When it comes to building a winning AI strategy for M&A, data is everything. At the most foundational level, that means AI models built for dealmakers must be annotated with rigorous precision and consistency.
AI model annotation, or data annotation, is the process of implementing a tagging system for raw data so that it can be used to train AI and machine learning (ML) models. For private market dealmakers, it’s the key to winning in specialized verticals.
Below, we dive deep into what AI model annotation is, how it works, and why it’s crucial for efficient M&A workflows.
Key Takeaways
- AI model annotation adds tags to raw data so that AI models can interpret it correctly and learn from it. This learning is what allows the AI to perform specific tasks.
- Diligent data annotation powers precise company classification, signal extraction, relationship management, and data standardization. This helps private market dealmakers find the right acquisition targets faster.
What Is AI Model Annotation?
AI models are like babies. They aren’t “born” with all of the knowledge they need to perform tasks and go about their lives — they have to be taught.
Data annotation sets the precedent for how an AI model learns and ultimately performs. It teaches the model to identify items, recognize patterns, and make predictions. The main types of annotation are:
- Text annotation: This process includes sentiment analysis (i.e., defining text as positive, negative, or neutral), named entity recognition (labeling names, places, organizations, etc.), and intent classification (recognizing a user’s objective).
- Image annotation: This process involves object detection (i.e., drawing bounding boxes around objects), image classification, and segmentation.
- Audio annotation: This process includes transcription, speaker identification, emotion tagging, and labeling non-speech sounds.
- Video annotation: This process involves object tracking, activity recognition, and frame-by-frame labeling.
How Does AI Model Annotation Work?
So how does data annotation actually work? Let’s break down each step of the process.
- Raw Data Collection — The process begins by gathering all of the relevant unstructured data from web scraping, user interactions, databases, etc. in a single, centralized place.
- Data Cleaning & Processing — Next, the raw data has to be cleaned to prep it for annotation. This involves removing duplicates, corrupt files, and other noise, normalizing formats, and breaking up the data into manageable pieces for processing.
- Annotation — Human annotators then use specialized tools to tag the data. They may use AI tools to assist with quality checks and labeling.
- Quality Control — Senior annotators pull random samples of the annotated data for review. They check for annotation accuracy and verify agreement scores. Annotators also address any inconsistencies in labeling or phrasing (for example, if one annotator labels “car” and another labels “vehicle” or “automobile”) at this stage.
- Dataset Creation — Once any inconsistencies are resolved, the annotated data is combined into structured formats and split into sets for training, validation, and testing.
- Model Training — Here, the annotated data is fed to a machine learning framework. This is where the annotated data labels become the source of truth for supervised learning. The AI model ingests the data and optimizes parameters accordingly.
- Evaluation — This is where the dataset saved for testing comes into play. The AI model is fed the testing data and labels it based on what it has learned from its training data. Annotators then evaluate its classification accuracy.
- Iterative Feedback Loop — Once annotators have identified the error cases, they send them back for re-annotation or additional labeling. The model will also flag uncertain predictions so that humans can label them. The data is then fed back to the model for further learning. This cycle repeats until the model’s performance plateaus.
- Deployment & Monitoring — Finally, it’s time to deploy the model. But even when it’s live, annotators continue to tag new samples for model retraining. They also monitor for drift and regularly update their labels to maintain a high level of accuracy.
Why Is AI Model Annotation Important for M&A Workflows?
M&A workflows depend on a lot of unstructured private market data, making it difficult for dealmakers to source and screen targets.
Private market platforms like Grata rely on high-quality annotation to make private market data usable for dealmakers and to enable greater precision in sourcing and screening.
Diligent Annotation Enables Data Standardization
Cultivating accurate private market data involves synthesizing lots of disparate data sources. In order for the AI model to train properly, and for its findings to be useable for dealmakers, the data needs to be standardized.
That means annotators must align financial metrics, headcount, office locations, etc. to the same format. This ensures models can compare datapoints across companies and industries.
One way Grata leverages data standardization is our Consolidated Financials for dealmakers in the UK, France, and Germany. Typically, private market investors in these countries would need to sift through government documents across multiple sources to get a solid picture of a target’s financial performance, subsidiaries, etc. Grata consolidates everything directly on the company’s profile.
For example, here is the financial data that a dealmaker could see for UK-based Renewable Energy Systems (RES):
More Precise Industry Classifications Power Deeper Search
Private market dealmakers cannot effectively navigate through millions of private companies without technology that allows them to dig deep. Broad industry classifications and reliance on human processes limit how granular information from company databases can get.
Grata, on the other hand, uses a proprietary industry classification system — including industry-leading software classifications — powered by machine learning and high-quality data annotation to drive deeper searches.
As a result, dealmakers can drill down into niche spaces using specific filters. Grata users can search for companies by keyword, industry, business model, location, ownership, and more. Grata’s NLP algorithm will also recommend industry matches based on the user’s entry.
With more data granularity, dealmakers can discover new market adjacencies that fit their investment thesis and identify more targets. They can also find targets before their competitors, giving them an edge.
Signal Extraction Identifies Growth Trends
Accurate annotation of data for changes in headcount, new funding activity, or website traffic powers growth trend identification. AI models learn to classify these data points and synthesize trends, then flag them as growth signals.
Here’s an example of how Grata tracks and displays company headcount growth:
Relationship Mentions Map Org Structures and Identify Decision Makers
Annotating mentions of partnerships, customers, suppliers, etc. helps map out organizational structures and ownership trees. This provides important context for dealmakers as they search for and evaluate potential targets.
Here’s an example of how the Grata platform displays ownership trees on company profiles.
The same process can be applied to identify company executives, founders, owners, and investors. Grata users can access verified contact information for executives directly on the company’s profile. That way, they can easily reach out to the right decision makers to start the conversation.
Consistency Is Key
Each AI model is only as good as the quality of its training data. It’s crucial that annotators be clear, meticulous, and consistent about the structure and categorization that they implement — especially if the model is designed for highly specific industries like M&A.
Remember: garbage in, garbage out.
Unlock the Private Market with Grata
Grata’s high-quality data annotation powers deeper search capabilities and faster, more precise screening. Learn about how we’re changing the game for dealmakers around the world here.
Unlock the coverage, data depth, and comprehensive workflows that private market dealmakers need — all in one sleek, user-friendly platform. Schedule a demo to get started.
