Expertise and consulting services to help clients drive transformation with data, analytics and artificial intelligence

A complete expertise dedicated to your projects

Through deep expertise in technology, data and analytics, Neos Analytics helps clients realize their full potential and accelerate their growth. Whether you are getting started, or already advanced in your digital transformation, our team of experts is here to fast-track your initiatives.  

From the strategic planning of a data strategy, to the implementation of a machine learning model, Neos Analytics helps clients maximize the value of their data. By aligning strategygovernance, and data engineering, our team brings the digital backbone of your company to life. 

From Business Intelligence to Artificial Intelligence


Strategic Planning

Creating a vision and implementing a data strategy can be a daunting task for many organizations. One may face skepticism from a changing set of technologies, or from previous experiences that have had mixed success.

As a firm specializing in data and analytics, Neos Analytics supports organizations that need to realign their investments in analytics, and fully capitalize on them. Our team works with clients to define clear analytics objectives and identify the desired outcome. We thrive at creating a pragmatic program that considers the reality of each of our clients, by connecting the dots between technology, human, and financial resources.

Solution Design and Planning

While strategic planning is crucial to ensuring that analytics initiatives support the business strategy, it is also essential to have the right technology in place. Neos Analytics brings years of experience in cloud technology, allowing our clients to reduce implementation efforts.
This optimization is facilitated using software components in the public domain and using cloud services such as "Platform-As-A-Service". Through proofs of concepts, we can quickly validate the solution offered, and mitigate risk.


Data Governance

Data is the main asset of your business and influences your success. You will only be able to use your data and carry out a successful digital transformation if you are able to govern your data. This means that it is imperative to deploy a data governance framework that suits your organization, your future goals and business models.
This framework must outline the necessary data standards and delegate the roles and responsibilities required within your organization, in relation to your business ecosystem. Our team can help you define a governance framework that will consider your specific needs and challenges, as well as operationalize this framework.

Security & Privacy

If your data is part of your company's assets, it goes without saying that it must be protected, and its access controlled. To do this, Neos Analytics helps clients by considering the following main components:
  • Data Classification
  • Identification and Access Management
  • Data Protection
Once security needs are clearly identified, our team aligns the right mechanisms in order to define the architecture of the analytics solution, and its various layers. This exercise requires rigor and a great knowledge of the various security mechanisms put forward by cloud service providers.

Data Engineering

Data Acquisition

Data acquisition allows you to bring data into the organization, from external sources. The data ingestion phase brings this data into a storage medium where it can be transformed, consulted and analyzed by the organization. This data is usually stored either in a data lake, a data warehouse, or directly within analytics applications.
The data ingestion layer is the backbone of any analytics solution architecture. Downstream analysis systems are based on clean, consistent, accessible and sufficient data to be meaningful. There are different ways to ingest data, and the design of a data ingestion layer can be based on different models or architectures. Our data engineering experts seek efficient data processing to limit unnecessary duplication, while allowing access according to the context of use.
Neos Analytics can also help clients make the most of their metadata. Although metadata is frequently overlooked, it remains an essential part of the puzzle. We can assist our clients with characterization, taxonomy analysis and data cataloging. This will make it even easier to allow the reuse of existing datasets, according to different context analysis.

Data Cleansing

The statement "Garbage in - Garbage out" describes a reality faced by many organizations, often without even knowing it. Erroneous or incomplete data produces erroneous and incomplete information, and misguided decisions follow.
Neos Analytics recommends the initial evaluation of critical data according to the following 5 criteria:
  • Validity (level of data compliance)
  • Accuracy (level of data accuracy)
  • Completeness (the level of assurance that all necessary data is present)
  • Consistency (the level of data consistency within the same dataset or between different datasets)
  • Uniformity (the degree of adherence to standardized measurement units)
Once the data analysis has been completed using a data profiling technique, the data will be cleaned and verified, and then produce a data compliance report.

Data Science

Descriptive Analysis

Descriptive analysis tends to answer the question "what happened?" To do this, organizations frequently use predefined reports, already available in a CRM or ERP. Data from these systems is therefore used to describe and characterize past or present events. Managers then produce summary reports of these datasets.

Business Intelligence Diagnostic Analysis

Companies will favor the use of diagnostic analysis in order to understand "why did this happen?". From known results, using data from different source systems and lines of analysis, managers try to determine the factors and events that contributed to this event. Thus, by using an appropriate analysis platform and the advanced analysis functions it provides, it becomes possible to provide users an increased autonomy to perform analysis, supporting decision-making.

Prescriptive/Predictive Modeling

Predictive analytics represent a huge leap in complexity as it tackles uncertainty in the future. It proposes, using complex mathematical tools, to transform the information obtained so far, into knowledge. This knowledge, by calculating the probability of an event occurring in the future, allows us to make educated decisions. Predictive analytics help lift part of the veil of future uncertainty and help make the best educated decision possible. This is the field of predictive modeling, Machine Learning and Deep Learning.

Prescriptive analysis is mathematically identical to predictive analysis but reversed over time. If predictive analysis teaches us, for a given present universe, the probability of an event occurring in the future, prescriptive analysis tells us what we need to change in our present universe in order to increase the probability of occurrence of a given event in the future.


Static predictive modeling

A model is a mathematical equation that has parameters. Each parameter represents a correlation between two variables. In a classic or static model, the parameters are fixed and therefore do not vary over time. However, the universe evolves, the correlations change and the models therefore become obsolete over time. It is therefore necessary to recalculate them regularly.

Static models, however, have the advantage of being able to explain the origin of a prediction, which is useful, and sometimes mandatory, in regulated industries such as banking and finance.


Dynamic predictive modeling - Machine Learning

In order to counter the obsolescence of predictive models over time, it has been made possible the automatic recalculation of parameters with the addition of new observations. This recalculation is done thanks to the use of cross validation and thanks to a regularization controlled by hyperparameters. Predictive models designed in machine learning or Machine Learning adapt automatically as new observations are added. This brings a considerable gain in efficiency, time and precision.


Neural network / Deep Learning

Neural networks first appeared in the 1960s and have continued to improve. Designed to mimic the functioning of a human brain, they are made up of "neurons" or "nodes" through which pass a flow of data, controlled by activation functions. Neurons are arranged in layers. When a network has more than 7 layers, we speak of a deep neural network and therefore of Deep Learning.

These networks are widely used today, among others in complex applications of image and sound recognition, voice recognition and writing.

Artificial Intelligence

  • RPA
  • Intelligent Agents
  • Process Discovery & Improvement Opportunity
  • Business Case Preparation
  • Process Mapping & Optimization
  • Solution Building, "Piloting" & Go-live

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