Posted on : August 4th 2021
Enterprises tend to employ data from external sources in their data strategy to convert insights into financial gain as they mature in their data journey. This external data comes in diverse forms. However, for enterprises, the most critical is public data.
Government entities and regulatory agencies like the Office of the Assessor, Securities and Exchange Commission (SEC), and Secretaries of State to company and consumer websites, forums, and social media, have vast public data repositories. This public data has an array of intelligence that can be used across industries, domains, and functions to gain actionable insights. Hence, there is a burgeoning appetite among enterprises to incorporate public data into their data strategy. Nevertheless, the data needs to be extracted, massaged, and structured to be of any use.
There are many challenges in leveraging public data for gaining insights. For instance, it is unstructured and raw. Most of the content in the public space is not meant for structured consumption and analysis. The content has been developed as research-based and informative sources. The publication frequency is also highly variable, ranging from regular to annual, half-yearly, quarterly, to sometimes daily and hourly frequencies. Ordinarily, there is no specific cadence, standard, or format to enable data processing efficiently.
Further, with a vast number of sources and virtually anyone and everyone publishing content, it is an arduous task to establish the veracity of the published information. In addition, the quality and consistency of public data are poor. Besides, enterprises find it challenging to discover specific data in published content in subpages rather than on its homepage.
Straive’s public data intelligence solutions enable enterprises to overcome the challenges associated with acquiring, enriching, and managing public data. The solutions, enhanced with source, process, and technical knowledge gained from processing public data at scale for hundreds of thousands of sites, can accelerate the incorporation of public data into your data strategy. Straive’s solution experts combined with the Straive Data Platform (SDP) help create valuable intelligence from picking the right source to extracting, structuring, and enriching public data. Our solution delivers organized data from websites, news articles, data feeds, and regulatory websites.
Straive’s public data intelligence solution has been used across information services, banking, insurance, and the real estate sectors in a multitude of use cases, including:
Straive Data Platform is the foundation for our public data intelligence solution. The platform comprises multiple customizable microservices to solve unstructured data use cases at scale. The Straive Data Platform includes strategic frameworks, accelerators, tools, scripts, and technical capabilities, in addition to customizable extraction, transformation, enrichment, and delivery modules.
For the functional scope of activities, the Straive Data Platform comprises various functionalities to acquire, enrich, and manage unstructured data across multiple touchpoints. Enterprises employ the platform's patented architecture, which separates compute and storage, to speed up the data crunching and analysis process.
The process of data extraction involves identifying and recovering alternative and semi-structured data from various data sources such as files, XMLs, JSON, etc.
Capital markets are an excellent example of a perfect competition. The nature of the market is such the participants have to be competitive and result focussed. For instance, brokerages and investment banks have to deliver passive gains for their clients and, at the same time, earn a margin for themselves.
Today’s ESG analytics require processing data, patterns, and hidden connections to provide insights that investors, asset managers, and companies need. For example, Straive deploys advanced machine learning algorithms to analyze reams of documents to collect evidence across executive statements for signs of vagueness or obfuscation.
Talking about using data to gain insights is easy. But actually doing it will uncover a newer set of challenges, especially when it comes to unstructured data.
Integrating ESG data into commodities trading operations requires structured, easy-to-consume data. By their nature, ESG data resist such integration, and highly scalable data solutions across the data life cycle are needed to allow stakeholders to deploy end-to-end data solutions for a successful data-to-intelligence journey.
Our solutioning team is eager to know about your challenge and how we can help.