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Eating the Elephant of Data Strategy

Paul Blogs on BI

With both the rise in organizations looking to move their data to the cloud and the increasing awareness of the power of BI and generative AI, data strategy has become a top priority.

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How can CIOs Build Business Value with Business Analytics?

Smart Data Collective

Team Upskilling: Train business analysts on planning, gap analysis, scoping & blueprinting, cost-benefit calculation of new initiatives, solution architecture, modelling, elicitation, requirement management, performance management, and other improvement initiatives.

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How to unlock a scientific approach to change management with powerful data insights

IBM Big Data Hub

Process mining tools can perform a fit-gap analysis on new processes to rapidly and more accurately identify the greatest change impacts. Likewise, change agents spend a lot of time and effort on ‘To Be’ design and identifying the impact on those affected by the potential ‘new normal’.

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SEC climate-related disclosure rules for public companies

IBM Big Data Hub

IBM Consulting Sustainability Services can assist organizations in addressing the SEC’s climate disclosure regulations through a comprehensive approach that includes data curation, gap analysis, strategy development and reporting services.

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How healthcare organizations can analyze and create insights using price transparency data

AWS Big Data

As hospitals generate this data, they can use their organization data or ingest data from other hospitals to derive analytics and competitive comparison.

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Upskilling and reskilling for talent transformation in the era of AI

IBM Big Data Hub

Skill-gap analysis Organizations can input a ton of information about their employees’ performance and certifications and use machine learning to identify areas where they need more training. On-the-job training Employees can improve their knowledge and expertise in AI tools by using AI applications while doing their jobs.

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The art and science of data product portfolio management

AWS Big Data

As part of this practice, a gap analysis is conducted between the current and target data product portfolio, and a set of required actions and estimated time and effort prepared for review by the organization.