As big data continues to transform the financial services industry, alternative data use cases are being leveraged by data analytics professionals. Alternative data refers to sources of data that have not traditionally been used to inform financial institutions’ investment decisions, help manage risk, gain insight into consumer intelligence, and more.
The data science technology required to leverage alternative data has improved exponentially in recent years, and has enabled an increasing amount of financial institutions and corporations to start enhancing their operations.
Who Uses Alternative Data?
In terms of the finance sector, alternative data is used by financial institutions and companies alike to discover market and consumer intelligence previously unavailable or inaccessible to them.
The financial services industry is at the forefront of alternative data usage, and asset managers use it following quantitative or fundamental research approaches.
- Quantitative research approaches involve using data science like machine learning and AI technologies to decipher data into actionable insights and automate processes, while fundamental research approaches have limited data science tools and technologies. Quantitative portfolio managers, researchers, and data managers are capable of integrating alternative data sets into their existing systems, and quickly start extracting insights from it.
- Fundamental asset managers typically have limited data science capabilities, and must integrate alternative data in their existing, traditional systems. They have the biggest opportunity to adopt new technologies to enable them to use alternative data, and gain competitive advantages that alternative data insights uncover.
By combining their traditional research methods with data science technologies and data analytics experts, fundamental asset managers are increasingly becoming a blend of the two approaches known as quantamental.
Corporate strategy departments, financial planners, and marketers use alternative data to improve the quality of their market research. Whether they’re looking for spending trends in a particular market, or consumer and competitor intelligence, leveraging alternative data sources adds a deeper understanding behind all of their financial analyses, and helps automate their processes.
Applications of Alternative Data in Finance
Leveraging alternative data sources has not always been easy or affordable for companies and financial institutions. Over the last decade, data science technology wasn't always so accessible or even developed yet.
Advances in technology coupled with the mass amounts of data sources and data types ushered into the finance sector with big data made it easier and cheaper to access data. This alignment in the industry enabled financial institutions and companies to start using alternative data sources to increase the quality of their data analytics and business insights.
With deeper data insights into market and customer behaviors more than ever before, financial institutions have the potential to gain unforeseen market and competitive insights to help them get ahead of market and consumer reactions.
Additional applications of alternative data for asset managers and corporate financial managers are listed below.
Asset Management & Investing
Alternative data in asset management and investing is used for a variety of use cases that include the following:
- Uncovering predictive revenue signals
- Gaining insights into payroll trends
- Managing risk using spending data analytics
- Better managing investments using comprehensive consumer spending trends and income data analytics
Predicting sales, recessions, and future purchase intention in various categories is made possible for investors ready to take advantage of alternative data. Envestnet | Yodlee is the market leader in data aggregation and provides investor data solutions to asset managers looking to obtain these valuable insights.
Corporate data is used to help companies strengthen their market research against competitors, and help better inform corporate financial decisions.
Companies can use alternative data for various uses cases, including:
- Identifying strategic growth opportunities
- Monitoring the competitive landscape with consumer spending trends and income data analytics
- Understanding brand affinity and subscription changes
Other Alternative Data Sources
In addition to financial institutions and companies, government policy makers are also increasingly using alternative data to help with department planning and reports. Other alternative data sources available to them include:
- Credit Card Transaction Data
- Email Receipts
- Web Site Data/Screen Scraping
- Satellite Imagery
- Social Media Posts
- Online Browsing Activity
- Product Reviews
Challenges of Using Alternative Data Sources
Financial institutions and companies face a number of challenges when using alternative data sources. The challenges revolve around the acquisition and actual use of the data.
While evaluating alternatives data providers, it’s important to keep in mind that the quantity of alternative data available does not always equal quality. Not all providers of alternative data manage their databases themselves, and are often third-party sellers of data managed elsewhere. Resellers are not always as reliable as direct data sources in providing consistent data quality and supply.
By not checking the data sources they’re looking to purchase for consistency and quality, financial institutions and companies also risk investing in data that fails protection of privacy and security protocols. Alternative data providers should follow stringent privacy and security guidelines that buyers should evaluate before any acquisition of data.
The quality of data is also important from a use and integration perspective. Apart from being unstructured when originally sourced, alternative data sets can often be left incomplete, and require additional processing or data to fill in gaps that’re crucial to extracting any insights at all from the data.
AI and machine learning powered technologies help fill in incomplete data sets, and ensure the data is accurate and manageable for accurate insights. Since historical data is rarely available to use to verify the accuracy of a dataset, the speed in which AI powered software can verify data quality is extremely beneficial.
One of the biggest challenges financial institutions face using alternative data sources is with integration. The unstructured data models of alternative data sources are not compatible with most legacy systems. The extra effort to structure the data into existing systems discourages some financial institutions from leveraging alternative data, but with the right team of data scientists and reliable data provider, results can be achieved from extracting the information. Read our eBook to learn eight strategies for evaluating alternative data.
Alternative Data in FinTech Made Easy
FinTech companies like Envestnet | Yodlee makes integrating and understanding alternative data sources easier.
Financial institutions and companies can avoid alternative data silos that prevent getting any real value from the datasets by using the analytics and insights from our data aggregation platform. Envestnet | Yodlee ensures alternative data is of the highest quality by cleaning, enriching, and clarifying the data using AI and machine learning to extract the maximum amount of information from it. We adhere to leading industry practices for data security, regulatory compliance, and privacy, and do not sell data that identifies consumers.