Managing Libraries with Creative Data Mining Learning to

Managing Libraries with Creative Data Mining Learning to Use Your Library’s Data Warehouse to Understand Improve the Services You Provide Ted Koppel The Library Corporation Computers in Libraries 2005 Session B 203, March 17, 2005

The Plan • • • What is data mining and why is it useful? Who else does it? Does it make sense for libraries? Are libraries already doing data mining? What data can libraries mine? How much sophistication do I need?

What is Data Mining? • Collection and Analysis of one’s own data in order to make better business decisions. • More than simple data storage • Business intelligence technology for discerning unknown patterns from large databases • Uses statistics, artificial intelligence, various modeling techniques • Related to, but different from, bibliomining

Value and Importance • By identifying patterns and predicting future trends … – Make decisions based on facts, not guesswork – Develop sensible processes – Reduce costs or increase services by efficient use of resources • Serve the customer better

‘High Level’ planning • • Remember -- GIGO. Define the data mining goals Data collection Data organization and normalization Analysis Reiteration

Who is Data Mining now? • Manufacturing –process control • Banks and financial institutions – “full service” • Government and law – fraud, abuse • Sports – RHP versus LHB? Sucker for a curve ball? • Service industries – almost all CRM systems • Retail: product stock and placement • Travel: airline overbooking • Las Vegas: guest tracking for comps and benefits • Groceries: affinity cards • Internet: Google. Ads

Nuggets Found by Mining • Chase Bank: minimum balance versus other bank business • Home Depot hurricane planning • Wal. Mart (UK) diapers and beer (actually a hoax, but an informative one) • Casino security in Las Vegas - fraud

Implementer Level Tools • • Oracle® Data Mining Suite Microsoft SQL Server 2000 SPSS and similar Statistica STATSOFT • Open Source: – Cornell Univ. Himalaya Data Mining Tools – WEKA Waikato Environment for Knowledge Analysis (Univ. of Waikato, NZ)

Looking for the Dog that Doesn’t Bark • NORA – Non Obvious Relationship Awareness – Examines third ++ level relationships between datasets • ANNA – Anonymized Data – Double-blind application/offshoot of NORA that deals with personal attributes anonymously

Vocabulary Lesson • Bagging (averaging) • Data Models: • Boosting (calculating predictive data) – CRISP = Cross Industry Standard Process for DM • Drilling down – SEMMA = Sample, Explore, Modify, Model, Access • Stacking (combining predictions from different models) • Predictive mining (using X to predict Y)

Value to Libraries a Tool • Citizens demand more/better service at a time of reduced funding. • Anticipate USER behavior • Anticipate STAFF behavior • Service hours and staffing needs, facilities planning • Collection development – anticipating customer needs

Do Libraries Use DM? • Association of Research Libraries ARL Spec Kit 274 (2003) – Mento and Rapple – 124 surveys, 65 responses – 40% already doing some data mining – 90% had plans • Major areas of activity – Research and Collection Support – Administration – Repository management (future)

ARL Member Benefits Seen • • • Serials cancellation projects Collection Development tuning Budget allocation by material use Workflow analysis Weeding OPAC and Web presence usability and redesign • Hacking and break-in analysis (defensive data mining)

Other Library Data Mining • Kun Shan University of Technology (Taiwan) – ABAMDM Model = Acquisition Budget Allocation Model based on Data Mining – More material use More money – Compared: • • • Circulation Collection size Department size # of courses # students/faculty per department

Other Library Data Mining (2) • OCLC’s ACAS (Automated Collection Analysis System) (recently upgraded!) – Analyzes bibliographic records by call number ranges (LC 4 -digit, Dewey tens for example) – Subdivides by years and aggregated years – Subdivides by branch / collection – “Collection conspectus” as a way to: • Compare library collections • Identify collection deficiencies

Other Library Data Mining (3) • Univ. of Florida with FCLA – – Decision Support System for acquisitions activities Extracted from NOTIS bib files; saved to DB 2 Screen scraped Acq files Created large database of bib and in-process records which allowed querying: • Circ history of approval versus firm orders? • $ spent on titles that never circulate • Do originally-cataloged items circulate? More or less than copy cataloged items? • How many items circulate more than “n” times? – Assesses collection development and tech service activity

Libraries are fountains of data

Everything is countable (example: • Book: § § § branch location Media type pubdate size color thickness #circs cost vendor holds Circulation transaction) User: Extractable: § age § Census Tract § Location § Curriculum § Language § Holds § Sex § Circ History § Zipcode § Repairs § phone# § School § Loan history § delinquencies Multiply this by 10 million times a year!

Expand to: • Acquisitions information (book attributes, vendor history and performance, fund history, requester and department, etc. ) • OPAC searching and navigation (databases, searches, not founds) • Metasearch usage (databases, usage) • Reference desk interactions (who, what, how long? ). VRD by extension • Resource sharing (NCIP, ILL) • In-house usage transactions • Physical plant: elevator, restroom, copier use

Crunch (Data) Creatively • Unlikely variables give interesting data • Ideas: – Sex of user versus color of book – Call # range vs. age of item vs. circulation ratio by avg. $ paid per item – Story hour attendance vs. Adult circ vs. Fines collected – Best sellers cost vs. Trade books by cost per circ – Etc.

If you can count it, you can analyze it But remember QUALITY and CONSISTENCY

• Library Automation vendor for over 30 years • Family-owned, customer focused • Library • Solution® • Library • Solution™ for Schools • CARL • Solution® • CARL • X ™

Library • Solution Reports • • • Utilizes Report. Net software Drag and Drop Report Design Completely Web-based Fitted to Library. Solution data framework Zero footprint on workstations Central reporting with enhanced distribution • Multiple export formats • Charts, tables, etc. • Powerful

Using Library Data Outside the Library • City, County, RCOG, State Planning and Development Authorities – Require solid statistics about population, educational level, etc. – Quality of Life and capital budget services planning • Preserve user anonymity but share trends • Input to GIS systems for real time projection of future library needs

Applying GIS in the Library Market • • Library. Decision product Works with ILS vendors including TLC Focus collections development Strengthen advocacy planning; undertake cardholder development campaigns Support grant applications Site new facilities Calculate service indicators Evaluate service delivery in relation to the unique needs of your community

In closing … • Libraries are producing data every minute of every day • You need: – Some tools – Some creativity – Some analytical ability Knowledge is Power !

Acknowledgements • Nicholson and Stanton, Gaining strategic advantage through bibliomining. At www. bibliomining. com • Banerjee, Is Data Mining Right for your library? Computers in Libraries, Nov. 98 • Kao, Chang, and Lin. Decision Support for the Academic Library…, Information Processing and Management 39(2003) • Fabris. Advanced Navigation. CIO May 1998 • Library Administration and Management (journal) Winter 1996, section on Data Mining

Thank You • Contact information Ted Koppel The Library Corporation tedk@tlcdelivers. com (800)624 -0559