This tool guides data practitioners through different stages to ensure that the work they conduct is as actionable as possible. It offers guidance, brainstorming exercises, and supplementary resources.

The post Mastering the Practice of Defining Problems Well: Open Data Policy Lab Releases New Problem Definition Tool appeared first on data.org.

As people have used or heard about the power of OpenAI’s GPT-4 foundation model, they have often been surprised or even astounded. Many have been enthused or excited, but others have been concerned or even frightened. What has become clear to almost everyone is something we noted four years ago—we are the first generation in the history of humanity to create machines that can make decisions that previously could only be made by people. This resource provides best practices for implementing responsible AI systems.

The post Microsoft Responsible AI Principles appeared first on data.org.

Data can be a powerful tool, particularly in the social sector, with unique opportunities and challenges alike. By being more strategic in how we engage with data, we have an opportunity to advance our missions, increase impact, and promote equitable outcomes. This course was designed with and for social sector organizations seeking to build capacity in these areas.

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This Data Learning Journey Playbook is a hands-on resource with independent and team activities that seek to build and sustain data culture and skills for global development organizations. This playbook is designed with people at the center— developing data skills for everyone and leveraging data as a tool for learning collaboratively.

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Data science and user experience (UX) procedures have more in common than most of us think. Both functions help improve decision-making and products by understanding users and their needs. Depending on the problem, the integration of data science and UX can support identifying a small but focused user group, surveying a larger group, mining a huge amount of data, or building products that automatically make decisions and continuously improve based on user behavior.

The post How UX and Data Science Teams Can Work Together appeared first on data.org.

Harnessing the power of analytics — using data to inform and proactively guide institutional actions and decisions — is no longer an optional “extra.” A strong analytics capability has become a necessity that allows colleges and universities to provide the services that students expect and to predict and respond to change.

The post A Modern Framework for Institutional Analytics appeared first on data.org.

The post How to secure C-suite support for your data strategy appeared first on data.org.

Despite heavy, long-term investments in data management, data problems at many organizations continue to grow. One reason is that data has traditionally been perceived as just one aspect of a technology project; it has not been treated as a corporate asset. Consequently, the belief was that traditional application and database planning efforts were sufficient to address ongoing data issues.

Developing a data strategy ensures all data resources are positioned in such a way that they can be used, shared, and moved easily and efficiently. This resource outlines the five essential components of a data strategy.

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Chief data officers (CDOs) know effective business decisions require data but find it difficult to effectively link data to specific business benefits and results. However, Gartner’s 2021 CDO Survey found that 27% of respondents are measured by revenue generation or contribution and only 17% meet those objectives.

To influence stakeholders, data professionals must sell their vision and strategy to the business community. This requires identifying and communicating the desired business outcomes (why) and the personal impacts on the stakeholders you’re targeting (for whom). This resource provides some tactics that can help make the case.

The post Use This 6-Step Approach to Get Buy-In for Data and Analytics Strategies appeared first on data.org.

This playbook shares processes and best practices required to complete high-quality and ethical data science and AI projects for social impact organizations.

The post A Six-Step Playbook for Doing Data Science and AI for Good appeared first on data.org.

Data-driven organizations have a comprehensive data strategy that links key initiatives and data, ensuring they inform goals, objectives, and mission. Building and maintaining a data strategy requires both strategic and financial commitment from the entire organization. Therefore, before anyone builds their data strategy, they will need to secure support from leadership and their IT department, who will advocate and help orchestrate critical changes to scale the framework across the enterprise. 

The post Data Strategy Framework: How To Implement One & Scale It For Success appeared first on data.org.

How would one define ‘data science’? How about big data, AI, or data culture? These are just a few of the many jargons that are commonly used around in “data speak.” As data becomes more widely used, so has a dense vocabulary of jargon. It’s a real challenge to understand and agree on the definitions of these data terminologies.

This article demistifies the 26 most frequently used terms in the data space.

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