Artificial intelligence (AI)

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Data Platforms, Business Intelligence (BI), and Artificial Intelligence (AI).

Organizations are constantly looking for ways to become more data-driven, striving to use data analytics technologies instead of intuition, experience or the external environment to guide their strategic planning, innovation and operations. Considerable investment is being made in creating reliable and secure data platforms that are the foundation for deeper use of data through business intelligence (BI) and artificial intelligence techniques and tools. The expected benefits are support for better decisions, efficiency and innovation.

Artificial intelligence is making its way into every area of life and business. Driven by machine learning technologies and expert systems, artificial intelligence is revolutionizing the approach to transforming structured and unstructured data into valuable information or actions.

The adoption of such technologies, however, requires a certain level of readiness on the part of organizations, involving at least two basic dimensions. The first is related to raising the level of data & AI awareness/literacy of all people at every organizational level, which is usually achieved through training. The second dimension refers to a good understanding of what can be achieved with these technologies, but also what costs and benefits they involve. This understanding can usually be gained by engaging in demonstration, testing and experimentation activities.

Data Platforms and AI/ML Awareness ​

Individuals, projects and organizations have their own unique context and specific needs, so there is no “standard” training. That’s why the approach we take to the training effort consists of a set of phases that all boil down to a larger overall value: discovery, adaptation, delivery, follow-up.

Phase 1: we start a stage of Discovery with the client, the final goal being to understand the real needs and objectives, the context and the knowledge and experience of the people;

Phase 2: we adapt the content to suit the specific audience and their learning objectives and design the delivery in terms of duration and types of activities;

Phase 3: we organize intensive, highly interactive and practical sessions; theory is good, but we value “Show me the code!” from Linus Torvalds.

Phase 4: After a while, we meet again in follow-up sessions to understand progress, answer questions and plan next steps.

 

    • People who get involved in our training are exposed to at least the following types of topics:

 

  • Importance of data (A. better decision support, B. efficiency, C. innovation)
  • How to become a data-driven company
    • Approach, strategy and plan;
    • Business cases, problem and solution framing;
    • Skills needed to support digital transformation;
  • Identification of data within the organization.
  • Collecting data and preparing it for:
    • Advanced reporting and analysis;
    • AI/ML;
  • Advanced Reporting and Analysis – Practices and Tools
  • AI/ML
    •  
    • Level of training;
    • Data ingestion, EDA (exploratory data analysis) and data preparation;
    • Building and training models;
    • Implementation and Operationalization (ML Ops)
    • Validation and monitoring
    • AI/ML in end-to-end solutions
    • Security, explainability and ethical considerations;

Based on specific customer needs we can deliver a whole variety of more advanced training modules.

Artificial intelligence can be applied in most fields where human experts are involved, assisting and augmenting their natural capabilities. Below are just a few examples that might inspire you when choosing to implement an AI-based solution:

Health 

  • Real-time alerts and diagnostics
  • Proactive health management 

 

Production 

  • Predictive maintenance
  • Demand forecasting
  • Quality control
  • Improving processes to promote efficiency

 

Retail 

  • Predictive inventory planning
  • Recommendation systems
  • Churn rate prediction

 

Financial Management

  • Credit score
  • Classification of expenses
  • Cashflow forecast
  • Fraud detection

 

Education

  • Prediction of school dropout risk
  • Improved Health scoring systems
  • Real-time alerts and diagnostics
  • Proactive health management

Applicability and success cases

Healthcare
IPGE's HIPPA-compliant platform where real-world data can be seamlessly obtained, de-identified, and hundreds of millions of patient data records compared with the highest possible accuracy. Solution based on a combination of natural language processing and deep learning classification to automatically fit noisy real-world data into standardized sink tables.
Production/Auto
The analytics platform is a solution from one of Europe's largest car manufacturers that centralizes data quality analysis for engineers to enable effective warranty cost reduction, helping them find problems in a short time, plan maintenance cars and generally provide better customer service.

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