Decisions Blog

Archive for March, 2011

User Surveys and external data

Tools like Talis Decisions are designed to work with existing data from the Talis Alto database. They cannot report on data that do not exist or that are not accessible. This doesn’t stop users from requesting reports on loans per library visit, percentage of visitors who didn’t find what they were looking for, the attendance at library events, or borrowers opinions on stock, study facilities or decor.

There are a couple of strategies that can be used where users want data of this kind

  • Use of external data in Talis Decisions
  • On-line surveys

We have already discussed on this blog the use of external data within Talis Decisions.

Surveys are an interesting area. They can be used in “pure” mode to capture borrower opinion. One example of this was mentioned in an earlier post on information and action. The same software can be used to record statistics that might otherwise be either recorded on paper or simply lost, such as the attendance at homework club in a public library, or a library event on citation software in an academic context.

Google Docs forms There are many on line survey tools, some of them free; Survey Monkey is one example which we have used ourselves in the past. If your organisation has a CRM (Customer Relationship Management) system then it may have built in survey facilities. Even Google Docs allows you to create forms and accumulate the responses in a Google Docs spreadsheet. For many purposes this would be quite adequate.

One word of caution however. It is easy for administrative and management staff to do so much data collection that computer use becomes a burden to front line staff. The National Health Service suffers greatly from this. The main purpose of computing is to reduce the time it takes to get a job done, but many nurses and doctors will tell you that in many cases computerisation has slowed them down rather than speeded them up (not in all cases – medical imaging and some laboratory systems are positive examples).

Highlighting data in Talis Decisions

It is well said that the best place to hide a leaf is in a forest. It is all too common to miss important data in reams of print out. When you are designing reports, It is worth considering how to make it easier to home in on critical data. Here are two strategies:

Use a chart

Alert 1 Many users are keen to get actual numbers in tabular form, perhaps because they are required to report data in that form, or intend to do further manipulation in a spreadsheet. Here is an example. With some mental effort, it is possible to extract some useful information from the data. For example, it is clear from this example that non fiction is less well-used than fiction in both talking book and conventional book form. However, the data have to be read in detail and comparison done in the mind.

Alert 2 One way to make the data stand out more immediately is to use a chart. This need not be an alternative to the table. Once a query has been set up to bring back data for a table, the same data can normally be used to populate a chart with very little extra effort. The chart can be put on the same report tab or on a separate report tab. Here is an example of a chart showing Loans per item against Item Type. Charts are particularly good at highlighting trends (e.g. loans through time) or outliers (the one site with an average time to satisfy a reservation that is much shorter or much longer than all the others).

Use Alerters

Alert 3 In Talis Decisions, Alerters allow you to change the format (colours, fonts etc) in a table cell depending upon the data in the cell. This can be used to highlight data that has changed since last refresh, but in this context, it can be used to highlight outliers. One application for this is to embed Key Performance Indicators (KPIs) in alerters. In this example there is a minimum loans per item type KPI of 0.2. The loans per item for Adult Non Fiction is 0.16, so it is highlighted in red. You can have multiple alerters: to illustrate this, values > 1 have been highlighted in green, but in a real report there is often merit in keeping things simple.

If you have other strategies for helping users find their way through large volumes of data, please do add a comment.