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September 20, 2024
An investment banking firm wanted to unearth opportunities faster and generate more business by making its research process more efficient.
The research analysts at the firm scoured through mountains of structured and unstructured data to find useful insights on risk factors, growth trends, market landscape, etc. It involved manually:
An investment banking firm wanted to unearth opportunities faster and generate more business by making its research process more efficient.
The research analysts at the firm scoured through mountains of structured and unstructured data to find useful insights on risk factors, growth trends, market landscape, etc. It involved manually:
The firm was looking for a solution to automate the monotonous aspects of research so that its analysts spent more time analyzing data to extract valuable insights.
Since the research process was mostly manual, the analysts at the firm spent at least 30 hours each month merely looking for data and preparing it for further analysis.
As a result, the firm faced challenges such as:
Analysts would spend several days browsing news, announcements, and other real-time information in their Google Alerts.
Using Google Alerts, search, or RSS feeds to track every trend or development meant sifting through the noise – hundreds of links with low-quality information. Moreover, there was no mechanism to save the important links in an organized, searchable manner.
Additionally, they would go through paywalls to access in-depth, hundred-page reports and read the entire report to spot trends, patterns, investment risk factors, etc. Understanding these resources, summarizing their contents, and finding themes or patterns was a tedious, painful process that took days, even weeks.
Metadata offers insights on data flow, lineage, origins, and transformation within an organization. However, manually compiling data sources and extracting metadata is an expensive, time-consuming process, even for structured data sets.
Unstructured data comes in various formats (XML or JSON) and, in such cases, the metadata cannot be extracted without using automation and ML algorithms.
Timely insights are critical to spotting and capitalizing on investment opportunities. However, reading each and every line of hundred-page equity research reports or tracking each column in an investment portfolio spreadsheet isn’t quick or scalable.
The firm decided to adopt Needl.ai and leverage its intelligent algorithms and NLP-powered search to speed up the research process. Within days, the firm witnessed the following benefits:
With Needl.ai, analysts could run natural language keyword searches to find the data they needed within documents, images, RSS feeds, media files, and more across various platforms and applications.
Analysts could combine keywords to find specific search results using Boolean functions.
They could refine the search results using advanced filters such as date, source, sender, chat groups, email domain, and more.
Additionally, they could save these searches as custom feeds to avoid running the same search queries repeatedly.
Needl.ai ensured adequate context with auto-summaries generated by analyzing the meta description, keyword occurrences, topic titles, author, data type, and other metadata. So, analysts could capture the core theme or intent of a resource without opening it.
Analysts could track companies, people of interest, themes, industries, topics, and more by setting up data feeds across the sources of their choice. In addition, they could track specific keywords using these live data feeds that get automatically updated in real-time. As a result, the analysts didn’t have to spend time organizing and managing them.
The feeds can be shared across the firm, making it easy for analysts to share and discuss their research with other stakeholders.
Once set up, Needl.ai classifies data using system tags and auto-populates essential fields such as data type, author/owner, source, and date. Users can also create new tags to sort data further.
In addition to tags, users can clip sections of a report and save it as an Excel or a Word document within Needl.ai.
Needl.ai also enables users to clip and convert paragraphs, tables, and images from PDFs to word documents or Excel spreadsheets. The clipped content is available in the Clipped Reports tab.
As a result, analysts don’t have to manually copy and paste the content into a separate document and then upload it to the cloud. Instead, Needl.ai automates the entire process and ensures that all such documents stay inside Needl.ai, rather than in separate folders scattered across devices.
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