund managers need to have a data strategy to figure out what to do with all this data,” says Ross Ellis, Vice President and Managing Director of the Knowledge Partnership in the Investment Manager Services division at SEI.
It’s all well and good collecting data but if you don’t organise it in such a fashion that you can derive insights from it, then what’s the point? It’d be almost like not even getting it in the first place.
“You don’t know what you don’t know, and therefore you might not realise that there are links between one data set and another until you actually dig in to it. That’s why some fund managers are hiring data scientists. Managers today are able to collect lots of seemingly unrelated data – some of it unstructured – yet they do not necessarily know what to do with it all.
“That ends up being part of the data strategy problem. Someone has to define what you are going to do with it. The operations or IT team will not necessarily be the best people to leave it to, however, because they will tell you what they can do with it given today’s technology, not provide the insights that front office teams are looking for, or necessarily have ‘blue sky’ thinking,” says Ellis.
The volume of data that Google processes on a daily basis is staggering. Google now processes over 40,000 search queries every second on average. This translates to over 3.5 billion searches per day and 1.2 trillion searches per year, worldwide. Additionally, IBM estimated that about 80% of the data available is unstructured. In early 20162, Scott Gnau, CTO of Hortonworks, said that one of the data and analytics trends to look for this year is that businesses would look at deriving value from all data. “It’s not just the Internet of Things but rather Internet of Anything that can provide insights,” he said.
To Googlise their businesses, therefore, fund managers need a strategy that enables them to leverage what Stan Davis and Jim Botkin wrote in a seminal paper for the Harvard Business Review in 1994 entitled “The Coming of Knowledge-Based Business”. In short, they argue that there is a hierarchy of data to information to knowledge. Information is data that has been arranged into meaningful patterns, said Davis and Botkin, with knowledge deriving from not only the composition of information but the skill that one is able to apply to that information.
Ellis says that using data should not be a one-way street but a way to drive discussion with a client, potentially evoking further questions. “Dialogue, not diatribe,” he comments. In other words, managers with a data strategy in place will be better able to drive insights and improve both the way they run their businesses and provide better solutions and communications with their investors.
Moneyball
“Additionally, when your company is based on data, some of the things that you thought may have been true before can now be disproved (or not) given the facts. That can end up changing or enhancing a fund manager’s marketing story, creating a more targeted sales pitch. Think about the Billy Beane story in the major league baseball arena. If you are a huge team with an enormous budget you just go out and buy the best players, and even keep some on the bench that could be starters elsewhere.
“If you’re small, and you have limited budget, you have to look at other ways to compete. That’s what Beane did with the Oakland A’s. They used analytics to crunch numbers and look at things that the naked eye couldn’t see. It put them on an equal footing with the much bigger payroll teams,” relates Ellis.
This is the classic ‘moneyball3’ approach and is now being utilized by teams with the biggest budgets as well as those with the smallest as they seek to take advantage of data to pick players and build winning teams.
Likewise, large asset managers like Schroders and Fidelity had the foresight to be ‘data smart’ and having algorithms to analyse data is now used part and parcel with their human workforce. The more they use data tools to make the customer experience better, the more they are able to build deeper insights into the characteristics of investors; just as Google learns about us the more we search for things.
No more guesswork
Google’s algorithms make links and create predictions every time we type in a search. These things are only possible because of the extraordinarily vast amounts of data that Google has at its disposal. The same applies to other platforms, such as Facebook.
“I read something recently that Facebook has close to 100 bits of information on the average person. And that is data that we willingly give them in set-up and as we post messages on the platform. Facebook’s algorithms put together a rules puzzle; for example, Ross went to school in the south of England, lives in a 2000 ft house and likes The Beatles, therefore he’s more likely to like Crunchie Bars rather than Yorkies (both British chocolate bars). Step-by-step, and adding up all the data from all the users in their database, these algorithms build personas and learn about us by making suggestions, often to a very accurate level,” says Ellis.
This is a simple example, but what the advertising exchanges do when they autosuggest a pair of Oakleys, for example, after you’ve just looked at sunglasses on Amazon.
“The idea is that these custom ads and “recommendations” improve our customer experience and make it easier. In the asset management industry, a data-driven infrastructure could allow managers to cut out a number of steps and arrive at a quicker decision, enabling a faster go-to-market strategy and a much more bespoke approach to communicating with the end user.
“It’s not just guesswork anymore. An effective data strategy can tell you, with a higher level of probability, what types of managers investors are going to be interested in based on the performance attributes and managers they have allocated to in the past. By following a clearly defined data strategy, it has the potential to transform fund managers of old into more nimble client-centric organisations,” says Ellis, but it takes time and commitment to achieve this.
Finding talent
To become Google-esque in their approach, fund managers will need the right talent in place. And that means hiring data scientists and analytics experts from diverse fields, including math and statistics-focused universities and Silicon Valley, just as sports teams have done to great effect.
Various industries such as telecoms are hiring data scientists and analysts to make the transition into becoming a knowledge-based business and embrace the “machine learning” paradigm that is fast underway but fund management still has a way to go.
Depending on a firm’s cultural orientation will ultimately determine the extent to which they build internal capabilities – be it through hiring data scientists, a Chief Information Security Officer etc. – and/or partner with external service providers.
Asset managers should look at all the entities they work with – fund administrators, custodians, risk providers, brokers – and ask, ‘Do they have the same orientation around data and knowledge and, therefore, can they support my knowledge platform?’ Are they going to deliver the data I need, do they have individual skill-sets that I can leverage, will they make my organisation, and thus the value I can offer my clients, better?
“If you can get the right talent in place you can unlock opportunities that you didn’t know ever existed before. You don’t need to have all the talent in place right away; the main thing is that you have a strategy to help you become better tomorrow than you are today, and a commitment from the top to support this going forward,” concludes Ellis.
1: http://www.seic.com/futureofassetmanagement/disruption-today.html?cmpid=im-udarhw-16
2: http://www.cio.com/article/3023838/analytics/21-data-and-analytics-trends-that-will-dominate-2016.html
3: The phrase ‘moneyball’ was coined by Michael Lewis in his book, “Moneyball: The Art of Winning an Unfair Game”
