CIVICTechnologies, a company that provides location-based web-software solutions to libraries, published the first big data study on library services in March 2016. “Core Customer Intelligence: Public Library Reach, Relevance and Resilience” investigates the habits of core customers across ten library systems in the United States[1]. The goal of the study is to help libraries retain core customers and reach and recruit new audiences.

Collecting Core Customer Intelligence

The report defines “core customers” as a library system’s  top 20 percent of active cardholders who have checked out the most physical items. The ten library systems in this report were selected because they currently use  CIVICTechnology’s CommunityConnect, an application that integrates library data with demographics[2].

Together, these ten library systems serve 7.8 million people. The report looked at four million cardholders who made 6.74 million book and physical media checkouts in 2014 (the privacy of the individual customers was protected). Each library’s customer and checkout data was aligned with census block data, and an outside firm performed the analysis.  The report also defines customer types, a key tactic in market segmentation, such as “Green Acres” (rural upper-middle-class married couple families) and “NeWest Residents” (urban lower-middle-class mixed families)[3].

What the Report Found

As one might expect, core customer characteristics and behaviors are complex and unique from library system to library system. And even within individual library systems, the report found diversity within that top 20 percent of active cardholders. For example, some metro areas, such as Las Vegas, had “fragmented, diverse segments” of customer behavior.

Because of this diversity across systems, the report finds that the “business of public libraries is hyperlocal.” In other words, there is no one-size-fits-all model for core customer characteristics[4].

The report recommends that libraries use core customer intelligence do the following:

• Reach—The report found that libraries have core customers in every major community market segment. Data can help libraries gauge how effective their reach is.
• Relevance—The study found that libraries have relevance across a variety of customer segments. Libraries can benchmark and measure the strength of library connections to the community.
• Resilience—Data gives libraries the tools to stay flexible and adaptable in complex community and business environments.

The next steps from this report might be the creation of a toolkit or guide to exploring big data collection and reporting for public libraries. The report provides some excellent framework for getting started, but staff whose libraries did not participate in the study might wonder how they can use these same tactics. With some direction, other library systems can be empowered to make data-informed decisions as well.

Diving Even Deeper Into Library Data

While this report only covers ten library systems, it opens up a conversation about how libraries can borrow strategies from the sales and marketing world and it apply it to their own communities. Public Libraries Online’s Kristen Whitehair writes that there is great potential for crossover between the field of data science and libraries[5].  As libraries become more customer service-oriented, this sort of research is vital for longevity.

It would be fascinating to continue this research and expand it to digital items, such as e-books or audiobooks, library online database use, or even programming. Library Journal’s Lisa Peet interviewed some of the participating libraries, who shared that they’d like to see a similar study on these various facets of library service[6]. Hopefully this initial study helps pave the way for libraries to continue learning more about the customers they serve.

References

[1] Mark Futterman and Danielle Patrick Milam, “Core Customer Intelligence: Public Library Reach, Relevance, and Resilience,” CIVICTechnologies, March 2016.

[2] Ibid.

[3] Ibid.

[4] Ibid.

[5] Kristin Whitehair, “More than Buzz Words: Big Data and Data Science,” Public Libraries Online, May 9, 2016.

[6] Lisa Peet, “Core Customer Study Analyzes Library Demographics,” Library Journal, March 29, 2016.

The post Using Big Data to Address Local Needs first appeared on Public Libraries Online.

Is this possible through some kind of sophisticated body scan, or something that detects and reads the library card in his pocket? No. A simple computer program simply scans the faces of everyone who comes through the door and matches them with the library’s own database. How far away is this future? Really, it’s right around the corner.

Big data, or the huge piles of information much too vast for standard computers to catalog and analyze, is becoming more and more vital to nearly every industry. Most of this data is created by users of social media, the Internet, smartphones, and almost any app that is location enabled.  The White House has even become involved, with the creation of the Office of Data Science, and appointing the first chief data scientist in US history.[1]

Our faces are a part of big data. According to an article in The Atlantic titled “Who Owns Your Face?” the FBI has a facial recognition database with 52 million faces and up to one-third of Americans.[2] It’s easy to imagine facial recognition technology used against high-profile criminals: spies, fugitives, assassins. But we’re not legally far off from using facial recognition to catch people breaking ordinances and committing misdemeanors. Many  states, in fact, have already embraced this kind of enforcement.

With some libraries reporting patrons to the police who owe library fines, the above scenario could be closer than we think. And there are other possible benefits, too. What if facial recognition software let library staff know if a dangerous individual, such as a pedophile, has entered the building? This technology could make us more secure no matter where we were. But there are immediate legal and ethical concerns.

Legal consent: To run facial recognition on an individual, do you need their permission? According to most, security and law enforcement uses would be exempt from the consent issue. But where is the line drawn? What about non-law enforcement applications and consumers? Where would libraries fall? Many say a transparency clause such as a sign letting patrons know facial recognition is being used would be enough. But would it really?

Ethical conduct: Facial recognition is a part of the larger debate about the ethics of big data in general.[3] The simplified guidelines state:

• Collect minimal data: collect only the data absolutely needed
• Aggregate data: strip the data of personal information while still retaining its usefulness
• Identify and scrub sensitive data: know how to deal with sensitive personal data
• Let users opt out: allow users to deny use of the data you collect about them

The problems with facial recognition as big data?

• Your face is not minimal data. It is the only data in facial recognition.
• You face cannot be stripped of its attributes and aggregated.
• Your face identifies you, and so does other sensitive data.

Sure, people can opt out, in a sense. However, in a public institution where facial recognition is being used, such as a library, the only way to opt out is to not visit that place at all.

Accuracy: How accurate is the software anyway? Alarmingly and amazingly so. In a project entitled “Your face is big data,” Rodchenko Art School student Egor Tsvetkov began photographing about a hundred people who happened to sit across from him on the subway at some point.[4]

Using a simple facial recognition program called FindFace and the Russian social network VK, he found 60 to 70 percent of those aged 18–35. For older people, the experiment was a little less efficient, possibly due to lack of social media presence.

The Atlantic points to a study by Carnegie Mellon’s Professor Alessandro Acquisti that found about one-third of people walking around a college campus could be identified simply by using Facebook profile pictures as the data source. “In other words, 33 percent of people on any given street can be recognized by jury rigging a webcam, Facebook, and using Google’s reverse image search.”[5]

“From a technological perspective, the ability to successfully conduct mass-scale facial recognition in the wild seems inevitable,” Acquisti said. “Whether as a society we will accept that technology, however, is another story.”[6]

As big data gets larger, as facial recognition databases get more complete, and as the searching and analysis of them gets better, the uses of this technology will get broader, and libraries will be faced with choices about how and where to use it.

Your patrons’ faces belong to them. But they are not the only ones who consider and track those unique features that make them look like themselves. It is our responsibility not only to use this technology wisely and protect ourselves but also to protect the privacy of those who use our services. It’s a challenge we will face sooner rather than later.

References

[1] “The White House & The Booming Data Industry – Master of Information,” Rutgers Online, accessed May 16, 2016.

[2] Robinson Meyer, “Who Owns Your Face?” Atlantic, July 2, 2015.

[3] “The Ethics of Big Data,” Villanova University, 2016.

[4] Katherine Noyes, “‘Your face is big data:’ The title of this photographer’s experiment says it all,” PCWorld, April 13, 2016.

[5] Alessandro Acquisti, “Who Owns Your Face?” by Robinson Meyer, Atlantic, July 2, 2015.

[6] Ibid.

The post Facing Privacy Issues: Your Face as Big Data first appeared on Public Libraries Online.

Big data’s companion field, data science, focuses on extracting knowledge from these large data sets, and practitioners are called data scientists. Much like with big data, data science emerged when the right conditions developed—robust computing power, massive data sets, theoretical algorithms to extract knowledge, and powerful and flexible program languages. In practice, data science often focuses on predicting customer behavior and financial outcomes using large data sets that previously would have been too large to process for analytical purposes. Performing such tasks draws on a number of skillsets including machine learning, database programming, and predictive analytics According to Levi Bowles, practicing data scientist and author of DataScienceNotes.com, “The core abilities for a data scientist include higher level math statistics skills (calculus and beyond), computer programming, understanding business principles, as well as the scientific method and experimental design.”[2] Additionally, communication skills to translate highly technical findings to stakeholders throughout the business or organization are a huge plus. This combination of skills, encompassing expertise from a broad range of a number of fields, is a tall order.

As the field of data science has naturally evolved from diverse roots, including mathematics and computer programming, there hasn’t been a clear educational pathway for practitioners. Recognizing this gap, three academic units at the University of Illinois at Urbana–Champaign created a Master of Computer Science in Data Science (MCS-DS) degree in collaboration with Coursera, an online service offering massive open online courses.[3] The three units joining forces in creating this area of study are Department of Computer Science, Department of Statistics, and Graduate School of Library and Information Science. Unlike traditional graduate programs, the coursework is “stackable,” offering opportunities for students to focus on specific areas and earn certificates for study without the requirement to commit to the entire master’s program course load.[4] This flexibility allows both students new to the field to pursue a robust academic program in data science and also for practicing professionals to return to the classroom to focus on their specific areas of interest.

There is rich potential for collaboration between the field of data science and library science. Given data science’s powerful text analysis abilities and sizeable digital collections of significant works created by library science, there is an opportunity for a deeper understanding of content within the collection of these works looking at the broad collection to see patterns across millions—or more—documents. Since the capacity of an individual scholar to review documents over their entire lifetime would not match the capacity of data science’s tools to analyze in a relatively short time period, a collaboration of this nature, which can produce deep analyses of digital collections would complement individual scholarly study of documents.

Similarly, collaboration between the library science and library science could reap valuable information about citation patterns, such as the most influential scholars and journals. Relatedly, this collaboration could also identify citation patterns that are likely fraudulent. Work in this vein is already in progress at Louisiana State University where the Department of Mathematics and the School of Library & Information Science partnered to produce the presentation “Bibliometric Models and Preferential Attachment.”[5]

A final example of an area ripe for collaboration is result relevancy and recommendations: The tools of data science allow us to better predict user behavior. Capitalizing on this knowledge, search results and suggestions can be better refined based on user behavior for our patrons in library catalogs and online portals.

In summary, Urbana–Champaign’s Master of Computer Science in Data Science program seeks to fill a significant gap in the educational marketplace for the new and growing field of data science. This program found natural partners in statistics, computer science, and library science. Future collaboration in this vein could produce valuable understanding of library collections and citation behavior and can enhance library services.

References

[1] Gil Press, “12 Big Data Definitions: What’s Yours?” Forbes Tech, September 3, 2014.

[2] Levi Bowles, practicing data scientist, in an interview with the author, April 7, 2016.

[3] “GSLIS partners with CS, Statistics to offer first MOOC-based master’s degree in data science,” press release courtesy of CS@Illinois, March 30, 2016.

[4] Ibid.

[5] Department of Mathematics Partners with SLIS for Research Presentation. (2016, March 18). Retrieved April 26, 2016, from http://www.lsu.edu/chse/slis/news/smolinsky-research.php.

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As reported by WTMJ-TV, the bill pokes holes in privacy laws for Wisconsin’s citizens. Just as the healthcare industry deals with issues of confidentiality and privacy, this new state law may create its own legal headaches. When patrons sign up for a library card, they submit private information, such as their addresses and phone numbers. They do not permit libraries to hand over that information to other parties, whether or not they owe fines. Wisconsin has obviously been tough on patrons who don’t pay their fines. One Shawano woman was jailed in 2011 for not returning materials and racking up nearly $500 in fines. In Idaho, fines and private information can be sent to collections, but no one appears to have been jailed yet. Public outcry from states that advocate a more hands-on approach to government could stall efforts to mimic Wisconsin.

Many libraries already offer potential alternatives, like e-book lending services, and these typically don’t require patrons to do anything but link a Kindle or Nook account to a library card. When the lending period is over, the book is simply disabled, mitigating fear of fines and any other repercussions. In October 2015, Troy Lambert discussed e-lending versus subscription e-reading services. He concluded that libraries’ e-lending services would still come out on top, but that was before this new legislation. Now it may be worth it for people to pay $10 a month or Amazon’s $100 a year for unlimited e-reading rather than risk fines, a credit rating hit, and even a police record. If library borrowers worry about ending up in jail for their fines, sites like Audible.com and podcasters may become the future of lending.

As Wisconsin’s legislation is brand new, its ramifications may not be felt for months, if at all. Yet for patrons who have been fined before, the thought of incurring a police record for using the library again may be enough for them to think twice about borrowing another book.

References:

[1] Associated Press, “Libraries can now report overdue fines to the police.”  WTMJ-TV, March 1, 2016.

The post Will Reporting Fines to Police Hurt Library Patronage? first appeared on Public Libraries Online.

I wrote a few months ago about the data skills that future academic librarians can develop—but what would a data librarian look like in a public library? In this post, I’d like to review a few data concepts, outline potential differences between academic and public librarians, and suggest ways that public librarians could bring data to their patrons.

Data in the Public Sphere

You’ve heard about ”big data,” which I’ll loosely define as enormous collections of raw information. Ten thousand tweets on a given day, a million clicks on a website by 35,000 people, a hundred thousand economic indicators. How would you make sense of it all? That’s big data.

Click the animation to open the full version (via Penny Stocks Lab).

And big data matters, because it’s the method through which our personal life is swept up and analyzed by marketers, law enforcement, and researchers. This analysis of groups and individuals then impacts public policy, the economy, and our chances in life. But data isn’t just a danger—it’s also an opportunity. You and I have more access to datasets (collections of data about many separate people, institutions, or events) than ever before.

America’s Chief Data Scientist defines data science as “the ability to extract knowledge and insights from large and complex datasets” (whitehouse.gov). This resonates with one of our goals as librarians: to help people extract knowledge and insight from books.

In 2013, Obama signed executive order 13642, requiring government agencies to share their data in a way people can re-use, not just in summary reports. It’s a great move: it puts data about schools, the economy, business, and the environment into citizens’ hands. It allows ambitious high school students to do original analysis, journalists to cross-check official statements, community members to run advocacy campaigns, and business owners to evaluate the strength of their market.

And because this data is “open,” it comes at no cost to the community. As Meredith Schwartz writes in Library Journal, open governmental data is big news. Agencies now have to share—but we still need public user interfaces, local workshops, and skills tutorials to make this information truly accessible.

So how can libraries help? Academic librarians are compiling public and private data sources, teaching data analysis and visualization, and sharing how to manage and archive local data. Library schools are even hiring data specialists to train the next generation of tech-savvy librarians.

But there are strategic ways for public librarians to get involved as well. Just as e-books are available online and we help community members to use e-readers, so many types of data are online—and community members will still benefit from a guide.

Case Studies of Public Libraries in the Data Sphere

This spring, the Knight Foundation awarded a major grant to the Boston Public libraries to catalog and make regional data available to the public. Additionally, it awarded another grant to the Library Freedom Project so that public libraries could train citizens how to avoid the worst in data surveillance. Libraries like the Brooklyn Public Library are beginning to use Tableau to visualize their collections and patron needs, finding that visual displays of data capture the imagination of librarians and community members. Amidst thechallenges facing public libraries in the UK, Ben Lee argues that public libraries were created to help the working classes take ownership of their lives and communities—and that training residents to find and use public data fulfills a similar mission in the modern era.

What Would a Public Data Librarian Look Like?

As AnnaLee Saxenian says,

“A data librarian has a special set of responsibilities around stewardship and curation. . . defining standards, storing data . . . and organizing data in a way that makes it more accessible. And it may be a bit of an uphill battle.” 

While we would never want to replace the responsibility of other municipal agencies to care for their own records, data librarians could help patrons access public data, and even teach some of the skills that would allow people to make better use of these new resources. Given the cachet of “big data” in popular culture, publicizing the existence of “data librarians” could reinforce the relevance of public librarians as guides in the internet age.

Data training for librarians

While academic librarians focus on finding and managing research data, public data librarians are more likely to focus on open data: opening up the world of data to the community, helping people to access public data, or hosting workshops on data skills. Here I’m thinking of things like scraping real estate data and visualizing it using infographic tools like impact.io. People don’t need a data genius as much as a data guide—and that’s what librarians are there for.

So how could we get started? I would advise starting with School of Data to learn baseline concepts, and work through the Data Journalist’s Handbook to be able to teach how to work with public data in Excel.  Online study programs like Coursera and Edx run free classes on statistics, as well as more advanced courses on data science and data analysis.

Library schools are also likely to gear up and offer continuing education certificates in this area. As Sandy Hirsh writes from SJSU:

“We need people working in areas like big data who are coming in with the perspective that you get with an MLIS degree. . . it’s very different when you develop skillsets for big data from an LIS perspective.”

I’d suggest that this is true not only for LIS students going into software and tech development, but also for those who go into their communities and teach people how to find and use data. It fits our original mission so well: to bring knowledge to the community.

The post Data Librarians in Public Libraries first appeared on Public Libraries Online.

BPL turned to Tableau, a software company that offers a family of interactive data visualization products focused on business intelligence.  According to Manager of Strategic Initiatives Diana Plunkett, the hardest part of getting started was finding where the data would come from.  “We started with our simplest metrics, the ones that were easiest for us to capture. Our data around circulation is pretty clearly defined and pretty clearly understood, so that’s where we started,” said Plunkett.

Although much of the data BPL tracks is common (door count, program attendance, circulation, etc.), the data visualization reports help staff members make sense of the data. I took a look at some of the sample charts that BPL created through Tableau and am impressed with the results.  It’s one thing to look at door count numbers by hour, but to see those numbers in an attractive graph makes a much bigger impact:  http://public.tableausoftware.com/profile/bpl.it#!/vizhome/ShopperTrakv4/DoorCountbyHour

Not only does the visualization make the data more accessible, BPL makes the data available to everyone who works at the library.  I believe this is the single greatest benefit of Tableau’s capabilities and the way BPL is using it.  Giving all staff access to the data creates transparency across the organization since everyone can see the factors that are part of making decisions, and all staff members feel like they can lend a hand in making those decisions.  When data lives only within the IT Department and the Executive Committee, libraries miss out on the input of those on the front lines.

“A lot of the data we are displaying in these visualizations is data that was captured before, but there wasn’t an easy mechanism for everyone in the organization to see the result of that captured data all in one place,” Plunkett said.  “We find that people are more effective in their reporting because they can see the results. It’s not just being reported and it goes into a black hole somewhere. The visualizations make it so that people who aren’t used to diving in and mucking with the data can easily take a look at what’s going on, and understand what actions they can take as a result of it.”

Now that BPL has curated a set of data in Tableau and staff members are on board with the resource, the organization is looking to pull from local data sources as well as its own data warehouse for more ad-hoc analysis.  Plunkett believes the ad-hoc aspect will encourage more staff members to share their own ideas for data analysis and create more collective brain power.  BPL also plans to share some of the data with patrons as a way to increase awareness of the library’s services.  The appealing visual narratives might also be useful in proving the library’s importance to politicians and other stakeholders.

Sources:

http://diginomica.com/2015/02/18/how-the-brooklyn-public-library-data-visualization-a-better-library-with-tableau/

http://www.tableau.com/learn/stories/brooklyn-public-library-saves-time-money-and-headcount-tableau

http://www.ala.org/acrl/publications/keeping_up_with/big_data

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