Monthly Archives: January 2012

Dinosaurs & libraries

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.

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Do you need a data scientist?

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.

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Building the Future of Libraries Now

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!

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WWW & innate fears

Here is one of the most interesting pieces on knowledge and computers I’ve recently read. It is written by Jascha Kessler, Professor Emeritus of Modern English & American Literature, UCLA, and copied here in its entity from: Humanist Discussion Group, Vol. 25, No. 578.  Enjoy!

I wonder whether the World Wide Web, the knowledge stored and accessed using the Internet, is the fulfillment of some basic instinct comparable to those that led to the creation of language and the recording of knowledge in text itself. Once words and then language had been invented, the idea of written language must have been a major breakthrough, allowing ideas to be preserved and read back as if from the voices of their originators. Then the invention of forms of written text evolved into the numerous varieties we see today.  The Web appears to be the latest evolutionary step in that progression, with a mechanism for recording content in ‘web pages’ collected into ‘web sites’; and with an overall indexing strategy yet evolving–somewhat comparable to the creation of the mechanisms of titles, tables of contents, indexes, etc. created to handle access to  the growth of written text.

What presumably is most frightening about the World Wide Web is that it shows signs of automation that printed text did not. The Web is harvested by ‘web crawlers’ that automatically collect and index web sites for other people to then retrieve. A development that seems almost like there is a robotic army that sneaks about reading everything and keeping a record of it while we’re sleeping. The very idea that text and the content that it contains now flows without our intervention, without the elaborate methods of print distribution that were created to handle the movement of paper-based text, is startling if not outright frightening.

It also is intimidating to the professionals who made their living operating the paper-based system. Everyone from the manufacturers of the implements for recording language through those whose professions involved assembling and proofing it, manufacturing its print products en masse, marketing those products, distributing them, receiving them and storing them in places such as bookstores and libraries, recycling  those products through used bookstores. All these professions are threatened by the web.  Even those who made their living reading and reviewing print text are threatened, since distribution of web pages and web sites doesn’t require human intervention–everyone can read the web without reviewers. That does seem odd, i.e., that we haven’t taken up the challenge to ‘review’ web sites as actively as we reviewed print publications.

Perhaps it is because the text itself is no longer fixed into units with permanence that can be bounded. Whereas publications involved ‘editions’, the web doesn’t have ‘editions’ of web pages or web sites.  They can and do change their content continuously. This is threatening as well. For if we cannot depend upon the text to be permanent, how can we add value to its statements, produce the important summation and analyses of its significance. It’s like trying to review the beauty of cloud-filled skies or sunsets. They are not only each seen from a different perspective, but are ever changing such that the subsequent potential viewers cannot precisely see the same thing the reviewer described. That’s potentially dangerous as well. The web has responded with an effort at ‘permanent’ links; but how much permanence can there be when the method of preserving permanent references is itself dependent upon the same technology that creates total impermanence.

So, apart from the threat from computers and software, the agents of these changes, whose capacity to reorganize and rewrite the knowledge itself we’re still discovering; we’re now threatened by the changes to all of stored world knowledge that could come about. How will we authenticate it in the future such that we know it’s the same text our  ancestors read? You say this eBook is the text of a 20th century work that is no longer available in print and all of whose copies are now stored in the ‘cloud’.   Oh, there can be new glorious new work done by scholars everywhere at anytime. Never have so many been able to access so much with so little effort. But it just seems to depend on such a lot of intangible and ephemeral resources. We seem to be discarding the proven survivability of mass produced and widely dispersed recorded knowledge for the admitted impermanence of electronic and optical recordings requiring special hardware to access. Take away the electricity and the knowledge becomes inaccessible. Surge the electricity and the stored knowledge itself and the hardware to access it could be destroyed.

So, there you have it… The fear is creeping in when we see clearly the need to change every job that has to do with creating, distributing, retrieving and reviewing knowledge and the threat of the complete loss of world knowledge due to technical glitches or its selective revision at the hands of powerful groups.

What I find somewhat amusing here is that the perceived threat from ‘computers’ themselves doesn’t seem as potentially dangerous, apart from them being the agents of change. I.e., hardware and software may well be our best allies in the effort to combat the threats. It reminds me of something I heard about the insects of the world as a  threat to humans. If it wasn’t for insects acting as predators on other insects, we’d never survive. So, computers are just the agents of change—whether change is safe of not is up to how we use computers to combat the changes brought about by computers.

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