How can digital libraries align data collection with organizational values?

I think our databases for searching should be objective and not aligned to the purpose for searching. How I use the data is aligned to my goals and purposes -- not the other way around.

In my experience, understanding data is akin to learning a new language—it ultimately reflects our thought processes. Data becomes most powerful when it allows for real-time, predictive analytics. For instance, in the realm of digital marketing, one can track customer interactions with content live, providing immediate insights into engagement levels. Such capabilities enable us to assess whether content resonates with the audience effectively. And take action in real time rather than wait until the end. We've adopted a similar approach on a Platform called EasySim used in realm of digital learning.

Libraries have a critical issue in the midst of the digital era: using data while adhering to their fundamental principles of intellectual freedom, privacy, and openness. In order to enable responsible data collecting that supports knowledge discovery without jeopardizing user confidence, this endeavor necessitates a careful dance. As a specialist in information ethics, it is my responsibility to lead you through this dance and provide a route that will enable data to be used for the benefit of both user requirements and library ideals.

To align data collection with organizational values in digital libraries, it's important to establish clear guidelines and principles for data collection and usage. This involves defining the purpose of data collection, ensuring data privacy and security, and obtaining informed consent from users. By being transparent about the data being collected and how it will be used, organizations can build trust with users and demonstrate their commitment to privacy and ethical practices. Additionally, organizations can implement data governance frameworks and regularly review their data collection practices to ensure they align with their values and comply with relevant regulations.

To ensure alignment, establishing data governance that adds tangible value is crucial. This involves crafting a framework that not only regulates data but also contributes positively to organizational objectives. A well-designed data governance strategy can optimize processes, enhance decision-making, and foster a cohesive approach towards achieving overarching goals.

A critical issue that sits right alongside methodology is ensuring that you know the sources and can track back to the evidence. Primary research kind of rules the roost. And using AI -- unless you can designate where the data mix comes from -- is most dangerous. I had ChatGPT write an article on whether three cueing in early reading should be banned and then put it aside and wrote the article the old-fashioned way -- finding peer-reviewed articles from the last 25 years, looking at assessment statistics from NAEP, and then drawing my own conclusions. The ChatGPT article was more fashionable but way less valid. And that isn't hubris speaking -- the research and evidence base makes all the difference.

This approach goes beyond just ticking regulatory boxes. It's about upholding core values like user privacy and intellectual freedom. By collecting data responsibly and ethically, libraries can unlock valuable insights to improve user experiences, personalize learning, and strengthen their services, all while staying true to their mission.

One thing that need to be considered in collecting data is privacy, security and ethical implications of the process. Ensure that data collection practices respect the privacy of the user, confidentiality of data, and autonomy. Avoid collecting unnecessary or intrusive data that may compromise user trust.

No matter which method you choose, the quality of the data relies on having good questions. For example, having poorly designed survey questions could impact the number of and quality of the responses. Also, choose a method which is likely to get the most engagement to increase the chances of getting insightful data.

Elegir los métodos correctos asegura datos relevantes, precisos y de calidad, lo que mejora la comprensión de los usuarios y la eficacia de las decisiones. Además, refuerza la integridad y la reputación de la organización al alinear la recopilación de datos con sus valores.

This approach goes beyond just ticking regulatory boxes. It's about upholding core values like user privacy and intellectual freedom. By collecting data responsibly and ethically, libraries can unlock valuable insights to improve user experiences, personalize learning, and strengthen their services, all while staying true to their mission.

You need to follow key data protection principles: Legal Basis: Ensure you have a legal basis (e.g., consent) before collecting personal data. Transparency: Provide clear and easily understandable information to individuals about how their data is being used, including who is processing it, for what purposes, and their data protection rights. Purpose Limitation: Collect data only for specified, explicit, and legitimate purposes (see above). Data Minimization: Gather only the minimum amount of data necessary for the purpose. Accuracy: Ensure that data is accurate and up-to-date. Storage Limitation: Store data only for as long as necessary for the purpose. Data Security: Implement measures to safeguard data.

Considerar la protección de datos es crucial para garantizar la privacidad y seguridad de los usuarios. Esto fortalece la confianza del usuario y cumple con regulaciones legales, evitando posibles sanciones y da?os a la reputación de la organización. Además, proteger los datos ayuda a prevenir el uso indebido o la divulgación no autorizada de información sensible, lo que es fundamental para mantener la integridad y la ética en la recopilación y el tratamiento de datos.

I'm not an expert, but my opinion is to protect the data in digital libraries, we could employ encryption, access controls, regular backups, firewalls, and good and robust antivirus software. Also, we should provide employee training, classify data, and establish an incident response plan to mitigate risks and prevent unauthorized access or data breaches. That could be part of a training and development programme with coaching as an plus, in order to reflecting this practice's.

Pour moi, la protection des données est cruciale. Cela signifie s'assurer que les informations collectées sont sécurisées, confidentielles et correctes. Il est important de suivre les meilleures pratiques en matière de sécurité, comme le cryptage et l'anonymisation des données, ainsi que de contr?ler qui y a accès. De plus, il faut respecter les lois et règlements en vigueur pour garantir la conformité.

especially the focus on stakeholder engagement, privacy-enhancing technologies, and clear communication. It's important to involve librarians, users, and experts in these conversations to ensure diverse perspectives are heard.

With the advent of AI tools, analyzing and drawing data meaningfully will become a breeze. Co-pilot for example can already analyze data, represent it and provide insights in context, predicting outcomes.

Analyzing data helps uncover insights, patterns, and trends that can lead to better decision-making, improved efficiency, and a deeper understanding of your data strategy and projects.

Para analizar todos los datos, centralízalos y organízalos. Utiliza herramientas como Excel, Python, Power BI o Tableau para explorar, describir y visualizarlos. Aplica técnicas estadísticas para identificar patrones. Presenta los resultados de manera narrativa y visualmente atractiva para facilitar la comprensión. Finalmente, interpreta los resultados para tomar decisiones informadas y acciones pertinentes.

Analyzing data while protecting it in digital libraries: Some ideas: Utilize secure analytics tools that allow for data analysis while maintaining data privacy and confidentiality. Anonymize sensitive data before analysis to protect individual privacy and comply with data protection regulations. Implement role-based access controls to ensure that only authorized personnel can access and analyze sensitive data. Mask or obfuscate sensitive data during analysis to prevent unauthorized exposure. That could be some ideas for analyze data.

Data can be a powerful tool for libraries, but only if it's wielded responsibly. Let's work together to create a future where libraries can harness data's potential while safeguarding user trust and ethical principles.

Es muy importante el acceso abierto y compartido del conocimiento: Al promover el acceso abierto y compartir recursos y conocimientos, las bibliotecas digitales pueden demostrar su compromiso con la educación y la equidad. Esto debe equilibrarse con el respeto a la privacidad y los derechos de autor.

Al compartir datos, prioriza la privacidad y claridad. Proporciona contexto, asegura la accesibilidad y transparencia en la metodología. Fomenta el feedback para enriquecer la comprensión y la toma de decisiones.

Limit access to a secure authentication methods and role-based permissions.Encrypt data before sharing to prevent unauthorized access during transmission. Using encrypted communication channels and secure file-sharing platforms to transmit data safely. Establishing data sharing agreements with clear terms and conditions to protect the confidentiality and integrity of shared data. Sharing only the necessary data required for the intended purpose to minimize the risk of exposure. Implementing a monitoring mechanisms to track data access and usage by recipients, ensuring compliance with sharing agreements. Some ideas that we can securely share data while safeguarding confidentiality, integrity, and compliance with data protection regulations.

Quand on partage nos données, on les met à disposition d'autres personnes comme des chercheurs ou des décideurs. ?a peut être super pour favoriser la collaboration et l'innovation. Mais il y a aussi des défis, comme qui détient les données et comment elles sont utilisées. On doit penser aux avantages et aux inconvénients, et être conscient des questions éthiques et légales. En respectant les bonnes pratiques, comme fournir des informations sur nos données et les mettre dans des formats compréhensibles, on peut rendre le partage de données plus efficace et équitable.

Como usuario de bibliotecas digitales, la alineación de la recopilación de datos con valores organizacionales es crucial para una experiencia positiva y ética. La transparencia en la recopilación, almacenamiento y uso de datos es esencial, reflejando valores de privacidad y seguridad. Las bibliotecas deben personalizar la recopilación según las preferencias de los usuarios, respetando la diversidad y garantizando accesibilidad. Utilizar datos para mejorar servicios, como recomendaciones personalizadas, está alineado con la mejora continua. En resumen, la alineación implica transparencia, personalización, equidad y mejora continua para una experiencia en línea ética y fundamentada en principios bibliotecarios.

I’d say another category worth highlighting in this article, after evaluating data, should be “Presenting data”. We often present data to convince others of a predetermined idea, assumption or belief. This is most pertinent when we try to convince stakeholders to buy into or find a project. Savvy stakeholders will question data that is presented in a one-sided manner. It is best (and values aligned) to present data objectively and point out shortcomings, anomalies and data conflicts. Stakeholders will feel more empowered to make key decisions when they feel they have been presented with objective data, as opposed to being “sold to” based on selective data and conclusions. A useful read on this topic is Adam Grant’s book “Rethink”.

It's always a case of following the principles of internal controls. A spick and span control environment will allow for businesses to make linkages easily.

Las bibliotecas deben utilizar tecnologías de manera que respeten los derechos y la privacidad de los individuos. Esto incluye ser cauteloso con el uso de tecnologías de vigilancia y reconocimiento facial y asegurarse de que las herramientas de análisis de datos no perpetúen sesgos o discriminación.

This ? asks about alignment to org values, not org goals. Both are necessary for purposeful data collection. Having it align to the org's values helps demonstrate the org's ethos is woven throughout everything it does. It doesn't mean objective, valuable data has been collected. For example, the design of the data collection tools need to continue the objective position the defined purpose set forth. An org needs to ensure alignment is purposeful at each phase of data collection, from setup through to reporting. If it isn't, the data's value could be reduced, making decisions based on this information potentially misaligned to org needs, even if it aligns to org values. Separating the 2 to verify both were factored doesn't hurt though!

?? Transparency is the key: Communicate openly about data collection practices, providing users with clear information on what data is collected, why it's collected, and how it will be used. Transparency fosters trust and ensures alignment with organizational values.