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Scale data strategies globally with IBM Cloud Pak for Data and CockroachDB

Scale data strategies globally with IBM Cloud Pak for Data and CockroachDB

January 16, 2020 | by Pearl Chen, Content Marketer, IBM Cloud Pak for Data, IBM
Cloud Pak for Data, IBM’s leading data and AI platform, partners with Cockroach to solve multicloud and compliance challenges so organizations can scale their data strategies across the globe.
Transforming data management procurement into a fun practice

Transforming data management procurement into a fun practice

January 15, 2020 | by Kevin Oliver, Portfolio Marketing Manager, Hybrid Data Management, IBM
Let’s be honest: no one wakes up in the morning excited to go through a procurement process. This reaction can be particularly true where data management is concerned. When quick responses to market changes are necessary, it’s essential to be able to adjust your architecture rapidly without...
AutoAI: Synchronize ModelOps and DevOps to drive digital transformation

AutoAI: Synchronize ModelOps and DevOps to drive digital transformation

January 10, 2020 | by Julianna Delua, Portfolio Lead, Watson Studio Data and AI, Cloud, IBM
AutoAI, a feature of IBM Watson Studio, helps application developers and data scientists work in concert to increase yields for model and app investments, and orchestrate ModelOps with DevOps.
Components of a Modern Data Platform Ready for the AI Future

Components of a modern data platform ready for the AI future

January 8, 2020 | by Kevin Oliver, Portfolio Marketing Manager, Hybrid Data Management, IBM
How to build a modern data management platform ready for the AI future Every data architect knows the value of keeping their data management platform up-to-date and ready for the next phase. Yet how to put this into practice is not always clear. With many businesses embarking on their journey to AI...
Postal services could avoid this seasonal complaint with data and AI

Postal services could avoid this seasonal complaint with data and AI

The IBM Data Science and AI Elite team tackles missed deliveries in the Nordics

December 20, 2019 | by Umit Mert Cakmak, Senior Data Scientist, IBM Cloud and Cognitive Software
The IBM Data Science and AI Elite team showed that PostNord can predict non-deliveries of traceable items depending on address, weather condition, sizes and time of delivery. By leveraging AI, it’s possible to reduce non-deliveries by 50 percent annually, beneficial for both customers and PostNord...
Reality and misconceptions about big data analytics, data lakes and the future of AI

Reality and misconceptions about big data analytics, data lakes and the future of AI

December 19, 2019 | by Holly Vatter, Product Marketing Manager for Data Lake & Cloudera Partnership, IBM
With the amount of choices surrounding big data analytics, data lakes and AI, it can sometimes be difficult to tell fact from fiction. With more than 40% of organizations expecting AI to be a “game changer,” it’s important to have a complete picture of the capabilities and opportunities available.
Implementing DataOps across a banking enterprise

Implementing DataOps across a banking enterprise

December 18, 2019 | by Elaine Hanley, Program Director, Unified Governance & Integration Platform Offerings, IBM
Imagine a day in the life of Sarah, a hypothetical Chief Data Officer at a major bank in South Africa. There are many expectations on her shoulders. She struggles to deliver business-ready data to fuel her organization and support the decision makers within the bank. It is her job to put in place a...
6 steps to start your DataOps practice

6 steps to start your DataOps practice

December 18, 2019 | by Julie Lockner, Portfolio Optimization and Offering Management Director, IBM
DataOps is the orchestration of people, process, and technology to accelerate the quick delivery of high-quality data to data citizens. When done right, DataOps creates business value because users know what data they have, can trust the quality and its meaning, and use it without violating...
Components of the DataOps toolchain and best practices to make it successful

Components of the DataOps toolchain and best practices to make it successful

December 18, 2019 | by Ritesh Gupta, Chief Architect, Data Integration and DataOps Innovations, IBM
High-quality data is the core requirement for any successful, business-critical analytics project. It is the key to unlock and generate business value and deliver insights in a timely fashion. However,  stakeholders across the board are responsible for data delivery, quickly evolving requirements,...
The difference between DataOps and DevOps and other emerging technology practices.

The difference between DataOps and DevOps and other emerging technology practices.

December 18, 2019 | by Karen Madera, Product Marketing Manager, Unified Governance and Integration, IBM
The expectation to achieve faster results continues to rise. Businesses everywhere are looking for ways to improve their operational efficiency and effectiveness to enable the best decision-making. The need to optimize typically comes to a head with the reality that there are many silos within any...

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