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The big impact of big data 2. In particular, big data is making headlines in a big way. Rich Mathieson discusses how big data is transforming the way we think about investing. View transcript Liz Koehler: The world is awash in data. In this episode of The Bid, we speak to the expert on how big data is transforming the way we think about investing.
Rich, thanks so much for joining us today. Take us back just a few years ago: I will take it back further than a few years.
The Systematic Active Equity Team at blackrock have been combining data and technology with traditional investment insight for as long ago as 30 plus years. And I think really through a lot of that period, the big constraint we had to generating alpha in client portfolios was data.
Today that is no longer the case. Think of something you would want to measure electronically and the chances are somebody in the world is already doing that. And it required a lot of work to turn it into useful information but we felt if we could do that we would have a real edge in terms of investing.
And speaking of all of that data, last year inyour team trailed over 70 new datasets. What kinds of data does the Systematic Active Equity Team really look at, and then how does your team analyze those massive amounts of data to really gain those investment insights that you referenced?
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We have a general philosophy that there is no such thing as bad data. We want to look at as much data and information as possible to ultimately try and answer the traditional questions that any investor would ask of the securities or stocks that they invest in. Today that information is electronic.
We can capture it, we can use it to measure how well securities and companies are doing. Information from social media, information from internet search—think about the way we all use the internet today. Geolocation data that helps us analyze consumer aggregate behavior in terms of actual physical location, in and out of retail locations, such as shops and stores.
There has been a lot of publicity around tech firms recently that have access to personal data of their customers.
How does your team think about that in the data that you touch? I think that the big differentiation of it highly is what we are interested in compared to what a lot of technology firms might be interested in.
But it seems that some of these managers could easily buy some of this data or the technologies in order to analyze it off the shelf. And I think you hit the nail in the head earlier, SAE researchers last year trialed around about 70 different datasets. Many of those will be publicly available, anybody could get hold of it, anybody could buy it.
And only firms with an ability to leverage a technology platform and they can accelerate as much data as possible over that platform, will be able to answer those types of questions successfully. So size and scale is important in this game. The second point I would make is again, this data even when you acquire it from an often third party vendor, is messy, noisy, unstructured, a lot of work is required to refine and curate that data and ultimately map it onto an easily tradeable security for you to actually build that information into a client portfolio.
And we have a lot of years of skill and experience and the talent required in order to make that transformation. Rich, the Systematic Active Equity Team has been analyzing Big Data to enhance its investment outcomes with technologies like machine learning for almost a decade.
What are some of the necessary ingredients that you think asset managers need to ensure they are actually using this Big Data most effectively? The first I highlighted earlier is size and scale is important and an ability to bring as much differentiated data to bear on the same investment question as possible.
So for example, I talked about internet search earlier is giving you a very nice early warning of an intention across large groups of consumers towards particular brands and products. The problem being there that I think that gives you just an intention rather than get you close to hard transaction activity.
One of the best examples I think we have of that is the way that abilities in natural language processing have evolved over the last eight to ten years.
Original algorithms that we ran to build models that essentially enabled us to read text which is very rigid preprogrammed dictionaries of words.This material is intended for U.S. distribution only. This material is for informational purposes and is prepared by BlackRock, is not intended to be relied upon as a forecast, research or investment advice, and is not a recommendation, offer or solicitation to buy .
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