Disrupting Data Management with AI?

 Value of intelligently positioned disruption.   From Deloitte

To achieve the benefits and scale of AI and MLOps, data must be tuned for native machine consumption, not humans, causing organizations to rethink data management, capture, and organization.

With machine learning (ML) poised to augment and in some cases replace human decision-making, chief data officers, data scientists, and CIOs are recognizing that traditional ways of organizing data for human consumption will not suffice in the coming age of artificial intelligence (AI)–based decision-making. This leaves a growing number of future-focused companies with only one path forward: For their ML strategies to succeed, they will need to fundamentally disrupt the data management value chain from end to end.

In the next 18 to 24 months, we expect to see companies begin addressing this challenge by reengineering the way they capture, store, and process data. As part of this effort, they will deploy an array of tools and approaches including advanced data capture and structuring capabilities, analytics to identify connections among random data, and next-generation cloud-based data stores to support complex modeling.  ... ' 

Supply Chain Best Practices after Crises.

Good overview in APQC regarding management after and during crisis.

Keys to Maintaining Best Practices in Supply Chain After a Crisis

As one-third of 2020 is behind us, what’s next for supply chains? What are the keys to maintaining best practices in supply chains after a crisis? The answers are tied to foundational management practices: sound data management, strong process management, a focus on future-ready skills, and enhanced digitization. In short: an acceleration of trends already underway.

Many organizations are still hip-deep in dealing with the impact of the coronavirus/COVID-19 pandemic, with the impacts and degree of disruption varied by industry and business model. However, strong supply chains are vital to survival during the crisis and thriving after the crisis. While individual organizations may take different routes and while the crisis may exacerbate existing weaknesses in some organizations’ supply chains, the path forward to maintain best practices in the new/next normal/abnormal will have some similarities.

Sound Data Management
The number one enabler to making better data-driven decisions in many organizations is sound data management.

One regional supply chain master data manager APQC interviewed evaluated data quality, governance, and processes in his organization. “It all pointed to a lack of discipline, end-to-end process, and focus on data. Each department’s data responsibility was a small part of someone’s job. Regrettably, how that data impacted others upstream and downstream in the supply chain was not considered, nor was it understood.” His organization then created a supply chain data management program to standardize, align, and improve efficiency and quality in data management. (For more insights, read this APQC case study: Applying Supply Chain Data Management at a Large Healthcare Organization.)

Building a solid data management foundation is critical to enable advanced analytics. In APQC research into supply chain analytics, we found that almost 80 percent of organizations have seen their investment in supply chain analytics increase in the past three years. This is significant because supply chain leaders are turning to advanced analytics to help make business decisions specifically related to supply chain optimization, reducing cost, and improving customer satisfaction—and the insights from the analytics are only as strong as the data upon which they are based.  ... "