11Search results
AI Industrialization: the key steps to a MLOps approach
The industrialization of artificial intelligence - one of the 7 hot data topics for 2022 requires the implementation of MLOps. This approach includes some necessary steps, including a common platform...
Data Science: the 4 obstacles to overcome to ensure a successful project
The last five years we have seen the number of Data Science projects carried out by Business & Decision in various sectors, such as the oil industry, telephony, retail and...
WHITEPAPER | Data Ethics/AI Ethics: the 2 faces of a responsible future
Artificial Intelligence is at the heart of all attentions and concerns right now. Did you know that the real difficulty with Artificial Intelligence is not the algorithms, nor the design...
WHITEPAPER | Artificial Intelligence: Stay in control of your future!
If there is one topic that really ignites passion and fuels all ideas and discussions in the world of new technologies, it’s Artificial Intelligence. What are the opportunities for enterprises?...
Statistics versus Machine Learning: should they really be opposed?
This "seemingly" old debate deserves to be revisited with fresh perspective. Data Science (such as Big Data) is a constantly evolving field with nowadays proven applications namely in the fields of customer knowledge and marketing… Statistics...
The 5 key Data Science practices
In the wake of Big Data, many companies embarked on the Data Science journey, the field having established itself as the inescapable route towards Big Data transformation into knowledge and...
Can a whole Data Science project be done using R or Python?
For several years now, many Data Scientists have found themselves turning to "language" command line tools, such as R and Python, to deal with Big Data. But can you really undertake a whole Data...
The key to Data Science success is the CRISP methodology
The CRISP methodology (originally known as CRISP-DM), first developed by IBM in the 60s for data mining projects, remains, today, the only truly efficient process used for Data Science projects… CRISP methodology: User guide The CRISP methodology includes 6 steps that range from business understanding to deployment...
Data Scientist/Data Engineer: the skills required to give you a head start in Data Science
Back in 2012, the Harvard Business Review published an article with a somewhat revealing title: "Data Scientist: The Sexiest Job of the 21st Century". Years later, we revisit this vision...
Data Engineer: which training programs to choose?
To all those young people wishing to embark on a career in Data Science, my advice was to begin with a Data Engineering job rather than directly as a Data...
Artificial intelligence, machine learning, data science: are these terms interchangeable?
Many writers talk about AI, machine learning and data science, as if these terms were broadly interchangeable. What’s going on exactly?
Data Governance and Data Management: what's the difference?
In a world where companies' ambition is to be data-driven, data governance and data management are still too often regarded as being synonymous. Let us clear up the confusion. Data...
REPLAY | Let’s win the Data Mesh Battle: the winning alliance between Data Architecture and Data Governance
The Data Mesh vision has brought to light the various challenges that companies face in managing and effectively utilizing their data. This is not a new challenge, as it has...
REPLAY | The missing pillars in the Data Mesh approach
Is Data Mesh a utopia? For two years now, the concept of Data Mesh has been seen as a revolution in the world of data since it would fill the...
WHITEPAPER | Spiderman guides you towards a data-driven company
There is tremendous enthusiasm for Data Mesh. And for good reason: we finally have a complete framework for valuing data at company level. This white paper offers you a deep...
Data Mesh, a total data-driven model
Through its four main pillars, Data Mesh truly moves away from the dogma of centralisation and all-technology in favor of a global approach based on federation. Data Mesh thus promises...
#Data #AI: 7 hot topics for 2023
The 7 hot topics Data and AI of this 7th edition are the solutions for the performing company. What are specifically the trends and topics to track in 2023? This...
Data Mesh: Practical examples and feedback
Mastering data and its uses to create value is an ambition that is increasingly shared. However, organisations continue to face obstacles that Data Mesh could help to overcome… provided the...
Data Mesh: federated governance to guarantee efficiency
Data governance is an essential part of any data strategy. Nevertheless, it remains complex to deploy in a traditional organisation, but through its federated approach, Data Mesh is able to...
Data infrastructure self-service as the technological driving force behind Data Mesh
Data Mesh is not strictly speaking a technological approach, but data domains need powerful technical resources to develop their products. The data platform and its infrastructure are a facilitator for...
Data Mesh: data is a product
Oil, digital black gold, strategic asset… With Data Mesh, data is regarded as a product. Data domains are responsible for managing the life cycle of these products and for sharing...
Data domains: Data Mesh gives business domains superpowers
The Data Mesh concept is based on four main pillars, the first of which is an organisation divided into data domains. To be effective, this structure must reflect the business...
Data Mesh:The ultimate model for data-driven companies?
A new paradigm for data management, Data Mesh breaks with data centralisation models used for the past 30 years. Its foundations: federated decentralisation and redistribution of responsibility for the benefit...
TUTORIEL | Spark Structured Streaming: performance testing
Spark is an open source distributed computing framework that is more efficient than Hadoop, supports three main languages (Scala, Java and Python) and has rapidly carved out a significant niche...
Green AI: Responsible artificial intelligence is also frugal
When it comes to Artificial Intelligence, it’s not only about improving performance at any costs. Its benefits along its adoption requires AI to be responsible by also including an environmental...