Big Data & Asset Management

As the world has gone digital, the amount of data that is generated from online activity, smartphone use, satellites, and other devices only continues to grow in both volume and complexity. This data is known as Big Data, and it has helped fuel innovation well beyond just the financial sector because of the deep insights that can be extracted from it.

Big Data refers to various types of data varying in structure and complexity within the same data set, making it more difficult to process without the help of data analytics software. Data sets are simply a collection of data, but no two data sets are exactly alike in their structure, volume, and type.

In the financial sector, big data and asset management go hand in hand in helping asset managers make better investment and risk management decisions on behalf of their clients.

The Role of AI, ML and Big Data in Asset Management

AI and Big Data in asset management has become increasingly critical for asset managers to do their jobs most effectively. Investors have been able to leverage big data to predict sales, recessions, and future purchase intentions across categories. Such precise predictions used to be only dreamed about, but innovations in technology have made them increasingly possible.

With an increase in the number of people investing in passive funds vs active funds, such as hedge funds, providing longer-term investment strategies backed by big data is becoming more important. Leveraging aggregated and de-identified financial data helps reduce asset managers’ biases from investment decisions and improves the quality of risk management.

Alternative Data in Asset Management

Alternative data in asset management goes beyond traditional data and refers to types of data that have not been primarily used in the past to inform investment strategies. Big Data makes these types of data sets more accessible to investors.

In the past, institutional investors and investment managers would look at fundamental market data like company filings or earnings to inform their investment strategies. Investors are now looking at alternative data sets such as credit card transactions, survey data, weather forecasts, social media data, and more to make their decisions. Big Data and data analytics software make this possible.

Certain strategies that investors are using now to collect alternative data, like web scraping or crowdsourcing, will likely see the advantages they offer slowly fade off as they become more common practice in asset management. Bloomberg reported in 2019 that fund managers globally may spend over $1.7 billion a year just on alternative data sources by 2020. The sooner investors invest in Big Data, the sooner they are able to reap the benefits.

3 Powerful Ways Data Can be Used by Asset Managers

With the large quantity of data available to asset managers, planning how to acquire the data, analyze it, and extract actionable insights from it is crucial to successfully integrate new tools and technologies into their existing systems.

1. Data Science and Acquisition

Data science skills are being used more frequently by financial services organizations to aggregate data from various sources and create predictive models to improve decision-making.

Due to Big Data’s complexity and mix of structured and unstructured data models, data scientists are needed to extract any meaningful insights from data sets. More and more investors are using data science, which includes data intelligence like AI and machine learning, to understand data they otherwise wouldn’t be able to use to drive returns.

2. Insightful Advanced Analytics

Asset managers are using advanced analytics to enable portfolio managers to access and understand previously unreadable data. Data visualization and customized dashboards help asset managers uncover investment patterns based on consumer behavior, transaction data, payroll data from companies, and more.

Asset management firms can leverage pre-configured and pre-selected key performance indicators (KPI’s) created by industry-leading data science teams. The visualized models don’t require in-house data science teams to create and are easy to integrate with existing data analysis workflows.

3. Daily Revenue Signals for Revenue Prediction

The quantity of data available also makes it possible for asset managers to receive daily revenue signals they can use to drive corporate or sector revenue predictions. Connecting market activity and consumer spending trends to predict revenue and earnings is made simpler and faster.

With the amount of data available, however, there is also the need for proven accuracy and effectiveness. As an increasing number of companies leverage Big Data, whether or not it can add Alpha to investment decisions is a major consideration for asset managers.

By having access to these signals and alternative data sources, firms have an ongoing way to more effectively make portfolio allocation decisions, manage risk and reduce the likelihood of falling behind competitors with more established data analytics solutions in place.

The Future of Data Analytics in Asset Management

Data Analytics in Asset Management is undoubtedly a big topic moving forward. Asset managers who are unable to use big data technology and leverage data to enhance their market research, using big data for investment decision making, assist with risk analysis, and other capabilities are likely to struggle to stay competitive in coming years.

The leading types of alternative data most companies are already using or plan to start using include web scraping, crowdsourcing, credit cards and POS systems, social media sentiment analysis, search trends, and web traffic.

It is up to asset managers to discover different types of data and how to best leverage it for their clients. Asset management companies are hiring more data specialists for in-house expertise that a few years from now could make all the difference in their ability to keep getting the most out of Big Data.

Moving forward, data intelligence software like artificial intelligence (AI) and machine learning will continue to streamline investment processes across the board for asset managers, and will likely leave them with more time to help a greater number of clients.

It is also important to remember that the longer data science technologies are refined and matured, in addition to the growing amounts of data and data types being generated worldwide, the better they’ll become at extracting actionable insights and deciphering the most complex of data sets.

Why Asset Managers Should Partner With Envestnet | Yodlee

Envestnet | Yodlee is a market leader in data aggregation and helps asset managers gain better data-generated insights into their current and potential future investments on behalf of their clients with alternative data analytics. Whether it be for business insights, consumer insights, or market trends, or more, Envestnet | Yodlee partners with asset managers looking to modernize their investment processes using real-time and relevant market spending trends data that are de-identified and secure to protect consumer privacy.