Challenges

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Research analysts spent most of their time – 20 to 30 hours each month – looking for data and figuring out how to share it. This translated to thousands of dollars lost in merely searching for and organizing data for the firm.

Moreover, the data found and shared could have multiple versions and lack the essential context needed to make informed decisions. As a result, the firm faced challenges such as: 

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1. Information overload

Keeping track of critical information pouring into the firm’s various communication and data management platforms in real time was challenging. The data deluge led to an information overload, with the team struggling to stay on top of everything.

2. A loss of valuable information

In other cases, the data shared would get lost or become unavailable if an analyst wasn’t online. For example, file attachments shared over chat groups weren’t available for download at a later stage. 

3. Siloed data

Even when the right data gets downloaded, it’s stored in an analyst’s personal device rather than a centralized cloud-based repository. Over time, such practices lead to data silos and make data sharing more challenging.

4. An inability to analyze data in real-time

Processing thousands of research reports and real-time data from stock exchanges to create a knowledge graph was a herculean task. Moreover, relying on numerous scattered tools and manual intervention made data processing and analysis inefficient and error-prone.

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How Needl.ai solved the productivity problem

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The firm took Need for a test drive and the results were immediate. After setting up Needl.ai – within an hour, the firm had:

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1. A centralized knowledge repository for historical and real-time data

Needl.ai integrated data from emails, chat groups, social media channels, storage platforms, and even meta searches on popular search engines in a single cloud-based repository.

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All existing documents, media, and links were now on Needl.ai, rather than residing in the personal devices of analysts. Needl.ai also enabled the firm to set up crawlers that continuously collect news, updates, and announcements from exchanges, regulators, company blogs, news sites, and more. 

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As a result, the firm removed data silos and brought all of its data under a single roof. Users with the right credentials could instantaneously view, download, and share any data. 

2. A Google-like search engine for data

Using Needl.ai’s powerful, ad-free search engine, the firm could search for data across apps, platforms, websites, and multiple document types (audio, video, or images). Users could quickly pull up granular pieces of information on any company, industry, or topic.

The algorithm ranks search results in the order of contextual relevance. 

So, users could quickly sift through search results to find the data they need, without opening each file, link, message, or PDF. 

Lastly, users can fine-tune their search by filtering the results by date, source, sender, chat groups, creation date, and more to cut the time spent searching for data substantially.

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3. An intelligent data platform to quickly see, understand and share data

Once set up, Needl.ai classifies and labels data using ML-based tagging algorithms. It also intelligently groups real-time data into the relevant feeds. However, if users required additional tags to further organize their work, they could create new tags. 

They could also bookmark content and review it later under the Bookmarks tab.

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As mentioned earlier, Needl.ai clips and summarizes large data sets to help users understand and assess data quickly. Needl.ai lets users 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, the firm’s analysts didn’t have to leave Needl.ai to open multiple documents, copy data from each file, and paste it into another document. Instead, Needl.ai automated such time-consuming tasks, making it easier for the firm to consume, interpret and use data.  

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