What PMM success looks like

How do you measure product marketing? I look at adoption, sales impact, positioning, and collaboration, using numbers alongside feedback from customers and colleagues.

Product marketing
Positioning & messaging
Sales enablement
October 20, 2026
·
6 min
read

Measuring product marketing gets complicated quickly. Ask Sales, Product, and Marketing what a PMM should deliver, and you will hear different answers: pipeline, adoption, win rate, positioning, a successful launch.

Each answer covers part of the job. Much of a PMM's work shows up in results shared with other teams, which makes it hard to separate your contribution from everyone else's. You still need to know whether the work is helping.

Across 25 years in marketing, including 15 in deep tech, I have found four areas useful to look at: adoption, sales impact, positioning, and how early PMM gets involved in decisions. For each one, I want numbers and feedback from the people doing the work.

Why does no single number work?

Product marketing connects Product, Sales, Marketing, and Customer Success. The priorities depend on the company, but the work usually crosses several teams.

A pipeline target can tell you something about commercial impact. It tells you less about whether customers understand a new feature or whether the product team is solving the right problem. A launch calendar tells you what shipped, but you need to look further to find out what happened afterward.

I use the four areas below to work out what to measure. They give me a fuller picture than any one metric can.

Do your launches keep working after launch day?

Launch week gets a lot of attention. I also want to know what customers are doing a few months later. Did the intended segment try the feature? Did they find enough value to keep using it?

The Product Marketing Alliance's article on how technical product marketers measure success is a useful reference for thinking about adoption and retention alongside launch activity.

Depending on the product, I would look at:

  • Feature activation rate
  • Onboarding completion
  • Adoption among the intended users
  • Retention and expansion

Before launch, I set objectives and build the messaging. After launch, I look at usage, collect feedback, and work through what needs to change. Sometimes the message needs work. Sometimes customers need more help getting started. You only find that out if someone keeps paying attention after the announcement.

At Visual Layer, we went from zero to acquisition in a year. That is a company result I am proud to have contributed to. To understand how the launch itself performed, I would still look at who adopted the product and what they did with it.

Does Sales use your work?

A battlecard can be accurate, well written, and completely unused. Before making another one, I want to understand why.

Useful sales material fits the conversations reps are having. A competitive card helps them answer an objection. A deck gives them a clearer way to explain the product. When reps come back with questions or ask for an update, that feedback helps me understand what they need.

The measures I would look at include:

  • Win rate and reasons for wins and losses
  • Deal velocity
  • Use of sales materials
  • Influenced pipeline and revenue

At Coro, my work contributed to $1M+ in quarterly annual contract value (ACV) and 300% year-over-year company growth. At Lytx, on the Surfsight line, we saw a 65% increase in market share. Those were shared results. I would look at them alongside sales feedback and use of the materials to understand my contribution.

Does everyone tell the same story?

Positioning starts to work when people can explain the product consistently without reading from a document.

Listen to how Product, Marketing, and Sales describe it. Do they agree on who it is for, what problem it solves, and why someone would choose it? Then listen to customers. What do they understand, and where do you still have to explain?

Useful signals include:

  • Website conversion rates
  • Sales feedback on the messaging
  • Share of voice
  • Analyst mentions and how analysts describe the product

None of these tells the whole story. Website conversion depends on more than the words on the page, and an analyst mention does not necessarily mean your positioning landed.

I find sales calls especially useful. I listen for the questions buyers ask, the explanations that help, and the points where the conversation stalls. That gives me something specific to investigate before changing a headline.

The same applies before writing new positioning. I interview stakeholders, review existing material, and identify the assumptions we need to test. If the premise is wrong, better wording will only take you so far.

How early do you get involved in decisions?

Some of the most useful signs of PMM success are harder to put on a dashboard.

Product asks for your customer insight while the roadmap is still open. Sales brings you recurring objections so you can work on them together. Marketing involves you while a campaign is taking shape.

These are signs that people trust your contribution. They also give you more opportunity to help. If you first hear about a feature when the specification is final, many of the decisions your research could have informed have already been made.

I would ask the teams directly whether PMM is involved early enough and whether that involvement helps them make better decisions. Their answers can tell you where the working relationship needs attention.

How does success change by company stage?

The company's stage changes what useful progress looks like.

At a Series A or B startup

You may be defining the first ideal customer profile (ICP), establishing baseline messaging, and looking for signs of product-market fit. There may be little reliable data to compare against, so part of the job is deciding what to track and building that baseline.

At an enterprise

You are more likely to have an established product, customer base, and set of measures. The priority might be adoption of a particular feature, cross-sell, or expansion within an existing segment. Agree on the outcome you want to improve and how you will assess PMM's contribution.

I have worked as a solo marketer across ICP, messaging, and GTM planning, and built a 300-page developer portal. That breadth matters when you are establishing the foundations. In a more established team, the remit may be narrower. The scorecard should reflect the job you need done.

How do you know yours is working?

Start with four questions:

  1. Are customers still using what we launched a few months later?
  2. Do reps use the materials in their sales conversations?
  3. Can customers and colleagues explain the product consistently?
  4. Does PMM get involved while there is still time to influence a decision?

The answers will not replace your metrics. They will help you interpret them and decide where to look more closely.

If reps are not using the materials, find out why before counting the next batch as progress. If adoption is low, investigate where customers get stuck. Those conversations give you a better starting point than the number of assets or launches delivered.

I want a PMM review to cover what changed, where the work helped, and what still needs attention. The other teams' experience belongs in that discussion.

Key takeaways

  • Assess PMM's contribution across adoption, sales impact, positioning, and collaboration.
  • Use feedback from customers and colleagues to help explain the numbers.
  • Choose measures that reflect the company's stage and PMM's remit.
  • Review what people use and what changes as a result of the work.

Where does your scorecard need work?

If your current measures leave you unsure where product marketing is helping, start with one of these areas and talk to the people closest to it.

For a second set of eyes, bring your current scorecard to a free 30-minute call.

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share-of-voice
Share of Voice
The portion of market conversation a brand owns compared with its competitors: mentions, search visibility, and coverage. It is a read on how much of the category talks about you.
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feature-activation-rate
Feature Activation Rate
The share of eligible users who use a new feature meaningfully after it launches. It shows whether a launch changed customer behavior, not just whether it created awareness.
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annual-contract-value-acv
Annual Contract Value (ACV)
The average revenue a customer contract brings in per year. It is used to size deals, compare segments, and track how much new business a company signs in a period.
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deal-velocity
Deal Velocity
How quickly deals move through the sales pipeline from first contact to close. Shorter sales cycles mean less friction between a buyer's interest and a decision.
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win-loss-ratio
Win/Loss Ratio
The share of competitive sales opportunities a company wins compared with those it loses, tracked over a period. A rising ratio suggests that positioning and enablement are working.
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battlecard
Battlecard
A short internal reference that helps a sales rep win against one named competitor: how they position, where they are strong, where they are weak, and how to answer the objections buyers raise.
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sales-enablement
Sales Enablement
The training, content, and tools that help sales teams sell: product education, talk tracks, decks, and competitive material, kept current and shaped by feedback from the field.
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product-market-fit
Product-Market Fit
The point at which a product satisfies a clearly defined market: the right customers adopt it, keep using it, and tell others. The first target for an early-stage company.
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go-to-market-gtm
Go-to-Market (GTM)
The plan and ongoing practice for bringing a product to its buyers: audience, positioning, pricing, channels, launch, and feedback. It runs as a cycle that continues after launch day.
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ideal-customer-profile-icp
Ideal Customer Profile (ICP)
A description of the type of company most likely to buy, succeed with, and stay with a product. It combines firmographics, technology stack, challenges, and budget reality into a portrait precise enough to act on.
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messaging-framework
Messaging Framework
A single document that turns positioning into the words a company uses: the core narrative, the value propositions, and the proof points for each audience. Product, marketing and sales all draw from it.
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positioning
Positioning
The place a product holds in the buyer's mind relative to the alternatives: who it is for, what problem it solves, and why it is different. It is the foundation every message is built on.
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data-augmentation
Data Augmentation
Creating extra training examples by altering existing ones, such as flipping, cropping, rotating or recolouring images, so the model sees more variety.
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workflow
Workflow
The ordered steps and hand-offs by which work moves from start to finish, who does each one and what triggers the next.
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verification
Verification
Confirming that a result, label or component is correct by checking it against a reference or a second method.
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vectorized-execution
Vectorized Execution
A query-engine technique that processes whole columns of values in one CPU instruction instead of row by row, which is what makes engines like DuckDB fast.
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vector-space
Vector Space
The multi-dimensional space where embeddings live; distance within it stands for how similar two items are.
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uncertainty-sampling
Uncertainty Sampling
An active-learning strategy that sends the examples the model is least sure about to human reviewers first, because those teach it the most.
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transformer
Transformer
A neural network architecture built on attention, which weighs every part of the input against every other part; it underpins modern language and vision models.
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traceability
Traceability
The ability to follow any output back through the data, labels, code and model versions that produced it.
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tokenization
Tokenization
Splitting text into the units a language model works with, which may be words, parts of words or characters.
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token-limit
Token Limit
The maximum number of tokens a language model can take in and produce in one request, which caps how much context it can hold.
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threading
Threading
Running several parts of a program at the same time on separate threads so work overlaps instead of waiting in line.
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temporal-analysis
Temporal Analysis
Studying how things change over time across frames or records, to detect motion, events, trends or anomalies.
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sla
SLA
Service Level Agreement: the committed level of service a provider promises, such as uptime, response time or support hours, with consequences if missed.
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simulation
Simulation
Generating realistic scenarios in software to test or train a system where real data is scarce, risky or expensive to collect.
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similarity-search-vector-search
Similarity Search (Vector Search)
Finding the items whose embeddings sit closest to a query's embedding, which returns things that look or mean alike rather than share keywords.
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setup
Setup
The initial configuration of a project: connecting data sources, choosing models and settings, and defining the workflow before work begins.
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semi-supervised-learning
Semi-Supervised Learning
Training on a small labelled set plus a much larger unlabelled one, using the labelled examples to guide what the model learns from the rest.
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semantics
Semantics
The meaning behind data rather than its surface form, which lets a system treat "car" and "automobile", or two photos of one scene, as the same thing.
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segmentation
Segmentation
Dividing an image into regions by labelling every pixel, so the exact shape of each object or area is known.
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search-attributes
Search Attributes
The fields a search can filter or rank on, such as labels, tags, dates, camera id or model confidence.
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search-and-retrieval
Search & Retrieval
Finding the items in a collection that best match a query, whether by keyword, metadata or visual and semantic similarity.
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scene-understanding
Scene Understanding
Interpreting a whole image or video as a scene: what objects are present, where they are and how they relate.
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saliency-map
Saliency Map
A heat map over an image showing which pixels most influenced a model's prediction, used to explain and debug it.
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reward-function
Reward Function
In reinforcement learning, the rule that scores each action so the agent learns which behaviour to repeat.
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review-batch
Review Batch
A set of items grouped for a human reviewer to check in one sitting, often chosen because the model was unsure about them.
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relational-database
Relational Database
A database that stores data in tables with rows and columns and links them through keys, queried with SQL.
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regularization
Regularization
Techniques that stop a model memorising its training data, such as penalising large weights or dropping random neurons, so it generalises better.
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refactoring
Refactoring
Restructuring existing code to make it cleaner and easier to change without altering what it does.
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recipe
Recipe
A saved, repeatable set of processing steps or settings that can be rerun on new data to get the same kind of result.
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rag
RAG
Retrieval-Augmented Generation: a language model that first fetches relevant documents and then writes its answer from them, which cuts hallucination.
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quality-assurance-qa
Quality Assurance (QA)
The checks and processes that make sure a product, dataset or model meets its standard before it ships.
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predictive-maintenance
Predictive Maintenance
Using sensor data and models to forecast when equipment will fail, so it is serviced just before it does rather than on a fixed schedule or after a breakdown.
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policy
Policy
In reinforcement learning, the strategy that maps each state to the action the agent should take.
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pixel
Pixel
The smallest unit of a digital image, a single point with a colour or intensity value; images are grids of them.
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pgvector
pgvector
A PostgreSQL extension that stores embeddings and runs similarity search inside the database, so vector search needs no separate system.
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One-shot Learning
Learning to recognise a new class from a single example, usually by comparing against a learned notion of similarity.
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Object Storage
Storage that keeps files as objects with metadata in flat buckets rather than folders, built for huge volumes and cheap durability.
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normalization
Normalization
Rescaling values to a common range or distribution so no single feature dominates and models train more stably.
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noise
Noise
Random or irrelevant variation in data that hides the real signal, from sensor error, compression, lighting or sloppy labels.
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mvcc
MVCC
Multi-Version Concurrency Control: a database technique that keeps multiple versions of a row so readers and writers do not block each other.
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monitoring
Monitoring
Continuously watching a deployed system's health and output quality so drift, errors and slowdowns are caught early.
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model-versioning
Model Versioning
Tracking each trained model with its data, code and settings, so any version can be reproduced, compared or rolled back.
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metrics
Metrics
The numbers used to judge a model or system: accuracy, precision, recall, F1, latency and others chosen to match the goal.
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metric-learning
Metric Learning
Training a model so that similar items end up close together in its embedding space and dissimilar ones far apart.
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meta-learning
Meta-learning
Training a model to learn how to learn, so it adapts to a new task from very few examples.
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mask
Mask
A pixel-level map marking exactly which pixels belong to an object or region, the output of segmentation.
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markov-decision-process-mdp
Markov Decision Process (MDP)
The mathematical frame for reinforcement learning: states, actions, rewards and transition probabilities where the next state depends only on the current one.
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loss-function
Loss Function
The formula that scores how wrong a model's predictions are during training; training works to make this number as small as possible.
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latent-feature
Latent Feature
A hidden characteristic a model learns internally that is not directly observed in the data but helps explain it.
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label-noise
Label Noise
Errors and inconsistencies in labels, from mistakes, ambiguity or disagreeing annotators, which degrade what a model can learn.
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keypoint-detection
Keypoint Detection
Locating specific points of interest on an object, such as joints on a body or corners on a part, rather than drawing a box around the whole thing.
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iou
IOU
Intersection over Union: the overlap between a predicted box or mask and the true one, divided by their combined area. 1.0 is a perfect match.
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inverted-index
Inverted Index
An index that maps each term to the documents containing it, which is how full-text search answers queries quickly.
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instance-object-instance
Instance (Object instance)
One individual occurrence of an object in an image, as distinct from the class it belongs to.
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information-extraction
Information Extraction
Pulling structured facts such as names, dates, amounts or relationships out of unstructured text or images.
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indexing
Indexing
Building a lookup structure over data so searches find matches without scanning everything.
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image-processing
Image Processing
Operations applied to pixels, such as resizing, filtering, thresholding or colour correction, usually to prepare images for analysis.
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hyperparameter-selection
Hyperparameter selection
Choosing the settings that control how a model learns, such as learning rate, batch size or network depth, which are set before training rather than learned.
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grid-search
Grid search
Trying every combination of chosen hyperparameter values in turn and keeping the set that scores best.
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gradient
Gradient
The direction and size of change in a model's error for each weight, used during training to decide how to adjust that weight.
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gpu
GPU
Graphics Processing Unit: a chip built for many parallel calculations at once, which makes it the workhorse for training and running neural networks.
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generative-ai
Generative AI
AI that creates new content such as text, images, code or audio from a prompt, learned from patterns in large training sets.
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generalization
Generalization
A model's ability to perform well on data it has never seen, rather than only on its training examples.
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feedback-loop
Feedback Loop
A cycle in which a system's outputs, or human reactions to them, are fed back as training signal so it improves over time.
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feature-map
Feature Map
The output of one layer in a convolutional network: a grid showing where in the image a particular pattern was detected.
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feature-engineering
Feature Engineering
Creating or transforming input variables so a model can learn from them more easily, using domain knowledge rather than raw data alone.
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fairness
Fairness
A model behaving without systematic disadvantage to groups defined by traits such as gender, age or region.
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exploration-vs-exploitation
Exploration vs. Exploitation
The trade-off in learning systems between trying new actions to discover better options and repeating actions already known to work.
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evaluation
Evaluation
Measuring how well a model performs against held-out data using agreed metrics, before and after it is deployed.
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efficiency
Efficiency
Getting the same or better result with less time, compute, money or manual effort.
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ec2
EC2
Amazon Elastic Compute Cloud: AWS's service for renting virtual servers by the hour, including GPU machines for training.
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domain-shift
Domain Shift
When the data a model meets in production differs from the data it was trained on, so accuracy drops even though nothing in the model changed.
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dml
DML
Data Manipulation Language: the SQL commands that read and change rows, such as SELECT, INSERT, UPDATE and DELETE.
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dino
DINO
A self-supervised vision method from Meta that learns strong image features without labels by teaching a network to match different views of the same image.
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ddl
DDL
Data Definition Language: the SQL commands that create and change the structure of a database, such as CREATE TABLE and ALTER.
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database-reliability
Database Reliability
A database's ability to stay available, correct and recoverable under load, failure and change.
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data-quality
Data Quality
How fit data is for its purpose: accurate, complete, consistent, current and free of duplicates, noise and wrong labels.
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data-lineage
Data Lineage
The record of where data came from and every transformation it went through, so any result can be traced back to its source.
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data-leakage
Data Leakage
When information from the test set, or from the future, slips into training, so the model scores well in the lab and fails in production.
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data-lake
Data Lake
A store that keeps raw data in its original format at any scale, structured or not, for later processing and analysis.
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data-integrity
Data Integrity
Data that is complete, accurate and unaltered over its whole life, protected from corruption, loss or unauthorised change.
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data-ingestion
Data Ingestion
Bringing data from its sources into a system where it can be stored, processed and analysed, whether in batches or as a live stream.
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data-governance
Data Governance
The policies, roles and controls that decide who can access data, how it is kept accurate and how its use is tracked.
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data-consistency
Data Consistency
Data that stays correct and in agreement across copies, transactions and systems, so every reader sees the same truth.
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data-balance
Data Balance
How evenly examples are spread across classes or conditions in a dataset; imbalance teaches a model to favour whatever it sees most.
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cross-validation
Cross-validation
Evaluating a model by splitting the data into several folds, training on some and testing on the rest in turn, to get a more honest accuracy estimate.
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cosine-similarity
Cosine Similarity
A score between -1 and 1 for how closely two vectors point in the same direction, used to compare embeddings regardless of their length.
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convolutional-neural-network-cnn
Convolutional Neural Network (CNN)
A neural network built for images: it slides small filters over the picture to detect edges, textures and shapes, then combines them into higher-level features.
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