With the economy slow and layoffs abound, there is one thing you can be certain about as an engineer or developer: your organization is asking both you and your data analyst and knowledge worker peers to do far more with less.
Your peers know they’re likely to spend hours every week playing “catch up” on the flood of recent changes, researching new information, and categorizing data to build knowledge through graph thinking. They prioritize the most urgent data requests to clear their backlog and give themselves a bit of breathing room. You can’t blame them for trying to minimize burnout, but if they don’t have the tools they need to work efficiently, they’ll inevitably start to make decisions based on outdated data, which could drive misinformed strategies.
Imagine how much time you could save your peers if you could build them a solution that automatically organizes new data and pushes what’s most relevant to their devices. Even better—imagine if you could give them the confidence they’re always working from the latest version of your organization’s source of truth.
Achieving this state won’t be overwhelming, even though you’re also being asked to do more with less, if you can accelerate graph solution development at your organization and combine graph data, natural language processing (NLP), and change data capture (CDC) onto a single platform. When done right, like with the world’s leading composable graph + AI platform, GraphGrid, you can give your data analyst and knowledge worker peers new efficiency superpowers by automating how they discover the latest and most relevant data.
Automatically gather and process data with NLP
An NLP service operates seamlessly as your first defense against the incoming flood of new or changed text documents. As an engineer or developer responsible for delivering the solutions your peers need to stay up-to-date on the latest information in your industry, you can train NLP models around the data-intensive business challenges or opportunities they’re responsible for solving.
NLP models can come in many different forms, but Named Entity Recognition (NER) is likely its best-known form, which picks out proper nouns belonging to people, places, or formal “things.” Other popular models include extracting key phrases, sorting unstructured content by similarity, and determining relationships via paraphrasing.
Need a quick refresher on how graph data and graph thinking are extraordinarily valuable for these folks? Be sure to check out: Graph Thinking: A Simple Explainer for Connected Data.
When new or changed text data hits your data lake or any other source that you can integrate with your platform, the NLP service automatically leverages one or more models to transform unstructured text into connected data entities, which are immediately available to your peers via a knowledge graph.
When you have a cycle of ingesting, processing, and making data available, you get data analysts and knowledge workers with newly-discovered superpowers. They can:
- Work far more efficiently, as NLP has offloaded the most tedious and time-consuming parts of their job, like identifying sentiment and tagging entities.
- Include more of the unstructured data you already store in your data lake in their higher-level analysis and decision-making without spending more time on research.
- Focus more on uncovering new insights in your existing data, which are difficult or impossible to discern when doing everything manually.
- Diversify and expand their data sources without spending additional time to ultimately create more informed decisions.
Even with NLP enabled, and all these benefits unlocked, your peers would still be responsible for actively querying and exploring data in context in a knowledge graph, which doesn’t help them with the overwhelming burden of data requests in their inbox—time to take their efficiency to the next level.
Enable ‘push notifications’ on graph data with change data capture
If NLP is the service that streamlines how your organization processes text data with no additional burden on your peers, giving them superpowered access to more context-rich data, CDC is what ensures everyone is working from the latest organizational knowledge, even if that changes multiple times every day.
CDC tracks every time your data is modified or updated, then pushes those changes to any person or process that’s subscribed to those changes. CDC doesn’t run applications, start processes, or drive insights on its own—it just informs others about a change and its context—but that’s still a massive efficiency for data analytics and knowledge workers: They know, in real time, when relevant data has changed.
With CDC, you upgrade your peers with even more superpowers:
- They can safely add more data sources without burdening themselves with an even longer daily or weekly “catch up” session, which typically takes hours.
- They don’t have to waste time with SQL queries, batch processing, or other manual ETL tasks to integrate and update their data sources.
- They know, with certainty, their analysis and decision-making is derived from the absolute latest and greatest organizational knowledge.
As a combination, NLP and CDC offload tedious and time-consuming tasks away from these folks, which frees them up to focus on what your organization hired them to do in the first place: do complex analysis and make difficult, business-enabling decisions with it.
What automated discovery looks like day-to-day: open source intelligence
Open source intelligence (OSINT) is the practice of deriving insights based on data and information that’s available from any open source, both public and covert, like news articles, social media content, government data, traditional media, and beyond. The use cases for OSINT are wide-ranging:
- Competitive analysis: Gather information about your competitors’ products and services, business strategies, and financial performance to guide better decisions about how your organization should respond.
- Reputation management: Track reviews and social media posts to monitor how your organization is perceived over time or proactively respond to negative comments where they happen.
- Risk assessment: Identify and assess the possible risks, whether that’s from natural disasters or political unrest, involved in any large capital expenditure, joint venture, or expansion.
- Anti-money laundering (AML) / know your customer (KYC): Verify the identity of your customers, and analyze their transactions and relationships, as part of ongoing due diligence to meet regulatory requirements.
- Detecting human trafficking or drug operations: Track the assets, financial activity, or physical movements of suspected criminals to uncover where they’ve hidden their wealth or contraband of interest to law enforcement.
The biggest challenge with efficiently employing OSINT is processing the endless flood of information. To ensure they’re capturing every possible source of information, teams of highly specialized OSINT analysts attempt to keep up with thousands of news sources, websites, social media feeds, and public databases. The sheer scale of the operation means they’re wasting hours every day sifting through the flood of data, hoping they can find a bit of information that’s relevant to the problem they’re trying to solve.
Instead, their time would be spent synthesizing what they’ve already found, collaborating with other analysts to develop new insights, or crafting the reports that summarize and make recommendations on their area of research for the rest of their organization to act on.
While your organization might not be doing OSINT on this scale, you can still deliver a technical solution that bypasses the same wasteful “catching up” experience your peers face every day as they try to find relevant data.
With a composable graph + AI platform like GraphGrid, you can quickly develop a solution that provides automated ingest for any publicly available RSS source. Every time the RSS feed updates, your platform ingests the data, extracts full article content, then ships it over to the NLP service, which processes and annotates the unstructured data into valuable nodes in the graph. CDC then tracks these changes and notifies the right people that your organizational truth has changed.
From your peers’ perspective, all they need to worry about is adding RSS feeds, taking action on notifications they get from the graph + AI platform, and exploring just the changed data in their knowledge graph.
With this system in place, they can use a larger number and greater diversity of sources, work from the same data, and deliver the all-important competitive analysis in a fraction of the time.
Cut the ‘catch up’ cycle from hours to minutes
With the economic headwinds we’re all facing, your organization likely isn’t adding more data analyst or knowledge worker roles—at least not enough to work at the pace of today’s business. For the folks you already have, their headwinds are getting stronger, too—the more data your organization acquires and stores, and the faster your industry pivots, the harder it is for them to keep up with the most up-to-date data.
Graph data, NLP, and CDC working in close synchronization also benefit your engineering team. As your peers take what’s now in their knowledge graph and further refine it with new connections, structure, distance, and context, they’re also teaching your NLP service about what’s most valuable to them, refining your models for better results down the line. The same goes for CDC—it delivers all changes to your NLP service for automatic reprocessing, which means you know your people are getting superpowers from your AI, not fighting its out-of-date analysis.
The first step to delivering solutions that simplify how your peers keep up with your flood of data is scheduling a solution session with GraphGrid experts. We’ll discuss your data-intensive opportunities and the information you’re already sourcing to showcase technical solutions, using graphs, NLP, and CDC, that will save your peers valuable hours every day.
You’ll walk away with nothing less than a clear vision of how a composable graph + AI platform will fundamentally change how your organization builds and acts upon knowledge at the speed of today’s business.