Infrastructure

Datavision’s unified platform for logs and metrics is addressing the need that most of today’s organizations are facing of having a central repository where placing and correlating in real-time the different logs coming from their stack elements.

A centralized log repository is a mechanism that once in place helps to have a coherent holistic picture compiling all the information coming from the different silos that are included within the organization. Having all this information handy helps organizations to diminish the MTTR (Mean Time To Resolve).

When facing a situation Datavision’s intelligent log management solution will help to make sense of what is happening in the affected environment by providing insights into the different components that are related to the issue.

Insight capabilities are provided by Datavision monitoring features. Such capabilities enables business benefits based on having:

DATA TRENDS AND ANALYSIS VIA OUR REAL-TIME DASHBOARDS

Enable data-driven insights and decisions. Monitoring in an efficient way means having access to critical information that is valuable for not only IT department but other business stakeholders

INTEGRATION WITH ENTERPRISE SOLUTIONS

Enable data-driven insights and decisions. Monitoring in an efficient way means having access to critical information that is valuable for not only IT department but other business stakeholders

Data trends and analysis via our Real-Time Dashboards.

Enable data driven insights and decisions. Monitoring in an efficient way means having access to critical information that is valuable for not only the IT department but other business stakeholders.

Integration with Enterprise Solutions.

Understanding how critical it is that the different systems and technologies across your business ecosystem work together, Datavision’s approach provides an Open API that allows third parties to get connected having access to the different capabilities offered by Datavision Open Platform. And all the way around, Datavision is fully capable of using the APIs offered by the most relevant third parties to get the most valuable metrics and KPIs that these systems can provide.

Data Collection

Organizations today are producing tons of data coming from a variety of sources, both physical and virtual. Datavision’s platform is fully capable of gathering data from these sources, having been tested to handle inflows from high bandwidth sources such as Mainframe systems.

Data Transformation and Aggregation

Datavision is fully capable of aggregating data that has been sourced from different domains. By doing this it improves original data value by combining information that may be coming from infrastructure assets, networking devices or application performance metrics.

Data Enrichment

On the top of transformed and aggregated data, original raw data can be enriched by applying metadata and additional tags that will provide context for potential searches and indexes.

Analytical Insights

Datavision is fully capable of providing granular meaningful insights, being this the best help to assist in forensic tasks aimed to discover whatever is impacting business operations performance.

Insights may be derived based on a simple statistical analysis, a plain correlation or based on Datavision’s anomaly detection system.

Automation

Previous insights are useful, but is not enough, preactive remediation actions are required in order to recover situations affecting performance. Prescripted actions based on Datavision’s insights will facilitate and speed-up the time to resolve issues found.

Ease of Use

Dealing with most of today’s AIOps solutions in the market may become a cumbersome and awkward experience. Datavision’s platform since its inception has ‘simplicity’ as the driver of all decisions that our skilled team of engineers make.

Datavision is as a result a powerful yet easy to use interface for both technical and business roles. Having both the granularity and detail that may be required to get a technical issue solved, or the bird eye view that via a meaningful abstraction may hide noisy data from the real valuable one.

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