everis Data & Cognitive

Creating digital intelligence to transform organizations

Digital transformation has a guiding principle: to apply new analytic and cognitive models to achieve “actionable” intelligence.

 

We work toward generating “actionable” intelligence in real time by applying artificial intelligence with analytical and cognitive models based on sources of newly available data for companies.

 

A decisive factor in remaining competitive is to incorporate data analysis that originates from diverse sources such as social media, sensors and call centres in the decision making process.

 

As a result, the capacity that a company has to identify, process and analyze that data is a key success factor which requires transforming companies’ current business models, operational models and technological models.

 

In order to achieve the desired value from this new data, it is necessary to know:

 

  • What data is important for the company’s business.
  • How to access and verify its sources.
  • How to gain value with analysis models.
  • How to adapt processes to the information.
  • How to present the results in order to visualise, exploit and connect them.

 

This new flow of information also requires a structural transformation within companies. The weight of intuition in the decision-making process will be replaced by analysis. New business opportunities will emerge from the information that has been obtained and its possibilities. New areas of expertise in data and analytics will be created, which should have a deep understanding of the business and of the possibilities that technology gives us.

 

The results obtained from data analysis will operate dynamically on the operational and business processes of the company. The data, now a valuable asset, will have specialized organisational structures in its governing bodies and management.  A paradigm shift will take place in technological companies, which will accelerate the adoption of new technologies.

 

Our data strategy

 

1. – Strategic advantage of data:

 

  • What and how to improve:
    • New strategies and ideas
    • Trends and analysis
    • Competitors

 

  • What data has answers
    • Legal and compliance
    • Security and veracity
    • Value

 

2. – Make algorithms work:

 

  • How to obtain answers:
    • Data to knowledge
    • Text to knowledge
    • Knowledge to intelligence

 

  • How to use data feasibly
    • Artifical intelligence
    • Cognitive computing
    • Advanced sensing systems

 

3. – Technology:

 

  • Analytics
  • Data visualisation
  • Data trasnformation
  • Logical architecture of data
  • Physical architecture of data
  • Data administration
  • Data infrastructure (cloud, hybrid, local)