Pacific Data Integrators’ Post

I’ve been in the data business for my entire career, across multiple industries, as a leader, practitioner, and consultant. Throughout that experience I’ve always observed and remarked that all companies have basically the same data challenges. Much of it comes down to basic knowledge management…where is the information? Who owns it? How is it defined? Is it accurate? When I led a BI team at Seagram in the 1990s, I had an opportunity to work in a task force focused on the problem of knowledge management for our organization. We rallied around a great quote by Lou Gerstner (IBM CEO)…to paraphrase…”if we only knew what we know, we could be twice as profitable.” This is still true for most organizations today! This challenge is particularly problematic for MDM projects, which spend much of their time trying to identify trusted data sources, defining terms and rules to enable mastering, and cleansing errant data. Data catalogs and data governance programs are a complementary necessity, but are historically constrained by the availability of the humans that have the knowledge. AI is the great accelerator in this regard. Imagine AI tools that can leverage patterns found not only your own company’s knowledge, data, and information to “train” the discovery, ingestion, cleansing, and mastering processes…but in fact the algorithmic learnings from data commonly used in entire functions, verticals, and industries. The ability of AI to learn and use those learnings across contexts is the paradigm-shift, freeing humans to value-added work that drives results. In this sense, AI is the ultimate knowledge management tool. Check out this great blog that speaks to some of the specific ways that AI can accelerate MDM and data governance success. #Informatica #MDM #AI #datacatalog #datagovernance

🚀 Exciting developments in AI for master data management! 🤖💡 As businesses strive for efficiency and accuracy in managing their master data, leveraging AI holds immense potential. Artificial Intelligence can revolutionize the creation and maintenance of master data, streamlining processes and delivering real-time insights. Here's how AI can accelerate and automate master data management: 1️⃣ Data Extraction: AI-powered algorithms can extract structured and unstructured data from various sources, reducing manual effort and eliminating potential errors. 2️⃣ Data Cleansing: AI algorithms can identify and rectify inconsistencies, duplicates, and inaccuracies in master data, ensuring high-quality information. 3️⃣ Data Integration: AI can seamlessly integrate data from multiple systems and sources, enhancing data completeness and accuracy. 4️⃣ Data Enrichment: AI can enrich master data by automatically gathering additional relevant information, providing a comprehensive view and enabling better decision-making. 5️⃣ Data Governance: AI can play a vital role in enforcing data governance policies, ensuring compliance, and detecting anomalies or data quality issues. 6️⃣ Data Maintenance: AI-powered systems can proactively monitor and update master data, reducing manual intervention and enabling real-time data synchronization. By harnessing the power of AI, organizations can unlock the full potential of their master data, empowering data-driven decision-making, improving operational efficiency, and enhancing customer experiences. Let's embrace the AI revolution in master data management and pave the way for a more agile and intelligent future! 🌐💪 #AI #MasterDataManagement #DataAutomation #DigitalTransformation

We have learned over the past several years that without an Enterprise Data Strategy, there can be no AI Strategy. Data silos or deficiencies in data management eventually hamstrings the organization. In the dynamic landscape of technological evolution, HBSC, as a prominent technology strategy partner, underscores the pivotal role of an Enterprise Data Strategy in shaping the trajectory of Artificial Intelligence (AI) initiatives. Over the past several years, it has become increasingly evident that the symbiotic relationship between data strategy and AI strategy is integral to achieving meaningful outcomes. Without a robust Enterprise Data Strategy, an AI Strategy lacks a sufficient foundation for rapid innovation and effectiveness. Quality data is the lifeblood of AI and serves as the raw material from which insights are derived. An effective Enterprise Data Strategy, meticulously aligned with organizational objectives, ensures the availability of quality data and fosters a culture of data-driven decision-making. As technology advances, we continue to guide businesses in seamlessly integrating Enterprise Data with AI initiatives, empowering them to unlock the full potential of intelligent technologies and navigate the complexities of the digital era. Artificial Intelligence Services - HBSC Strategic Services (hbsconsult.com) #DigitalExecellence #CloudAi #Snowflake #DataStrategy

Equipment maintenance, aka, Aftermarket services are now a fairly huge cost component for a number of industries. Process improvement have achieved very little reduction. Most of these equipment generate data however, most of the equipment manufacturing companies do not have skills in house to derive value and optimise maintenance costs. Advanced capability providers are now available who have expert diagnosticians and have combined the knowledge data, machine learning, deep learning and artificial intelligence to deliver predictive and prescriptive maintenance. MAKE may take years. Instead, you can BUY to accelerate your #industry50 journey. Connect with Excelencia now! #analytics #predictiveanalytics #businessanalytics #digitization #operatingmodels #operationalexcellence #businessandmanagement #artificialintelligence #ai #equipment #data #dataanalytics #machinelearning #deeplearning #dataengineering #datacloud #datacleaning #digitalengineering #datalake #dataarchitecture #datasecurity #dataprotection #dataintegration #gcpcloud #azurecloud #awscloud #oraclecloudinfrastructure #sapcloud #ibmcloud #aerospaceindustry #aviationindustry #defenceindustry #shippingindustry #energyindustry #heavyengineering #automotiveindustry #transportationindustry Balakrishnan Ranganathan Rajesh Ramanathan Venkat Panchavati Pradeep PV SHANKAR ARUN Surej Vasudevan Potty Panpozhilpaavalan(Paavalan) M Joseph Roger, CSM® Vidula Sudarshan . Nizar Mohammed Dhruva Cs Daniel Raja SLN Solutions

I couldn't agree more! Industrial data holds incredible potential, and AI is the key to unlocking it. Transitioning from analysis paralysis to continuous improvement is essential in today's competitive landscape. From a technology standpoint: - AI's ability to bridge the structured/unstructured data gap is a game-changer. We can finally capture the full picture and gain deeper insights from all our data sources. - Implementing AI-powered solutions requires careful planning and integration with existing infrastructure. We need a robust strategy to ensure seamless adoption. From a project management perspective: - Leveraging methodologies like Pareto Analysis and Six Sigma alongside AI is a powerful combination. We can prioritize improvement efforts for maximum impact. - AI-driven root cause analysis can be a tremendous asset. Imagine predicting and preventing issues before they even occur! This will significantly improve efficiency and uptime. Overall, this is an exciting time for industrial organizations! By embracing AI and continuous improvement, we can optimize processes, make data-driven decisions, and gain a significant competitive edge.

Just a thought on Monolithic LLMs vs. Multi-agent AI implementation in data visualization of Supply chain management modules. Supply chain management modules could be managed by independent agents, instead of single AI agents. Each agent can be trained as a specialized employee/manager of a department handling the modules. The benefit of Multi agents:  1. Flexibility and Scalability  2. Self-Organization and Coordination  3. Specialized expertise  4. Fault tolerance I believe, we can take this a step further by implementing sub-agents for each module rather than having a specialized individual agent for each module. These sub-agents can be trained in data modeling and multi-data source GBI, allowing each agent to learn and become an expert in its intended function independently. In this case, for warehouse management, one agent can be trained to source, clean, and process the data and the other agent can be trained on GBI.

Businesses in GCC have an opportunity to engage in data supply-chain initiatives, supported by AI, to become part of a dizzying acceleration of industrywide transformation. Read further, article from our very own ……Vaibhav Vohra Charlotte Stretton Epicor CXO Insight Middle East

Unlock the full potential of your industrial data with #AI and transition from continuous analysis to continuous improvement! With #AI, industrial organizations can #seamlessly bridge the gap between structured and unstructured data, revolutionizing operations and freeing experts from manual analysis to drive success. By leveraging methodologies such as Pareto Analysis, Six Sigma, and AI-boosted Root Cause Analysis, organizations can prioritize efforts, strive for perfection, and predict or even prevent deviations with remarkable precision and efficiency. Discover how to transform your processes for optimized efficiency and stay ahead in a competitive marketplace!

🌐 AI demands new ways of data management 🔢 Data is also exploding in volume and generative AI has elevated the value of this new data. At the same time, the trustworthiness of AI is directly correlated to the quality, availability, and management of the underlying data. With growth comes complexity. IT leaders must create more value from their data while improving resiliency and reducing costs. They must also ensure scalability for modern applications, analytics, and AI use cases. This whitepaper explores how data teams can help ensure that applications run with optimal performance. Here’s what you’ll learn: ▪ How to develop your database management strategy to scale AI ✔ ▪ The three types of databases you can use to support analytics and AI ✔ ▪ What data warehouse architectures are meant to do ✔ Download now: https://trib.al/q5riIYD #sponsored #whitpaper #ITPro #IBM #AI

In today’s data-driven landscape, comprehending your organization’s data and #AIMaturity is paramount for achieving success.   Check out our recent blog on: "The AI Maturity Model: Move the Digital Transformation Needle towards an AI-Driven Business" written by Tanha Shah: https://lnkd.in/dCrs-wFm #CygnetDigital #AIRevolution #businesstransformation #supplychain #finance #operations #humanresource #shopoperations #marketing Sanjeev Thakkar Narasimha .. Premal Dave Bhaswata Sinharoy Vivek Kaushik Ambuj Kathuria Uma Tripathi Digesh Thakore Gopinath Dhandapani Kulmohan Makhija Avani Gupta Nimish Vora Shivangi Dubey Ashutosh Trasy