It’s not just data analysts in trouble—all our knowledge workers, from engineers to marketers to designers and beyond, are overwhelmed by the sheer volume of data they’re asked to understand.
The core problem isn’t that these people aren’t capable, but rather they have no practical way of keeping up with the task of connecting and organizing their data, which is just the first step in turning data into knowledge through analysis. And when data analysts and knowledge workers can’t keep up with the incoming flow of data, the information overload sets in, creating a feedback loop of frustration and burnout.
The answer isn’t changing processes or hiring more people to pick up the slack, so to speak, but rather implementing new technology that relieves knowledge workers from the burden of connecting and organizing data. But to understand exactly how that technology can improve day-to-day interactions with the data, we first need to peek into how and why data fatigue manifests itself within even the most technologically-advanced organizations and talented minds.
Can you identify data fatigue?
Let’s create a use case showing how easily data fatigue can settle in.
Jack is a data analyst of Griddle Inc., an online web app and community for food lovers to discover and chat about all things breakfast. Jack’s job is to analyze restaurant reviews across the U.S. and discover interesting trends so his organization can better serve its customers and community with new content.
But the scope of his current work is even narrower than U.S. breakfast food—he’s honed in on U.S. pancake culture. That seems simple, right? How much data can there possibly be about the types and trends of a single food in one country?
Griddle Inc. recognizes Jack’s talent, and his work history is full of exemplary performance reviews, but everyone can see that he’s dissatisfied and distracted, and his projects—from hitting deadlines to the quality of his output—aren’t what they used to be. In conversations with his manager, Jack talks often about feeling like he spends more of his time reading pancake reviews to generate data than analyzing the data itself. He’s frustrated that he can’t maintain the dataset he needs or drive new business initiatives with the knowledge he gets from it.
Jack’s manager starts looking into how data fatigue manifests itself within even the most talented of knowledge workers:
- First, their organization is collecting new data faster than any single person, no matter how intelligent or organized, can possibly read or understand.
- In turn, the data analyst or knowledge worker spends an excessive amount of their work day just searching for relevant data—up to 50% of their time “clocked in”—rather than analyzing it or developing new knowledge.
- Conversations about new initiatives most often drift toward the data itself rather than how they’re going to analyze it, which creates the foundation for new efforts.
Using those symptoms as guidelines, it’s clear Jack has a confirmed case:
- He needs to skim through hundreds of reviews for his new “Best Blueberry Pancakes in New York” project for relevant data.
- To meet that goal, he spends more time searching for data on sources like Yelp, Google Maps, and others rather than developing knowledge about the consensus of New York’s many breakfast aficionados.
- And this effort to read through thousands of reviews for data and trends leaves Jack in constant doubt. He’s wondering if he missed something or mistakenly included irrelevant data, which would affect the final report he’ll present to leadership.
In a way, Jack is lucky—his manager recognizes that Jack’s data fatigue can’t be solved with a pep talk, a bad performance review, or a vacation. Instead, Jack needs better tools for filtering through the noise and reducing the scope to just the most relevant data, so he stops mucking around with data and starts developing the pancake-centric knowledge that drives new efforts within Griddle Inc.
Knowledge graphs as the antidote to data fatigue
To ease this burden on Jack and other data analysts/knowledge workers, Jack’s manager pulls in the Griddle Inc. development team to search and scope out new data processing, management, and analysis tools.
Because they need to solve complex analytics problems, they agree on the potential value of knowledge graphs, which help analysts and knowledge workers by shouldering a heavy portion of the cognitive load required to keep data connected and organized so they can focus on applying it to research.
But they also recognize that spinning up a knowledge graph isn’t a solution in and of itself. They need a suite of services that connect otherwise siloed data and ingest new information, in real-time, using natural language processing (NLP) services, which is where the development team comes in.
Using a solution like GraphGrid, developers can rapidly launch a solution of advanced organizational and analytical tools that smooth over the causes of Jack’s data fatigue by:
- Identifying the “who, what, and where” of the review, such as the establishment’s name and what type of food is reviewed, to filter out anything irrelevant to blueberry pancakes.
- Understanding the meaning behind an arbitrary scoring system, like “8.5/10” or “two thumbs up,” and the overall sentiment of the review based on word choice and sentence structure.
- Picking out the names of other restaurants mentioned as comparisons to the blueberry pancakes in question.
GraphGrid automatically takes all these details, processed directly from unstructured text, and ingests it into the knowledge graph, organizing each detail into a web-like network of verifiable details about the landscape of blueberry pancakes in New York. And, in the end, Jack gets a simple notification from GraphGrid letting him know there’s new data in his knowledge graph to explore.
Instead of reading reviews and worrying whether he’s missed out on some crucial detail, Jack can focus most of his time generating new insights about the New York blueberry pancake scene by exploring the relationships within and between reviews. He can search for specific details, analyze his graph data visually, and continuously improve the data within the knowledge graph.
And, as he signs off one evening, he’s confident that he’ll show up for work the next day with all and only the most relevant data he needs—not to worry about data, but to develop knowledge that’s relevant to Griddle Inc.’s future.
Fight data fatigue with GraphGrid
When you stand up a platform of graph and artificial intelligence and deliver a knowledge graph to your colleagues, you relieve all the time and frustration wasted on manual data discovery efforts.
And when you choose a platform like GraphGrid, you get a composable platform that spins up quickly while also offering a breadth of SDKs and APIs in languages your team already knows, to extend and customize the experience to your organization’s exact needs and the problems you’re trying to solve.
Delivering graph-based capabilities to your knowledge workers doesn’t just solve data fatigue by searching for and processing new data, even swaths of unstructured text data, and processing it into only the most filtered-down, relevant insights for people like Jack. You’re also giving them a library of low-code tooling to explore their knowledge graph and build complex queries to continuously improve the quality of your data no matter how quickly it changes and expands.