Certification, inspection and audit solutions focused on business optimization.
CONFIDENCE
SECURITY
FLEXIBILITY
NOTE: THIS WEBSITE DOES NOT USE COOKIES OR ANY MEANS OF VISITOR CONTROL.
244 Fifth Avenue, Suite 1203, New York, NY 10001 US
INTERCER NORTH AMERICA BIG DATA
INTERCER NORTH AMERICA is a specialist in security systems and data analysis for any type of industry.
Our specialty is:
Vulnerability assessment
The rise of sophisticated attackers and the increasing reliance on cloud-based services and SaaS increases the need for greater trust and more useful intelligence to expose your organization to security incidents.
Penetration test
New vulnerabilities are discovered with surprising speed. Threat actors analyze these vulnerabilities to determine if exploit code is available or can be developed.
DATAVALUE
Data Value encompasses every activity to extract value from data.
Data Management
Data Quality
Data Governance
Regulatory Compliance
Business Discovery
ADVANCED ANALYTICS
Advanced Analytics focuses on designing and developing advanced analytics models to solve business problems.
Use of Machine Learning techniques to offer the most suitable solution
We design solutions for sales forecasting, NLP, Optimization,
Fraud detection, pricin
CUSTOMER CENTRIC
Customer centricity is the road to maintain and increase customer value.
Get the client to the center of the processes, getting a unique client vision and offering an improved customer experience
Client management strategy design (acquisition, loyalty, etc.)
Marketing actions management automation, with an omnichannel, real-time point of view
SERVICES
BIG DATA ANALYSIS: is the technology used to analyze a huge amount of structured and unstructured data that is collected, organized and interpreted by software, transforming it into useful information for decision making and to generate ideas about market trends and behavior of its consumers and data of the company itself.
Structured data are those already organized in a way that makes it easier to view and read the information, while unstructured data are still loose data, such as texts, images and results of unorganized campaigns that do not have the same data profile.
DATA SCIENCE: Is the extraction of exploitable information from raw data. The data comes from all departments and activities of the company and its main objective is to identify trends, concepts, reasons, practices, connections and correlations in large data series, which is used to make decisions. Data Science allows you to make decisions based on data, instead of simple intuition.
DASHBOARD: is an information management tool that monitors, analyzes and visually displays key business/performance indicators and fundamental data to track the status of a company, a department, a campaign or a specific process.
It is a control panel that allows the management team to manage the company.
SMART AUDIT:
- Define the data and information that the company needs and why it wants it
- Define where the data and information that the company requires are
- Define the data and information structure/architecture of the company and its software and hardware
- Define the personnel that manages the data and their qualifications
- Define the use that personnel make of the data and where they obtain it from (origin and destination).
- Define location of data and servers / clouds
- Create a final report where you collect.
ˇ Structure, location, data and information
ˇ Classification of information and data (for company use. Critical, important, relevant).
ˇ Determine the profile of the personnel who manages the data
ˇ Determine the company's information/data management operations
ˇ Determine the objective of using the data and what you want to do with it.
ˇ Determine the next step, that is, if the company requires Big Data analysis, Data Science, Dashboard
DATA MINING:
The actual data mining task is the automatic or semi-automatic analysis of large amounts of data to extract interesting, hitherto unknown patterns, such as groups of data records (cluster analysis), unusual records (anomaly detection), and dependencies ( mining by association rules). This usually involves the use of database techniques such as spatial indexes.
These patterns can then be seen as a kind of summary of the input data, and can be used in further analysis or, for example, in machine learning and predictive analysis.
For example, the data mining step could identify various groups in the data, which can then be used to obtain more accurate prediction results by a decision support system. Neither data collection, data preparation, nor interpretation of results and information are part of the data mining stage, but they belong to the entire KDD process as additional steps.3?