Big Data Brussels 2019 v.4.0 I 'How to Build Big Data Analytics Capabilities in the Organisation?' - Mohamed Ben Haddou
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One of the big challenges of organisations today, is leveraging analytics to convert Big data into actionable decisions. This necessarily goes through building the necessary capabilities. These capabilities need to be the right mix of People, Processes and Platforms. The talk will take each of these components and discuss them.
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Big Data Brussels 2019 v.4.0 I 'How to Build Big Data Analytics Capabilities in the Organisation?' - Mohamed Ben Haddou
- 1. www.mentis.io © 2019 Mentis. All Rights Reserved. 1 How to Build Big Data Analytics Capabilities in the Organisation? Mohamed Ben Haddou | Mentis S.A.
- 2. www.mentis.io © 2019 Mentis. All Rights Reserved. 2 Data science team Paradigms and methods & tools Scalable flexible architecture How to build big data analytics capabilities in the organization?
- 3. www.mentis.io © 2019 Mentis SA. All Rights Reserved. Helping you realize the value of AI
- 4. www.mentis.io © 2019 Mentis. All Rights Reserved. 4 Founded 2005 as a spin-off of Artificial Intelligence lab of the ULB Core technologies and expertise: AI, Semantics, Analytics, Big data Strong engineering and R&D capabilities. Multidisciplinary team of 20 PhDs, engineers and IT Specialits We are technology developers and we help our clients build competitive advantage About UsBringing intelligence to your business
- 5. 5 www.mentis.io © 2019 Mentis. All Rights Reserved. Data science sourcing AI Consultin g Technology Developme nt About UsBringing intelligence to your business
- 6. • Hire super heroes. • Reward self learners. • Promote creativity Data science team In a Gartner survey, business leaders indicated that the lack of necessary skills is the top challenge to adopting Big Data Analytics
- 7. Paradigms and methods & tools A data scientist need is math wizard, with domain expertise, who can build applications and design high-end computing systems. what is a Data Scientist?
- 8. 8
- 9. Paradigms and methods & tools. Domain expertData scientist Application DeveloperData Engineer 1-Create a team
- 10. www.mentis.io © 2019 Mentis. All Rights Reserved. 10Paradigms and methods & tools 1 Start with Current Data Platforms 2 Use machine generated data 3 Data quality Framework 4 Focus on data processing requirements 5 communicate 2 Generate ideas Data centric process 1 Start with opportunities that fit the industry 2 Use existing data science platforms 3 Automate existing processes 4 Use cloud based services 5 Leverage open source frameworks 2 Pretrained models Technology centric process 2-Generate and qualify projects
- 11. www.mentis.io © 2019 Mentis. All Rights Reserved. 11 Assess quality on production Define problem What exactly are the objectives Design data science pipeline Enable data science workflow Integration Analysis Paradigms and methods & tools 3-Guiding Framework
- 12. www.mentis.io © 2019 Mentis. All Rights Reserved. 12 PUBLIC CLOUD Storage/compute ON PREMISSE Storage/Compute DATA GOVERNANCE ANALYTICS MULTIFUNCTION HYBRID MULTICLOUD SECURE OPEN Scalable flexible architecture
- 13. www.mentis.io © 2019 Mentis. All Rights Reserved. 13 Corporate Systems Logistic s ERP CRM Sales Production Data Interface GDPR Securit y Semantic s Data Engineering Data Storage Data Science Data Services Client facing applications Data Applications ML/AI Notebooks Data assets Visualization Catalogs Shared services APIs
- 14. www.mentis.io © 2019 Mentis SA. All Rights Reserved. contact@mentis.io +32 2 647 54 41 www.mentis.io MENTIS Questions?
Notes de l'éditeur
- The business domain expert can work with a data scientists to identify potential use cases from the current state of data. The data scientist can then help build statistical models using the available data science tools or technologies within the organization's tech stack. Once the models are built, the platform engineer can help design and build the infrastructure needed to train the models. The application developer can then help build the front-end application
- The data-centric approach identifies near-term opportunities based on the availability of high-quality data and aligns investments to where technical professionals can immediately begin developing skills with machine learning and machine-learning-powered AI (MLpAI), rather than data preparation Targeting these opportunities allows teams to move quickly to apply technologies to the analysis, prevents data quality initiatives from delaying progress, and creates early wins within the AI realm