Searching and curating information is a critical challenge for research analysts at financial firms. According to an HBR study, research analysts across industries spend 70% of their time looking for and organizing information.
Furthermore, a 2021 workplace survey by Qatalog (in collaboration with Cornell University) states that 54% of the survey participants found it hard to find information they needed. That’s because they had to scour through messaging channels, navigate project management boards, and dig through cloud storage systems.
Financial institutions particularly deal with large volumes of data. According to Jasmit Sagoo, ex-director of technology at Veritas, “the financial industry holds huge backlogs of stale data, 20% of which are made up of old document files.” He also highlights the industry's “save everything often” mentality as a leading cause for accumulating alternative data – information you don’t know you have.
Often, organizations collect, process, and store alternative data during regular business activities, but generally fail to use for other purposes, according to Gartner.
Splunk summarizes alternative data best:
“Alternative data is all the unknown and untapped data across your organization, generated by systems, devices and interactions. Maybe it’s siloed off somewhere; maybe the format or metadata is inconsistent. Maybe no one’s figured out what to do with it. Maybe you literally don’t know it exists.”
According to a survey by Veritas, 52% of all data within organizations remains unclassified or untagged, i.e., alternative data. So, businesses have limited or no visibility over vast volumes of potentially business-critical data.
As the amount of information collected and processed goes up, the time spent organizing it will grow simultaneously, leading to information overload.
Let’s further explore the challenges research analysts in financial institutions face because of information overload and mismanagement.
To analyze the impact of information overload on research analysts at financial institutions, we must look at four consequences of alternative data:
Let’s explore each aspect further.
“We’re drowning in information and starving for knowledge.” – Rutherford D. Rogers
Alternative data exacts a toll on your operational expenses and business decision-making.
Let’s start with the impact on operational expenses. When you store large volumes of data and cannot leverage it for business decisions, your operational expenses shoot up. According to a study by Veritas, the average cost of storing 1 PB of data is USD 5 million. If even 50% of that data is considered to be alternative data, then that’s millions spent in merely retaining alternative data. The same study also states that half of a company’s storage budget goes into retaining alternative data.
Now let’s look at the impact on business decision-making. The financial industry is time- sensitive – if your research analysts are unable to find the right information and prioritize the relevant data sources, then you could miss out on time-sensitive investment opportunities.
For instance, when you receive a flurry of chats from multiple messaging apps everywhere, it’s easy to miss out on crucial information from high-value sources like investors, industry experts, and more.
“Information is a source of learning. But unless it is organized, processed, and available to the right people in a format for decision making, it is a burden, not a benefit.” - William Pollard
Alternative data is not searchable or retrievable. Let’s look at one aspect of a research analyst’s job – scouring through circulars, notices, and press releases on stock exchange websites like the ICE and NYSE.
These websites are black holes – there’s no way of searching through the information present here in the form of PDFs, images, and spreadsheets. As a result, the analyst must download the document and read through it with a fine tooth comb to discern any useful patterns or information.
Caption: Notices, circulars, or press releases on stock exchange websites aren’t searchable
When you don’t know what data you have, you rely on manual processes.
Let’s look at another everyday function in the life of a research analyst – note taking. Some of your analysts might take notes during essential calls or while reviewing trading documents spanning hundreds of pages.
These notes are taken on the analyst’s tool of choice – Evernote, Notion, Google Keep, and more. These tools aren’t interconnected, and the notes don’t get stored on a central, cloud-based repository.
Any time the analyst wishes to find something, they must switch between multiple apps – Google Docs, Evernote, Toby, a CRM, etc. – to find the information they need.
That’s because the CRM would maintain information about various meetings with investors, other team members, experts, and so on. The research analysts attending these calls maintain their notes on Google Docs and Evernote. Then they search for further context on the talking points from the meeting, which they bookmark using a Chrome extension like Toby.
None of these systems talk to each other, making it impossible to seamlessly operate across all these tools. Sharing such information with other stakeholders is another painful process.
Meanwhile, the alternative data keeps accumulating and the lack of scalable processes to retrieve the right information becomes more excruciating.
“Institutional memory (includes) intangible assets which, although not physically evident, add value to the intellectual capital of the institution thus being of incalculable value.” - WHO
In an organization with several manual processes, untagged or unclassified information, and scattered documents containing crucial notes and insights, it’s common to have a few people – SMEs, veteran analysts, etc. – with complete context on everything from competition landscape and equity market trends to investment decisions.
If these people were to leave the organization, a sizable chunk of institutional memory would be lost forever.
When you consider the above consequences of information overload and alternative data, you can understand how research analysts struggle with:
What if the research analyst could search for and organize information automatically, quickly prioritize the important messages, and get important context from documents at a glance?
The ideal solution would be putting all of your information sources in a single place and having mechanisms to automatically prioritize the sources and offer essential context at a glance.
That’s where an intelligent workspace with seamless information search, discovery, and collaboration can help. Such a platform would streamline the various activities related to research, content consumption, and information management.
Let’s see how.
Meet Needl.ai – an intelligent workspace for real-time information management. With Needl.ai, you can:
Picture this – all of your data sources are connected to a single workspace.
So, information gets collected and maintained at a single location in real-time from numerous sources – email, Slack messages, RSS feeds, WhatsApp messages, calls and meetings, stock exchange circulars, research from paid subscription platforms, and more.
The information collected – from static sources (existing reports and documents) and streaming sources (RSS feeds, chats, etc.) – gets organized automatically with the right tags.
For instance, Needl.ai has several tabs for each data type – emails, RSS feeds, chats, documents, etc.
Each piece of information gets tagged appropriately – company, sector, author, institution, and more – in real-time. So, no more piles of alternative data left unused and untapped within your storage repositories.
Moreover, these tags are searchable to further simplify information search and discovery.
Needl.ai lets you set up curated data feeds, wherein you can have information about a specific topic or from a particular contact streaming in real-time.
These curated feeds help you ignore the noise and focus on the information that truly matters from the right sources and contacts.
Needl.ai integrates all data sources and lets you search across various formats using a Google-like interface. So, you can look up anything from documents and chat messages to charts in PDFs.
Whenever you try to search for something on Needl.ai, the search results offer contextual information – file type, date of creation, authors or owners, a brief description of the contents, and more.
So, you can figure out what a 100-page document contains without even having to open it.
Sharing information with the right people is seamless – via email, Slack, or a link.
Needl.ai lets you define who gets to access a certain piece of information, and the duration for which they can access it. You can also decide who gets to access your data feeds or add notes to your research.
So, would you like to take Needl.ai for a spin and see how it helps you tackle information overload?
