HOW TO ELIMINATE THE HEADACHE OF EVALUATING ALTERNATIVE DATA FEEDS WITH A SANDBOX TESTING ENVIRONMENT
Alternative data sets are as popular as ever as investors seek timely, reliable information that can keep up with the evolving markets and shifting trends. However, a common struggle hedge funds face when incorporating alternative data into their investment workflows occurs during the data evaluation and testing phase.
Due to the nature of alternative data — big data coming from unstructured sources not immediately ready for analysis by investors — determining if a data set meets your firm’s needs can be a time intensive process. During the evaluation process, a firm needs to coordinate accessing the data, understand the structure, and run sample analysis. With limited information, firms may feel like they’re flying blind.
7Park understands this struggle well. Our own data science team needs to go through the same evaluation and testing process to understand the value before we can launch any new data set to the market.
The Jupyter notebook open source technology offers an easy-to-use, flexible, and interactive environment for analyzing alternative data sets. Leveraging this technology and the robust work our data science team does to structure the data, understand the scope of the data and build sample use cases, we developed the 7Park Jupyter notebooks and sandbox testing environment to simplify and shorten the lengthy process of evaluating many large bulk data sets.
We’re not talking about saving a couple of hours — think in terms of days or even weeks. Before a firm buys a data set, it must test the data to determine if there’s a signal and confirm it will provide value to their investment decisions. With tens of alternative data sets to evaluate, moving through several different data sets to understand the makeup of each one and the type of insights within it is anything but quick. And if it takes too long to determine an individual set’s structure or understand the value, it’s on to the next one to save time.
Another potential obstacle to efficiency is accessing the large files during the data exchange process and ensuring both parties are leveraging familiar-enough tools and coding languages. The learning curve can be steep, particularly when alternative data is often delivered in a raw form for quantitative analysis — it’s not clear how to approach the data to find a signal.
Consider too that many of these alternative data sets are massive with numerous fields and entries. There are no shortcuts – understanding the structure, possible analysis, and the value for investors takes time. Our clients don’t have that same luxury of time. That is why we developed 7Park’s Jupyter notebooks and sandbox testing environment.
To explain how it works, let’s consider a hypothetical investor interested in Amazon, particularly what impact Amazon Web Services (AWS) will have on Amazon’s short- and long-term growth.
The investor tasks the firm’s team to find and source data sets that provide edge as an additional input into their investment research process. The data science team begins to analyze various data sets. That starts with incorporating numerous data sources into their systems and running complex algorithms to try and familiarize themselves with the data set. The analysts find out about a data set that tracks actual company spend on AWS products. So they secure access to a trial of 7Park Data’s Cloud Infrastructure bulk data set. Next, they have to spend hour after hour conducting exploratory analysis to understand what information is available and possible use cases. That is a lot of work. Or, at least, it used to be.
7Park Jupyter notebooks allow data scientists, and other teams supporting quantitative analysis methods, to quickly familiarize themselves with new data sets and perform initial analysis, using Python code written by 7Park’s data science team.
Within our Data Discover interface, users can request trial access to a data set of interest, triggering the creation of a sandbox environment they can use. Within Jupyter, they’ll have access to the data and our data science team’s expert analysis.
We empower our clients to create value from data with unique data and tools that fit their workflow. And we want their relationships with us to be collaborative. By knowing what environment a team is working in, 7Park Data can work with them directly – even write code alongside them.
We provide the foundation of analysis to accelerate the crucial, yet challenging step of validating the data. This enables our clients to jump in and start performing their custom proprietary analyses. The faster a data science team can begin its analysis, the faster it can provide the information the portfolio managers need to inform their investment decisions.
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