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Organizational Embedding of Data Science & AI for a Leading Manufacturing Company",{"rendered":505,"protected":506},"\n\u003Cp>Learn how we guided a German manufacturing leader throughout their AI journey by assessing their maturity and implement a Data Science Platform, standardizing an agile project management and establishing data governance processes.\u003C\u002Fp>\n\n\n\u003Ch2 class=\"wp-block-heading\" id=\"h-context-amp-key-challenges-from-experimentation-to-operationalization\">Context & Key challenges: From Experimentation to Operationalization\u003C\u002Fh2>\n\n\n\u003Cp>Our client is a leading German manufacturer of white goods, recognized for high-quality products across their entire portfolio. When we first met, they had just moved their first use case from experimentation to operationalization by putting their very first use case into production.\u003C\u002Fp>\n\n\n\n\u003Cp>However, their approach had significant gaps:\u003C\u002Fp>\n\n\n\n\u003Cul class=\"wp-block-list\">\n\u003Cli>\u003Cstrong>Fragmented operations\u003C\u002Fstrong>: More than 10 data science labs working on similar use cases with completely different methods\u003C\u002Fli>\n\n\n\n\u003Cli>\u003Cstrong>Manual processes\u003C\u002Fstrong>: Very artisanal approach to managing machine learning model lifecycles\u003C\u002Fli>\n\n\n\n\u003Cli>\u003Cstrong>Siloed organization\u003C\u002Fstrong>: Decentralized Data Science teams using different tools and approaches\u003C\u002Fli>\n\u003C\u002Ful>\n\n\n\n\u003Cp>They needed our expertise to:\u003C\u002Fp>\n\n\n\n\u003Cul class=\"wp-block-list\">\n\u003Cli>Challenge their current approach and lab initiatives\u003C\u002Fli>\n\n\n\n\u003Cli>Structure and operationalize model development processes\u003C\u002Fli>\n\n\n\n\u003Cli>Improve their go-to-market approach and effectiveness of their data products\u003C\u002Fli>\n\u003C\u002Ful>\n\n\n\u003Ch2 class=\"wp-block-heading\" id=\"h-our-approach-ai-maturity-assessment-and-centralized-platform-implementation\">Our Approach: AI Maturity Assessment and Centralized Platform Implementation\u003C\u002Fh2>\n\n\n\u003Cp>We addressed their challenges through a comprehensive two-phase approach:\u003C\u002Fp>\n\n\n\u003Ch3 class=\"wp-block-heading\" id=\"h-phase-1-ai-maturity-assessment\">Phase 1: AI maturity assessment\u003C\u002Fh3>\n\n\n\u003Cp>Assess the company \u003Cstrong>\u003Cem>AI Maturity Level\u003C\u002Fem>\u003C\u002Fstrong> and the organization’s infrastructure and processes dimensions in detail. Among others, we discovered that more than 10 data science labs were working on similar use cases in ten different ways.\u003C\u002Fp>\n\n\n\u003Ch3 class=\"wp-block-heading\" id=\"h-phase-2-strategic-implementation\">Phase 2: Strategic implementation\u003C\u002Fh3>\n\n\n\u003Cp>Based on this comprehensive analysis, we provided a set of recommendations to enable our client to operationalize their initiative. Among them, we strongly insisted on the need to create a competence hub including IT, Business and Data Science teams. Their role is to \u003Ca href=\"https:\u002F\u002Fcbtw.tech\u002Finsights\u002Fdigital-transformation-in-renewable-energy\">manage a centralized Data Science Platform\u003C\u002Fa> but also provide guidance and processes for developing and deploying ML models.\u003C\u002Fp>\n\n\n\n\u003Cp>Based on our findings and with a clear AI strategy in mind, our client decided to extend our support to include:\u003C\u002Fp>\n\n\n\n\u003Cul class=\"wp-block-list\">\n\u003Cli>\u003Cstrong>Data Science Platform Creation:\u003C\u002Fstrong> Built a centralized platform in collaboration with a newly established Hub of Excellence and by implementing MLOps best practices\u003C\u002Fli>\n\n\n\n\u003Cli>\u003Cstrong>Agile Project Management\u003C\u002Fstrong>: Standardized project management structure ensuring its adoption using Jira and covering the entire journey from Proof of Concept to MVP to Production\u003C\u002Fli>\n\n\n\n\u003Cli>\u003Cstrong>Governance Framework\u003C\u002Fstrong>: Established comprehensive processes for data product lifecycle management\u003C\u002Fli>\n\n\n\n\u003Cli>\u003Cstrong>Use Case Deployment:\u003C\u002Fstrong> Implemented and deployed the first production-ready use cases\u003C\u002Fli>\n\u003C\u002Ful>\n\n\n\u003Ch2 class=\"wp-block-heading\" id=\"h-benefits-centralized-ai-operations-and-audit-proof-model-deployment\">Benefits: Centralized AI Operations and Audit-Proof Model Deployment\u003C\u002Fh2>\n\n\n\u003Cul class=\"wp-block-list\">\n\u003Cli>\u003Cstrong>Clear \u003Ca href=\"https:\u002F\u002Fcbtw.tech\u002Finsights\u002Fthe-three-main-benefits-of-a-well-designed-ai-strategy\">AI strategy\u003C\u002Fa>\u003C\u002Fstrong> with standardized processes and best practices\u003C\u002Fli>\n\n\n\n\u003Cli>\u003Cstrong>Increased efficiency \u003C\u002Fstrong>while establishing innovative methods to become a \u003Ca href=\"https:\u002F\u002Fcbtw.tech\u002Finsights\u002Fdata-literacy-for-a-data-driven-decision-mindset\">data-driven company\u003C\u002Fa>.\u003C\u002Fli>\n\n\n\n\u003Cli>\u003Cstrong>Centralized Data Science Platform\u003C\u002Fstrong> with consolidated know-how, new roles (Data Architect), and governance frameworks\u003C\u002Fli>\n\n\n\n\u003Cli>\u003Cstrong>Transparent project controlling\u003C\u002Fstrong> using agile project management frameworks\u003C\u002Fli>\n\n\n\n\u003Cli>\u003Cstrong>Audit-proof operationalization\u003C\u002Fstrong> of machine learning models and data products\u003C\u002Fli>\n\u003C\u002Ful>\n\n\n\u003Ch2 class=\"wp-block-heading\" id=\"h-technologies-used\">Technologies Used\u003C\u002Fh2>\n\n\n\u003Cp>The project implementation brought a comprehensive technology ecosystem designed for scalable data processing, machine learning operations, and collaborative development. Each technology was strategically selected to create an integrated platform capable of handling complex data workflows and advanced analytics requirements.\u003C\u002Fp>\n\n\n\n\u003Cul class=\"wp-block-list\">\n\u003Cli>\u003Cstrong>Databricks: \u003C\u002Fstrong>Its integrated environment enabled seamless collaboration between data scientists and engineers while supporting large-scale data processing workflows.\u003C\u002Fli>\n\n\n\n\u003Cli>\u003Cstrong>Python:\u003C\u002Fstrong> Its versatility enabled rapid prototyping, statistical analysis, and integration with various data processing tools throughout the technology stack.\u003C\u002Fli>\n\n\n\n\u003Cli>\u003Cstrong>Apache Spark:\u003C\u002Fstrong> Spark’s in-memory processing architecture significantly accelerated data transformation, feature engineering, and machine learning model training operations, ensuring optimal performance for compute-intensive workloads.\u003C\u002Fli>\n\n\n\n\u003Cli>\u003Cstrong>Azure Machine Learning\u003C\u002Fstrong>: The platform’s integrated tools supported the entire machine learning lifecycle from experimentation to production deployment, ensuring scalable and maintainable AI solutions.\u003C\u002Fli>\n\n\n\n\u003Cli>\u003Cstrong>MLflow:\u003C\u002Fstrong> This open-source platform ensured consistent model governance, simplified collaboration between team members, and streamlined the transition from development to production environments.\u003C\u002Fli>\n\n\n\n\u003Cli>\u003Cstrong>Atlassian\u003C\u002Fstrong>: The integrated toolset facilitated efficient workflow management, code review processes, and documentation throughout the project lifecycle.\u003C\u002Fli>\n\u003C\u002Ful>\n\n\n\n\u003Cfigure class=\"wp-block-image size-full\">\u003Ca href=\"https:\u002F\u002Fhubs.la\u002FQ03GtWHY0\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"598\" src=\"https:\u002F\u002Fadmin.cbtw.tech\u002Fwp-content\u002Fuploads\u002FAI-Stragy-Solution-01.jpg\" alt=\"Build an AI Strategy that delivers measurable business value – from vision to deployment.\" class=\"wp-image-10242\" srcset=\"https:\u002F\u002Fadmin.cbtw.tech\u002Fwp-content\u002Fuploads\u002FAI-Stragy-Solution-01.jpg 1024w, https:\u002F\u002Fadmin.cbtw.tech\u002Fwp-content\u002Fuploads\u002FAI-Stragy-Solution-01-800x467.jpg 800w, https:\u002F\u002Fadmin.cbtw.tech\u002Fwp-content\u002Fuploads\u002FAI-Stragy-Solution-01-100x58.jpg 100w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \u002F>\u003C\u002Fa>\u003C\u002Ffigure>\n",false,{"rendered":508,"protected":506},"\u003Cp>A German manufacturer had over 10 data science labs working on similar use cases with completely different approaches, creating fragmented AI operations.\u003Cbr \u002F>\nWe unified their scattered experiments through AI maturity assessment and centralized platform implementation with standardized governance frameworks.\u003Cbr \u002F>\nResults: Clear AI strategy, centralized Data Science platform, and audit-proof machine learning operationalization across the organization.\u003C\u002Fp>\n",43,10330,"open","standard",{"_acf_changed":506,"content-type":8,"inline_featured_image":506,"footnotes":8},[43],[],[517],137,[519],129,[521,500,522,523,524,525,526,527,528,529],"post-10206","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-case-studies","cbtw_industries-manufacturing","cbtw_services-ai-data-platforms",{"authors":8,"article_authors":506,"direct_external_link":8,"post_region":8,"layout_builder":531},[532],{"acf_fc_layout":533,"title":190,"ui_v2":534,"short_description":535,"cbtw_related_group_filters":536,"items":506,"link":543,"hubspot_form_id":8,"mainPostId":495,"region":547},"block_insights",true,"Access related expert 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