Investment management is a heavily data-driven industry—portfolio managers and investment researchers require a large number of data sources to guide them in shaping their investment strategies. 

New cloud capabilities and technologies enable investment managers to process data faster than ever before and iterate on ideas quickly to fuel innovation in the signal generation process and gain a competitive edge. 

Using the cloud for investment research workflows makes it easier to onboard data from data providers, spin up large compute workloads in the midst of market volatility or during heavy research cycles, and manage complex machine learning or natural language workflows to gain market insights.

We hear from industry leaders that they’re exploring new ways to run investment research. “Differentiated investment strategies require new types of information sources, and new ways to process that information,” David Easthope, senior analyst, Market Structure and Technology, Greenwich Associates. “And that, of course, relies heavily on having access to reliable and scalable storage, computational, and AI / ML resources. More specifically, quantitative strategies can benefit from the computational platforms and embedded AI/ML capabilities the cloud can offer.” 

Google Cloud gives investment managers essential components to work and operate faster as they bring their investment research workflows to the cloud. Here are the key highlights:

1. Simplify, speed up your data acquisition, discovery, and analytics

The foundation of any investment strategy starts with data—acquiring it, detecting patterns, and analyzing it for insights. Enabling data providers to easily share large datasets such as tick history within a high-performance analytics engine can greatly reduce the data engineering overhead when possible.

Once data is onboarded, you can tag business and technical metadata related to your datasets and provide portfolio managers the ability to discover these datasets via a search interface.

We further review analytics options for various scenarios, including aggregating massive datasets, creating dashboards, and incorporating streaming analytics workloads.

2. Take advantage of burst compute workloads

Data engineers and researchers require ready access to burst compute capabilities to perform backtesting, portfolio simulations and run risk calculations. Cloud works well for these workloads due to its elasticity, consumption-based models, and hardware evolution.

Many investment managers are shifting to a container-based strategy along with a Kubernetes-based scheduler for greater consistency, scaling and efficiency in environments with a large number of researchers. Cloud managed services and a rich suite of CI/CD tools can make this vision a reality while improving security and developer productivity.

3. Tackle machine learning (ML) and model deployment with the help from cloud

Quantitative researchers scour vast amounts of market and alternative data sources searching for signals and correlations, while ML engineers have the challenge of taking these signals and moving them to production.

Google Cloud empowers users to create and operationalize their models without wasting valuable time with a comprehensive set of MLOps tools. 

In this paper, we explore multiple solutions for ML and model deployment. Those capabilities reduce the amount of time operationalizing ML models, so quants and data scientists have more time to devote to differentiating activities. 

4. Get the data you need in less time with Natural Language and Document AI

Thousands of financial filings, news articles, and sell-side research reports are generated every day, and it’s difficult for humans alone to process this volume of information. These documents are often generated in many languages and the ability to do entity recognition, sentiment or syntactical analysis in those languages, or perhaps translate them into the language of the portfolio manager is of critical importance. Google Cloud provides these capabilities through pre-trained models, or allows you to train high-quality models with your own datasets.

Getting started

There are plenty of emerging technologies, tools, and approaches available to help investment managers today. At Google Cloud, we can help you access, organize, and utilize these essential components to make your research faster, reliable, and more valuable.

To learn more about these four keys to better investment research, check out our whitepaper for more.

Oden Technologies is using the Internet of Things (IoT) to improve the factories of today. The giant network of “things” (including people) connected to each other via the Internet has the potential to reduce waste, increase efficiency, and improve safety across all walks of life. Oden is leading IoT innovation in manufacturing by combining wireless connectivity, big data, and cloud computing.

The use of data to improve manufacturing is practically as old as manufacturing itself. But the computerization of manufacturing has resulted in broad and rapid changes to the way data is collected and processed, as well as the sheer volume of data available.

Oden’s goal is to help manufacturers tap into this data to quickly identify process trends and even warning signs of machine breakdown. Such visibility can reveal opportunities to improve manufacturing and maintenance processes that reduce waste and increase profit margins.

Oden designs and develops data collection devices that can plug into almost any kind of machine and can wirelessly transmit data with minimal complexity and setup time.

Once devices are installed, the Oden technology platform processes data to give manufacturers cutting-edge analytics that are easy to comprehend. Analysis produced by the platform provides factory engineers with data points such as detailed root-cause analysis down to the second, factory-wide performance in real-time, and trend analysis.

Improving cloud delivery

Oden’s previous cloud platform performed satisfactorily, but the company evaluated alternatives in search of potential reductions in cost and complexity and increases in performance.

When evaluating Google Cloud Platform, Oden discovered it would require fewer virtual machine (VM) instances for equivalent performance, which would cut costs. Furthermore, Oden could gain more sophisticated data analytics and machine learning capabilities compared with its existing cloud provider.

Today, Oden runs its entire platform on Google Cloud Platform including Google Compute Engine, Google Cloud Pub/Sub, Google Cloud Bigtable, Google Stackdriver, and Google Kubernetes Engine.

“In order to serve our customers, we need a cloud platform that can scale reliably while keeping costs low, perform under heavy loads, and consistently deliver sophisticated features such as machine learning,” says Willem Sundblad, CEO and Founder at Oden Technologies. “Google Cloud Platform is way ahead in all of these areas compared to our previous cloud provider.”

Capturing tens of millions of metrics a day

Using Google Cloud Platform, Oden can help an average factory capture and store approximately 10 million metrics on a single manufacturing line every day.

Metrics can include extremely granular detail, such as the amount of electricity going to machines, the amount of raw material consumed, and the volume of material produced. Sensors can also capture and transmit environmental information such as temperature, humidity, and dew point so that manufacturers can identify weather-related and seasonal impacts on production.

The updated Oden Cloud Platform uses Kubernetes Engine—powered by the open source Kubernetes system—to run application program interfaces (APIs) that capture data from Oden’s wireless devices on the factory floor.

Google Cloud Pub/Sub then sends the data in real time to Google Cloud Bigtable, where data is processed using Oden’s proprietary analytics tools. Google Stackdriver supports Google Cloud Platform monitoring, logging, and diagnostics, which help Oden deliver its cloud platform with confidence.

Oden Technologies builds dashboards powered by Kubernetes Engine, which pull analyzed data from Google Cloud Bigtable. The dashboards provide customers with real-time visibility into their manufacturing lines. Oden Factory Cloud dashboards allow customers to delve deeper into their data to fine-tune production processes or discover the root causes of production issues.

With the previous cloud provider, Oden required 80 VM instances to run the dashboards. With Google Cloud Platform that number has been cut to 45, which dramatically reduces costs and complexity.

“We migrated from our previous cloud provider to Google Cloud Platform in just one month,” says Willem. “Further, our storage and data analytics costs have decreased by 30%. Cost savings like these allow us to protect customers from rising expenses, keeping us focused on bringing the best products possible to market.”

Faster data access; more efficient factories

With Google Cloud Platform, Oden can now deliver a complete factory analytics picture to manufacturers. In environments where thousands of variables affect the bottom line, businesses can now automatically and perpetually record machine and performance measurement. Oden Factory Cloud gives customers access to comprehensive data insights and can eliminate reliance on onsite infrastructure investments to run their own analytics.

Because manufacturers have access to live data and can analyze production data quickly, they can troubleshoot and resolve problems in minutes rather than months. Such information helps improve product quality, minimize unplanned downtime, cut costs, and improve profitability.

“With the help of Google Cloud Platform, we are helping our customers to be data-driven, which wasn’t possible before,” adds Willem. “They now understand that data is their most important asset. That allows them to be more innovative and continually improve their production processes.”