Data Science & Analytics: What Is in It for iSchools?
Deinstitutionalizing the iSchoolOur suggestion was essentially that for iSchools to flourish in the context of big data, they must focus on extending collaborative partnerships across technical, social and cognitive science and philosophical domains. We cannot be all things to all people, but the iSchool can offer tremendous value in terms of the ability to integrate and critically reflect on broader perspectives surrounding the intersection between society, policy and information.
Over the short term, the iSchool should move past the perceived constraints of its role in library science to begin to build bridges to other domains that offer specialized knowledge. In the mid-term, the iSchool should actively develop perspectives that move beyond technical skills to address the integration and interpretation of big data into government and corporate arenas. Over the long term, the iSchool should seek to incorporate, and continually contribute to the development of, transcendent values that make the collection, storage and interpretation of big data both socially and ethically responsible.
Clarify and DefineOur workshop group's suggestion was to clarify and perhaps define the roles on a data science team, and to identify the role that iSchool graduates can uniquely fill, perhaps drawing on some of the historic strengths of the iSchools (e.g., adding data curation to the set of skills).
What the iSchools can bringWe focused on a few core areas: what iSchools can bring, who cares about it, leadership and actionable steps
For any effort to be successful, it needs to be relevant to the appropriate audiences. In this case, a graduate with Data Science skills are currently prized and will be desired in the future.
This is an ideal opportunity for the iSchools to band together and take a leadership role in defining Data Science for the greater academe and industry. Determining an initial set of standards will help institutions and affiliates
iSchools are perfectly poised to identify and clarify the first wave of Data Science 'standards'. Our students are taught hard skills such as database development, information retrieval, programming, and systems administration. They are also taught the soft or theoretical skills such as understanding data/information quality, social and technical aspects of information, value of information, where/how it is processed and serve as an intermediary between domain and technology.