DataLayer Guard Description

You can monitor all tags in real-time, on all devices and browsers. The DataLayer Guard monitors dataLayer in real-time and catches issues before they impact your business.

Real-time alerts notify you of any errors in data collection, ensuring that you do not miss any valuable data from your marketing or analytics tools.

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QRA’s tools streamline engineering artifact generation, evaluation, and prediction, refocusing engineers from tedious work to critical path development. Our solutions automate the creation of risk-free project artifacts for high-stakes engineering. Engineers often spend excessive time on the mundane task of refining requirements, with quality metrics varying across industries. QVscribe, QRA's flagship product, streamlines this by automatically consolidating these metrics and applying them to your documentation, identifying risks, errors, and ambiguities. This efficiency allows engineers to focus on more complex challenges. To further simplify requirement authoring, QRA introduced a pioneering five-point scoring system that instills confidence in engineers. A perfect score confirms accurate structure and phrasing, while lower scores prompt corrective guidance. This feature not only refines current requirements but also reduces common errors and enhances authoring skills over time.
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Big Data Quality must always be verified to ensure that data is safe, accurate, and complete. Data is moved through multiple IT platforms or stored in Data Lakes. The Big Data Challenge: Data often loses its trustworthiness because of (i) Undiscovered errors in incoming data (iii). Multiple data sources that get out-of-synchrony over time (iii). Structural changes to data in downstream processes not expected downstream and (iv) multiple IT platforms (Hadoop DW, Cloud). Unexpected errors can occur when data moves between systems, such as from a Data Warehouse to a Hadoop environment, NoSQL database, or the Cloud. Data can change unexpectedly due to poor processes, ad-hoc data policies, poor data storage and control, and lack of control over certain data sources (e.g., external providers). DataBuck is an autonomous, self-learning, Big Data Quality validation tool and Data Matching tool.
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Anomalo Reviews
Anomalo
Anomalo helps you get ahead of data issues by automatically detecting them as soon as they appear and before anyone else is impacted. -Depth of Checks: Provides both foundational observability (automated checks for data freshness, volume, schema changes) and deep data quality monitoring (automated checks for data consistency and correctness). -Automation: Use unsupervised machine learning to automatically identify missing and anomalous data. -Easy for everyone, no-code UI: A user can generate a no-code check that calculates a metric, plots it over time, generates a time series model, sends intuitive alerts to tools like Slack, and returns a root cause analysis. -Intelligent Alerting: Incredibly powerful unsupervised machine learning intelligently readjusts time series models and uses automatic secondary checks to weed out false positives. -Time to Resolution: Automatically generates a root cause analysis that saves users time determining why an anomaly is occurring. Our triage feature orchestrates a resolution workflow and can integrate with many remediation steps, like ticketing systems. -In-VPC Development: Data never leaves the customer’s environment. Anomalo can be run entirely in-VPC for the utmost in privacy & security
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Pricing

Pricing Starts At:
$79/month

Integrations

View Integrations

Reviews - 2 Verified Reviews

Total
ease
features
design
support
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Company Details

Company:
Code Cube
Year Founded:
2022
Headquarters:
Netherlands
Website:
code-cube.io
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DataLayer Guard

DataLayer Guard

DataLayer Guard
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Product Details

Platforms
Web-Based
Types of Training
Training Docs
Live Training (Online)
Customer Support
Live Rep (24/7)
Online Support

DataLayer Guard Features and Options

Data Quality Software

Address Validation
Data Deduplication
Data Discovery
Data Profililng
Master Data Management
Match & Merge
Metadata Management

DataLayer Guard User Reviews

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  • Name: Feiko B.
    Job Title: Team Lead Tracking & Analytics
    Length of product use: 6-12 Months
    Used How Often?: Weekly
    Role: Deployment
    Organization Size: 1 - 25
    Features
    Design
    Ease
    Pricing
    Support
    Likelihood to Recommend to Others
    1 2 3 4 5 6 7 8 9 10

    Boosting data quality and saving time

    Date: Mar 04 2025

    Summary: We are very happy we chose to start with monitoring in the first place. This solution seems like a very good choice compared to others when looking at the price, the flexibility to cancel every month and the fact that no actual visitor data is being used or stored.

    Positive: DataLayer Guard and Tag Monitor combined allow us to monitor the complete data pipeline. We can solve any issue as it happens, saving time to debug and allowing us to be pro-active towards our customers.

    Negative: For some of our enterprise customers the implementation was less straightforward due to the complexity of the tracking set-up configuration.

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  • Name: Clement T.
    Job Title: Analytics & Customer Journey Optimisation Team Lead
    Length of product use: Less than 6 months
    Used How Often?: Weekly
    Role: User, Administrator, Deployment
    Organization Size: 1,000 - 4,999
    Features
    Design
    Ease
    Pricing
    Support
    Likelihood to Recommend to Others
    1 2 3 4 5 6 7 8 9 10

    A tool that does well the job

    Date: Feb 10 2025

    Summary: Very satisfied with the tool's working and insights. The team is also a formidable work partner, always available and open to ideas we have to improve the tool.

    Positive: DataLayer Guard is a very flexible tool while remaining easy to manage. It allowed us to detect and fix some issues in our tracking plan, reducing the number of missed conversions. Code Cube’s team is also very reactive and attentive to our use and needs for the tool, in a co-constructive approach.

    Negative: It's missing a bit of responsiveness (example: see the scrapped result next to the requirements we have, as we edit them), which could increase even more the speed and complexity of editing configurations / testing scenarios

    Read More...
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