How can you prove your library's relevance with data?

It should begin with aligning your library annual operational plan (AOP) with the university strategic plan. This is very important to identify your goal and objectives for the academic year. Then you need to identify the KPIs for each of this objectives and the primary requirement for this should be that the KPIs should be measurable. The target audience can be students, staffs and faculty members. For bechmarking you can measure it by comparing your results with similar university libraries within the region or across the world.

Proving your library's relevance with data is crucial. You can start by collecting and analyzing data on library usage, such as circulation statistics, program attendance, and visitor demographics. Conduct surveys or focus groups to gather feedback from patrons about their needs and satisfaction levels. Track the impact of library services through metrics like information literacy outcomes or community engagement measures. Use this data to create reports and presentations that demonstrate how the library is meeting the needs of the community and making a positive impact. Data-driven evidence can help showcase your library's relevance and secure support for future initiatives.

If you are using statistics from a vendor, like a database or library catalog, don't be afraid to ask for help building reports. Vendors are usually happy to have a meeting with you to explain how to create reports (and let's be real, try to sell you other products too!).

To prove your library's relevance with data, focus on key metrics like circulation, patron demographics, program attendance, website/social media analytics, user surveys, and partnerships. This data will show the library's impact, engagement, and community value.

Data is extremely useful, however, nothing can replace knowing the needs and desires of your Patrons. Comparing your facility to others in the region can be a tricky task. I found geography is a key stakeholder in in a library's success. Circulation data can be telling and helpful. Analyzing a collection by topic and there is value in analyzing the quality and quantity of varied genres. If your collection lacks sources within specific catalog numbers, your data for circulation could be skewed.

The quantitative method works with the satisfaction surveys with the faculty, students and staffs. The other method applied can be observation method, focus group and even a qualitative method where you can do interviews with student Council members and the respective deans or the program chairs of various department. So a combination of various method of data collection can provide a more transparent picture in the long run. Data collections tools from opens source such as Google forms, survey monkey's and many others can be deployed.

I have a library system that analyses the issuing and returning of the leading materials. While shelving, I arrange the books according to the subjects so as to determine the subject the library users are interested in for a particular duration of time.

Streamlining information is another way to improve library usage. The fact that a lot of collections have been put in a digital format is very useful, it allows resources to be accessed by a large group at once without having to wait for print media to come in especially if it is in transit from another library. But making information more accessible via digital collections is a great way to monitor the data that patrons are actually making use of in the library.

The data collected for visualization requires librarians and library managers to become something similar to a position in sales. It is critical for future purchases and acquisition to be justifiable. Making the new and relevant content visible to patrons is the one key to circulation increase. Displaying the data or collection statistics is important, but nothing can replace an interaction with the librarian. Get out from behind the circulation desk.

There are several tools for data analysis and visualisation of various data collected whether qualitative or quantitative. Excel is still a great tool for data analytics and data visualisation. There are several other tools for data anlystics both open spurce and paid sources and it depends on the organizational requirements. Tools such as power BI, SPSS, PSPP, QDA and many others can be deployed. I would always consider open source tools for data analytics and data visualisation.

The data story can be easily said through your annual operational report at the end of the year. Various dashboard visualisation of KPIs can be integrated in the report. This need to very detailed and explained it with examples and should connect and relate it with users. Certainly the audience will include the students, faculty members and staff and can be tailor made for each of these audiences.

Playing around with data is a journey and you can play around with it and evwry year you have to keep abreast with new tools and applications and update your data collection and data analytics techniques. Also you need to review your surveys and questionnaire based on recent developments and technological changes. Identifying the strength and weakness of your outcome of the whole research design process and reading the data will surely help in improving various services.

Arguably the most important paragraph in this article. Dialogue among interest groups in necessary in the development of a quality library. Communication among stakeholders helps to determine the validity of the statistics. Many times numbers do not always reflect the facts. Percentages are constantly changing, so a close frequent reanalysis is key to the survival of your facility.

At my library, we have seasons of "Reference Tallies," where all three public service departments track how many interactions they have with patrons in the defined period of two weeks that count as "Reference transactions." I don't yet know what my supervisor does with these data sheets.

Don't be afraid to revise how you collect data. When compiling your data at the end of the year, look at the data. What did you forget to collect? What questions are you being asked by others? Shift your approach for next year.