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When considering the time and effort required to build a catalog and utilize it effectively, combined with the prices, it often does not make financial sense.
Implementing Collibra Data Catalog can be cost-effective if its features align well with the business requirements.
While there are no direct cost reductions, there is significant indirect cost reduction.
The solution can save costs by improving incident resolution times and reducing security incident costs.
When using the Collibra Resident Architect program, the customer service was excellent, with issues quickly resolved.
The technical support from Collibra Governance is excellent.
I encountered some issues with data quality initially in Collibra Data Intelligence Platform, and they resolved those issues.
Mission-critical offering a dedicated team, proactive monitoring, and fast resolution.
From the responsiveness perspective, Splunk is very responsive with SLA-bound support for premium tiers.
I would rate their technical support as 8.5 out of 10.
We were a big bank and had thousands of assets without any issues.
It is very critical for a data governance tool to be stable and scalable because we start with critical data sets in one line of business, but ultimately, we want to cover the enterprise and all lines of businesses.
Collibra Lineage's performance is reliable, as the lineage harvest runs on the lineage server which operates on Collibra cloud.
Splunk User Behavior Analytics is highly scalable, designed for enterprise scalability, allowing expansion of data ingestion, indexing, and search capabilities as log volumes grow.
I rate the stability of Collibra Lineage as seven.
With built-in redundancy across zones and regions, 99.9% uptime is achievable.
Splunk User Behavior Analytics is a one hundred percent stable solution.
Splunk User Behavior Analytics is highly stable and reliable, even in large-scale enterprise environments with high log injection rates.
Users often find it challenging to utilize data governance tools, with ease of use ranked as an important criterion by 2028 standards.
There should be a reduction in cost as compared to other tools.
There is an issue with Collibra Catalog's pricing model, especially for organizations with many databases, as the initial package comes with a limited number of connectors.
Global reach allows deployment of apps and services closer to users worldwide, but data sovereignty concerns exist and region selection must align with compliance requirements.
I encountered several issues while trying to create solutions for this advanced version, which seem unrelated to query or data issues.
High data ingestion costs can be an issue, especially for large enterprises, as Splunk charges based on the amount of data processed.
Collibra has high initial costs for licensing that can be a barrier to small and medium-sized companies starting with it.
There are plans to increase license rates.
Adding modules like Privacy could become expensive.
Reserved instances with one or three-year commitments offer lower rates, providing up to 70% savings.
Compared to all other products in the market, it is the most expensive one in all aspects including professional service and licenses, even the cloud version.
Comparing with the competitors, it's a bit expensive.
My experience with Collibra's collaboration tools in improving data literacy has been quite good. I think it is one of the best for helping people understand and discuss certain data sets and manage workflows.
We have saved up to 30% of manual work as a specific process or workflow became faster.
Collibra Platform will identify that this column appears to have social security numbers and will mask those columns automatically.
I also utilize it for anomaly detection and behavior analysis, particularly using Splunk's machine learning environment.
The dashboards themselves are nice, very good, and very helpful, but the accuracy of the data or the information that will be presented on the dashboard is something that needs to be questioned.
Features like alerts and auto report generation are valuable.
| Product | Market Share (%) |
|---|---|
| Collibra Platform | 9.2% |
| Microsoft Purview Data Governance | 13.8% |
| Varonis Platform | 7.9% |
| Other | 69.1% |
| Product | Market Share (%) |
|---|---|
| Splunk User Behavior Analytics | 6.0% |
| Exabeam | 7.5% |
| IBM Security QRadar | 6.8% |
| Other | 79.7% |
| Company Size | Count |
|---|---|
| Small Business | 20 |
| Midsize Enterprise | 9 |
| Large Enterprise | 55 |
| Company Size | Count |
|---|---|
| Small Business | 7 |
| Midsize Enterprise | 5 |
| Large Enterprise | 12 |
Collibra Platform is preferred for workflows, data lineage, and a user-friendly interface. It enhances metadata management with robust collaboration, flexible customization, and powerful reporting, aiding organizations in effective data management.
Collibra Platform provides dependable solutions for metadata management, data lineage, and governance. It strengthens data governance with cataloging, glossaries, automation, and integration, supporting compliance and data quality management. Despite challenges with integration and metadata ingestion, the platform is vital for data governance programs, offering comprehensive AI capabilities and streamlined processes for enterprise data management.
What are Collibra Platform's key features?In industries, Collibra Platform supports IT teams through metadata management and data quality assurance. It is widely used for compliance initiatives like GDPR, speeding up digital transformation and enforcing policy management. Organizations employ it to consolidate business and technical metadata, ensuring effective enterprise-scale data management in diverse sectors.
Splunk User Behavior Analytics is a behavior-based threat detection is based on machine learning methodologies that require no signatures or human analysis, enabling multi-entity behavior profiling and peer group analytics for users, devices, service accounts and applications. It detects insider threats and external attacks using out-of-the-box purpose-built that helps organizations find known, unknown and hidden threats, but extensible unsupervised machine learning (ML) algorithms, provides context around the threat via ML driven anomaly correlation and visual mapping of stitched anomalies over various phases of the attack lifecycle (Kill-Chain View). It uses a data science driven approach that produces actionable results with risk ratings and supporting evidence that increases SOC efficiency and supports bi-directional integration with Splunk Enterprise for data ingestion and correlation and with Splunk Enterprise Security for incident scoping, workflow management and automated response. The result is automated, accurate threat and anomaly detection.
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