“The most valuable thing you can have as a leader is clear data” Ruth Porat, Alphabet Inc. CFO
It is crucial to develop a method to continuously collect and analyze data. In today’s global organization, one of the big challenge is the proliferation of excel spreadsheets. The Excel reporting age is coming to an end…
In most of the cases, the excel spreadsheets contain static data that require manual updates on a regular basis. Finance Security integrates and correlates real-time application data from known vendors in a centralized data warehouse. Our tool covers all Infrastructure assets from the Data Center buildings down to the Application to provide you an holistic view of your Infrastructure including Asset information and relationships.
Machine Learning is revolutionizing the way IT Infrastructure are managed.
Making smart predictions is the core objective of Machine Learning. However, in order to achieve that goal, Applications require clean and structured data/information. Using the Centralized Data Warehouse, our Team of Data Analysts develop algorithms to enable self-managed IT Infrastructure.
Our Team of Data analysts identifies the data sets needed based on customer requirements. They pull together all information, structure and prepare them for analysis.
Our Team provides the right information in a timely manner to support the Operations Team enabling fast and pro-active problem identification and reducing Incident resolution time.
The results are communicated and described to business to aid in their decision making.
How does it work ?
The Centralized Data Warehouse is connected via API to popular and open source technologies such as NetApp OCI, ServiceNow, Openstack, Nutanix, VMware …
AI makes storage devices useful
At its core, Machine Learning is simply a way of achieving Artificial Intelligence.
Instead of hard coding software routines with specific instructions to accomplish a particular task, Machine Learning is a way of “training” an algorithm so that it can learn "how".
Training involves feeding huge amounts of data to the algorithm and allowing the algorithm to adjust itself and improve.
Examples of AI and ML in storage environment are: devices inside of Data Center collect all kind of sensor information (which we call raw data). That data is then used and processed by Machine Learning algorithms. ML/AI makes sense of the data in order to make decisions that will be sent back to sensors in order to preform a specific task in Data Center environment (like: building a holistic view of all data center assets, solving performance issues, optimizing network flow, predicting maintenance etc.).