Next generation technologies (big data, cloud, IoT etc.) are transforming the way business is done, compelling organizations to adopt and optimize their IT infrastructure to stay competitive and meet customer demands. Understanding issues in real time is critical to managing IT operations effectively before they cause significant disruptions and impact the business adversely.

GAVS’ analytics service helps businesses to harness their big data without depleting their human or infrastructure resources. We provide an integrated environment and a range of techniques and processes for the collection, classification, analysis, and interpretation of data to reveal patterns, vital relationships among data variables, ultimately leading to new insights and better decisions.

Our offerings include:

  • ITOA driven datacenter management
  • Predictive security analytics and intrusion detection


ITOA Driven Datacenter Management

The basic premise of ITOA is to cut the mean time to resolution (MTTR), significantly reduce downtime, and improve end-user experience. To achieve these goals, it requires a continuous improvement effort, and the ability to unify and analyze across operational data sets.

GAVS’ ITOA driven management facilitate businesses to:

  • Continuously learn about the applications, resources, and establish thresholds that monitor data and identify seasonal events
  • By predicting, and adjusting to operational changes, ITOA solutions can identify emerging issues across the infrastructure, detect defects, consolidate related detects and anomalies. It helps to proactively track any service related issues and suggest suitable recommendations
  • Using predictive and cognitive analytics, GAVS’ solutions help identify the most problematic areas of your operations

Predictive Security Analytics and Intrusion Detection

Cybersecurity experts and analysts are constantly trying to keep pace with changes and trends in the dynamic and ever-shifting IT security. It’s imperative for organizations in every industry to modernize and reinvent their business models keeping end-user security in mind.

If the security officer wants better security insight, monitoring, and surveillance, they must reconsider their monitoring and data source management. It should deviate from the traditional analysis of data silos of log management, anomaly detection, packet capture systems, and malware monitoring systems.

Predictive security analytics are gaining momentum in virtually every industry and is enabling organizations to revamp the way they do business by looking at data driven insights and obtain foresight they lacked previously.

Predictive algorithms enable IT security technicians to pre-empt and resolve incidents. It provides the status of tickets through dynamic visualization and uses statistical models on past incidents to help assign accurate estimated time to resolve issues thereby improving customer satisfaction.

Utilizing self-learning analytics and anomaly detection techniques, security teams can monitor activity across multiple network assets and real-time data streams to identify threats in real time without having specific knowledge of the exact signature. These analytics immediately detect anomalies in network traffic and data flows, while also quickly recognizing new “normal” activity, thus minimizing false-positive alerts.

Using security analytics can provide a data-driven assessment to guide executive decision making. Equipped with this data, the C-level and board members can better understand the organization and its risk so they can address the following issues:

  • What is our overall risk posture?
  • What are our high-value targets?
  • What are the risks if our high-value targets are compromised?
  • What are the most cost-effective ways of reducing risks?

ITOA Driven Datacenter Management

IT Operation Analytics (ITOA) assure superior customer services and achieve competitive advantage. Using ITOA, businesses optimize existing operations management for quick problem solving and better services. Enterprises can extract insight from operational data types such as log files, events, performance metrics etc. to achieve below results:

  • Proactively avoid outages, unforeseen events etc.,
  • Prevent issues & problems that affect business revenue
  • Achieve better repair time and efficiency (MTTR)
  • Realize cost savings through greater operational efficiency



Predictive Security Analytics and Intrusion Detection

First, businesses face IT security challenges from the exploding and exponential rate of value and volume of online assets. And, hackers are increasingly growing in sophistication due to their easy and inexpensive access to large computer resources through cloud computing. The use of predictive analytics coupled with machine learning and natural language processing allows for cybersecurity to move beyond the traditional security measures of signature matching, maintaining black-lists and investing in huge security resources and tools. It is a complete end-to-end solution for modeling, analyzing and visualizing data.


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