Data Trends for Investment Professionals

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The Data Factory, Part Four: Deploying your data

Having wound our way through all of the sub-steps of the previous stages of the data factory (acquire, transform, apply), it becomes clearer how much specialization is required when building a modern alternative data practice.  Of course, if you really wanted to, you could have one person do the entire process. But it’s highly unlikely that doing so would be the best use of their time—or the most scalable option for the business. As a result of the above challenges, the data factory model is likely to become more and more popular. In this last installment of our series, we’ll...

The Data Factory, Part Three: Applying your data

This is part three of a series of posts exploring the Data Factory, the premier approach to data management. If you missed it, catch up on Part Two: Transforming your data here. In our previous post, we explored the second stage of data management: transforming your data into usable, clean information. The third stage in our data factory journey is where we begin to unleash the potential of the data that we’ve worked painstakingly to acquire and transform.  How to apply your data The application stage is where portfolio managers, research heads and quantitative analysts begin to determine how they...

The Data Factory, Part Two: Transforming your data

This is part two of a series of posts exploring the Data Factory, the premier approach to data management. If you missed it, catch up on Part One: Acquiring the right data here. In this post, we’ll tackle the stage that comes after finding the right data for your business’ needs: transforming the data so that it’s usable and reliable in application.  Data quality assurance The truth of the matter is that raw, real-world data can have a multitude of problems—it can be messy, noisy, unstructured and full of gaps, errors, outliers and duplicates.  Before portfolio managers and research heads...

The Data Factory, Part One: Acquiring the right data

This is part one of a series of blog posts exploring the data factory, the premier approach to data management. In the assembly line model that powers the data factory, as with many other things, well begun is half done. The first stage in any data process is acquiring the right data for your  organization’s needs. The process of acquiring data involves a number of smaller steps which include sourcing; contracting; extracting, transforming and loading the data; and business development. These steps are by no means linear. They can and often do happen simultaneously.  This stage is all about ensuring...

Capitalizing on ETF data: In conversation with Atom Finance

Atom Finance turned to Nasdaq APIs for ETF and fund data to help fuel their research platform. Learn more about their experience with Nasdaq’s data. If you were an investor in the 80s, chances are good that you couldn’t do without a Bloomberg Terminal. The system burst onto the scene in 1982 and has remained in the investment industry’s toolkit ever since, alongside an ever-growing collection of datasets and analytics tools that help investment professionals make well-informed decisions. Data used to be a tool accessible mostly to the well-funded investor. Now, data and other information is available to investors of...

The future of data privacy in alternative data

An interview with Peter Greene We had the opportunity to interview Peter Greene, Vice Chair of the Investment Management Group at Lowenstein Sandler LLP, on the topic of data privacy in alternative data. We cover the evolution of data compliance, current challenges in the regulatory scheme and how data privacy might evolve in the future. Comments have been condensed and edited for clarity. Looking back at your presentation from the 2020 Quandl Data Conference, how important is data privacy and data compliance for a hedge fund or data-driven investor today versus 5 years ago? Greene: A lot has changed. Five...

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