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Machine Learning & 
BigData Analytics

The value of data and their use in the real world


The intrinsic value of Data

 

Data represents the engine of transformation in the digital economy, and this is the reason why it is considered “the oil” of new economy.

Data are the least exploited, and least valued assets of an organization. They hide some of the most important information that can be possibly acquired, at the IT level and throughout the company:

  • where problems occurred
  • how to optimize the user experience
  • the traces left by fraudulent activity
  • analyzing historical data to find recurring patterns
  • predicting future trends in a Data Driven economy

All this information can be identified in data generated by the organization’s normal activity.

Coaching and Training

 

The use of advanced tools becomes ineffective if it’s not supported by a proper training program, that can allow our customers to create real value from their innovation projects.

Predictive Analysis

 

The implementation of advanced predictive models makes it possible to make all the necessary elements available to the Decision Maker, in order to make informed and weighted decisions.

The presentation of this data is proposed through Machine Learning algorithms and Business Intelligence systems that, in an intuitive and interactive way, allow an effective synthesis and adherence with the customer’s operational reality.

Business Intelligence

 

Through specific analytical software supported by ML and Deep Learning algorithms, Big Data can drive business choices towards more effective marketing operations, better customer service, a richer and more targeted offer, a more efficient use of time, money and operations, and, in general, towards more conscious choices (Data Driven Business).

Tech BigData


The creation of a data-lake, in which all data converge (structured and unstructured), is supported by the most advanced technical solutions on the market, where scalability, security and fault-tolerance are the pillars: the Hadoop ecosystem and the Data Science platforms connected to it, allow the integration with existing systems and the evolution towards innovative solutions. Through distributed, In-Memory computing, Spark allows the elaboration of an immense amount of information and the application of the most complex ML algorithms and neural networks, to extract knowledge useful for business.


Areas of application

 

SOC 4.0 and Cybersecurity

 

With the advent of Big Data and its related analytical techniques, it is possible to use raw and/or unusable data sources, also by correlating information that is external to the IT world, considering it’s effect on business by providing results to the stakeholders.

 

In 2018, 90% of organizations feel vulnerable to insider attacks, both voluntarily and due to negligence, mainly related to the high number of privileged accesses given, unmonitored devices connected to the corporate network, and IT’s increasing complexity.
Often the data are not centralized in internal silos but reside in external environments: this implies that there is no longer a well defined, stationary perimeter, that clearly separates the company from the outside world.

 

“Product-based” Cybersecurity, built only on its technological part (DLP/AIM/PAM), is doomed to to fail, because it’s necessary to understand and integrate the business operation processes: therefor, to ensure that security becomes a company value, it is necessary to acquire an analytics engine that implements machine learning and neural networks predictive algorithms, in order to allow an advanced event correlation.

 

 

 

Maritime

 

At our headquarters in Genoa , we have set up a class “C” bridge simulator, equipped with an instructor station, to manage all the aspects of simulation based training. Our simulator allows to accomplish various types of training, both commercial and educational, also by creating cyber attack scenarios on the vessel: through Anomaly Detection algorithms we are able to detect any unplanned behaviour.

 

 

 

IoT and Industry 4.0

 

Data analysis allows companies to know when the machines are about to break, and carry out the necessary maintenance, before the machine stops or breaks. Another element to consider is the reduction of repair costs, which, in case of highly specialised machinery, can weigh heavily on the budget of any company, no matter how big or small.

The development of sensors and the broadband diffusion (both fixed and mobile ) allows to find data in any condition and geographical location.

 

Airports

Optimization and savings in passenger service delivery and in flight information

Sport

Control, training management and shape status predictions

Healthcare

Prevention, research, and targeted interventions, for less invasive techniques and light post-operative recovery

Insurance Companies

The use of blackboxes to define your driving style, in order to adapt the insurance premium

M.M.R.

Manage the information contained in the consumer profile, to define targeted and personalized business strategies

Sustainable Traffic & Smart City

Using travel records to intervene on traffic and mobilty, making it smarter and more intuitive

Tourism & Travel

Directing investments and allocating human and logistical resources, according to future trends and fashion.

IA & Finance

Artificial intelligence is increasingly a strategic asset for everybody: governments, corporations, and even investment banks.