Saturday, September 22, 2018

Data-driven approach of managing the libraries based on web based data analysis and visualization tools.

Data-driven approach of managing the libraries based on web based data analysis and visualization tools.

Libraries of big scientific and academic institutions are creating  enormous data every  day  through their scientific and research activities, mostly the data is raw and need to be organized for helping in further research and other activities. To know the amount of research going on, information seeking behavior of users, their needs for scientific data, what type of data is in demand and how extensively our data is being used,  are some points that library authorities  work over to improve the services of the library  and to justify their value and contributions. Data analysis and visualization tools can be helpful in this regards.
Library usage data can be analyzed and used to identify the use patterns of library users, assessment of which can lead to new and improved library services. In many libraries of the UK and US the projects have been started in finding out the patterns of usage data by using web based data analysis and visualization tools to improve understanding of how library services can be improved in a variety of perspectives.
 For example: “Ireland's largest university, UCD (University College Dublin) will use OCLC Sustainable Collection Services (SCS) to tackle collection management challenges as it redefines its library services and facilities.”1
 “NCSU (North Carolina State University) Libraries’ project uses a variety of source data formats, from custom logs and database tables to Excel spreadsheets. Visualizations are created using a variety of tools -- such as the Google Visualization API, Protovis, GGplot2, Microsoft Excel, and Adobe Illustrator -- to display the data as both static and interactive graphs.”2
“Data analysis and visualization software at NIH Library: The NIH Library’s Data Services Program offers assistance throughout the research process, including providing access to a data sciences workstation in the Library.The data sciences workstation features tools for data analysis, processing, and visualization, as well as links to information on using data tools, conducting research using data mining, and finding data available for reuse.”3

Uses of web based analysis tools in libraries

  • It will help in SWOT analysis of our libraries. To find out the strengths, weaknesses, Opportunities and Threats of any library, based on which the library authorities can take the decisions for the advancement of libraries.
  • Understanding users’ information seeking behavior and information access patterns. 
  • Assessing the usage of the Library web pages and other web-based services.
  • Helpful in taking decisions about acquiring collections by analyzing circulation trends and thus helping in allocation of budget in various resources of the library.
  •  Understanding collection use and thus determining what collection needs to be kept and what to be weeded out to maintain a vibrant and relevant collection while freeing space for other activities.
  •  Understanding reading patterns of users and finding insights about medium of reading print and electronic.
  •  To help justify library funding and preparing a good proposal for fund seeking.

Some examples of data analysis and visualization tools:

1. Spreadsheet tools: Microsoft Excel, Open Office and Google Docs in which excel is need not to tell about, are some tools supports all the important features like summarizing data, visualizing data, Transformation and mapping data etc. which are powerful enough to inspect data from all possible angles.

2. Tableau Public: A data visualization tool that does not require any programming knowledge.The Tableau is not open source, but is available for free if you save your work      publicly on the Tableau public server.

3. Google’s Fusion Table: A data visualization web application to gather, visualize, and share data tables. It allows users to decode points and create point and heat maps.

     4. Qlikview: It is not a statistical software, but best in exploring data, trends, can be used to assess library’s web based services and exploring the insights of library web pages.

5. Data Applied: It is powerful, interactive, non programming tool to build, share, design large  data analysis reports. It is robust in in visualizing large amounts of data using tree maps.

     6. AnswerMiner: It is very simple in connecting your database or upload the file in any  format to explore data from this application. No coding and statistical knowledge is required  for visualizing data, finding hidden information, creating charts and metrics.

Thus the time has come up to follow the data-driven approach to manage library, building collection, implementing new services based on that.



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