I came across the new Standards for Libraries in Higher Education (SLHE) drastically revised by the Association of College and Research Libraries’ Board of Directors this past October. The new standards aim to provide a roadmap that will assist academic libraries in responding effectively to the growing pressure to demonstrate their value through evidence based means.
“These standards differ from previous version[last revised in 2004] by articulating expectations for library contributions to institutional effectiveness […] They also differ structurally from the previous version by providing a comprehensive framework using an outcomes-based approach, with evidence collected in ways most appropriate for each institution.” – said University of Nevada-Las Vegas Dean of University Libraries Patricia Iannuzzi, who chaired the SLHE task force. They are based on the following nine principles:
Institutional Effectiveness: Libraries define, develop, and measure outcomes that contribute to institutional effectiveness and apply findings for purposes of continuous improvement.
Professional Values: Libraries advance professional values of intellectual freedom, intellectual property rights and values, user privacy and confidentiality, collaboration, and user-centered service.
Educational Role: Libraries partner in the educational mission of the institution to develop and support information-literate learners who can discover, access, and use information effectively for academic success, research, and lifelong learning.
Discovery: Libraries enable users to discover information in all formats through effective use of technology and organization of knowledge.
Collections: Libraries provide access to collections sufficient in quality, depth, diversity, format, and currency to support the research and teaching missions of the institution.
Space: Libraries are the intellectual commons where users interact with ideas in both physical and virtual environments to expand learning and facilitate the creation of new knowledge.
Management/Administration: Libraries engage in continuous planning and assessment to inform resource allocation and to meet their mission effectively and efficiently.
Personnel: Libraries provide sufficient number and quality of personnel to ensure excellence and to function successfully in an environment of continuous change.
External Relations: Libraries engage the campus and broader community through multiple strategies in order to advocate, educate, and promote their value. (See the document to read more).
We cannot have good libraries until we first have good librarians — properly educated, professionally recognized, and fairly rewarded.”
Herbert S. White (Library Journal column, 15 November 1999, pp. 44-45)
Sometimes I think that the biggest problem libraries face today is not the technology but the dinosaurs in our mist. “Dinosaur” here does not connote the age, but attitude.
We are a profession plagued with a very high degree of resistance to change and libraries as institutions are often places where drive for tackling new ideas, projects and services would be met with attitudes like: “well, that’s not how we do things here…” or “we don’t have money for that….” or “we don’t have time/staff for that…” We hear and say these excuses all the time and we well know that some ideas are not even that “big” nor they require money or staff time. We know that they were suggestions perhaps worth pursuing because they could put us in the direction some other institutions similar to libraries are going, but we do not want to risk making any changes to our routines and procedures. So originality of though is frowned upon in libraries and innovative thinkers get discouraged coming up against the mentality “we’ve tried this ten years ago and it didn’t work, why should we try again” or even worse “but I’ve always done it like this”.
But the world in which libraries operate has changed and we need to find ways how to survive these changes. Library administrators, who really care about libraries and the library profession, need to welcome and support innovative thinking professionals, let them experiment and contribute their ideas that will help to reinvent the profession.
Dinosaurs disappeared not because the climate changed. They disappeared because they did not change. They did not adapt.
From: VentureBeat — Interpreting Innovation
Some of the world’s biggest tech companies from Google to Facebook are data-driven, but few startup founders have any idea what a data scientist does, never mind whether they should hire one. Here is VentureBeat’s guide to data science for startups.
What does a data scientist do?
DJ Patil led LinkedIn’s data science team and is now the Data Scientist in residence at Greylock Partners. His free ebook “Building Data Science Teams” provides an excellent introduction to the basic areas of data science and how to build a team.
For startups, the most relevant applications of data science are probably decision science and product and marketing analytics. Decision science, as the name implies, allows you to identify and monitor key metrics for your business and answer strategic questions like “Which country should we expand into next?” or “What is the impact on the business if we lose this client?”. Google’s data science team even drives its HR policies.
Product analytics covers anything from how users are reacting to new features to developing standalone data products. LinkedIn’s “People you may know” feature and Amazon’s recommendation system are data-driven features that attempt to keep users on the site longer or drive more sales.
Using data to showcase or market a product is the domain of marketing analytics. One of the best known examples is okCupid’s okTrends blog, which features posts like “The case for an older woman” or “The 4 big myths of profile photos”. The blog drives massive traffic to the site and is regularly covered in the media.
Who are the data scientists?
Since data science is a new area, practitioners often migrate from other fields. You may see maths, statistics, machine learning or computer science on their resumes or a data-intensive field like meteorology. Data scientists want to be of central importance to a business, especially when it’s a startup. The best data scientists are both intensely curious and great communicators. They answer important questions and tell good stories using data.
What is data infrastructure?
Data scientists need specialized tools to manage and process large amounts of data. The minimum you need to get started is simple data access, usually via a database. Larger-scale or less uniform data may require a tool like Hadoop, an open source platform for distributed processing of large data sets across clusters of computers, as well as someone with the technical expertise to use it. Data stores like Cassandra are designed to perform well on very large datasets. These are some of the most commonly used tools, but there are many others for tasks such as streaming data collection, querying non-relational databases and job scheduling.
When do you need to hire a data scientist?
VentureBeat talked to data scientist Cathy O’Neil, who herself works for a startup (Intent Media), about when you need to hire a data scientist. If your data volume is growing, you don’t know if you are seeing noise or information in your data, or in general, if you are not running your business sufficiently quantitatively, then you may need to consider hiring.
Read the interview with Cathy O’Neil.
I came across this sweet and uplifting video that actually gives some good and sound advice what libraries need to do to ensure their relevance in the future:
Enjoy and have faith that we can do it!