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5 steps for implementing change management in your organization

IBM Big Data Hub

Change is inevitable in an organization; especially in the age of digital transformation and emerging technologies, businesses and employees need to adapt. Change management (CM) is a methodology that ensures both leaders and employees are equipped and supported when implementing changes to an organization.

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Streamlining compliance with IBM Cloud Infrastructure as Code and a shift-left approach

IBM Big Data Hub

This blog explores how IBM Cloud’s IaC, when combined with shift-left compliance practices, can help organizations enhance their cloud infrastructure and maintain a strong focus on compliance. IaC allows teams to manage infrastructure in a manner similar to software, using tools like Git, Jenkins and Ansible.

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Mastering identity security: A primer on FICAM best practices

IBM Big Data Hub

The executive order highlights the importance of securing digital assets and mitigating cyberthreats by emphasizing the modernization of identity and access management (IAM) systems. FICAM (Federal Identity, Credential, and Access Management) is an extension of ICAM protocols, methodologies, and systems for federal entities.

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How to implement enterprise resource planning (ERP)

IBM Big Data Hub

Once your business has decided to switch to an enterprise resource planning (ERP) software system, the next step is to implement ERP. For a business to see the benefits of an ERP adoption it must first be deployed properly and efficiently by a team that typically includes a project manager and department managers as well.

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IBM and Microsoft partnership accelerates sustainable cloud modernization

IBM Big Data Hub

IBM’s recommendations included API-specific improvements, bot UX optimization, workflow optimization, DevOps microservices and design consideration, and best practices for Azure manage services. Learn more about the benefits of cloud-native development.

Cloud 81
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Generative AI use cases for the enterprise

IBM Big Data Hub

The process demands high-level skills and resources, but the results are more likely to be compliant, custom-tailored and business-specific. The best option for an enterprise organization depends on its specific needs, resources and technical capabilities. Existing content can be reimagined and edited using AI tools.

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Data Governance Maturity and Tracking Progress

erwin

Data governance is best defined as the strategic, ongoing and collaborative processes involved in managing data’s access, availability, usability, quality and security in line with established internal policies and relevant data regulations. However, the practice of data governance is a relatively new discipline that is still evolving.