Category: Data

Data: Making better use?

One of my areas which I want to work on over the next year will be that of Management Information.   In my school as in almost all schools we have a Management Information System (MIS), sometimes referred to as a SIS (School or Student Information System).    This systems stores a large amount of student data including info on their performance as measured by assessments or by teacher professional judgement.    We also have data either coming from or stored in other data sources such as GL or CEM in relation to baseline data.   These represent the tip of the iceberg in terms of the data stored or at least available to schools and their staff.

Using the data we then generate reports which do basic summaries or analysis based on identified factors such as the gender of students, whether they are second language learners of English, etc.  Generally these reports are limited in that they consider only a single factor at a time as opposed to allowing for analysis of compound factors.   So gender might be considered in one report and then age in another, but not gender and age simultaneously.   In addition the reports are generally reported in a tabular format, with rows and columns of numeric values which therefore require some effort in their interpretation.    You cant just look at a tabular report and make a quick judgement, instead you need to exercise some mental effort in examining the various figures, considering and then drawing a conclusion.

My focus is on how we can make all the data we have useful and more usable.    Can we allow staff to explore the data in an easier way, allowing for compound factors to be examined?    Can we create reports which present data in a form from which a hypothesis can be quickly drawn?    Can the data be made to by live and dynamic as opposed to fixed into the form of predetermined “analysis” reports?   Can we adopt a more broad view of what data we have and therefore gather and make greater use of a broader dataset?

I do at this point raise a note of caution.   We aren’t talking about doing more work in terms of gathering more data to do more analysis.  No, we are talking about allowing for the data we already have to be better used and therefore better inform decision making.

I look forward to discussing data on Saturday as part of #EdChatMeda.    It may be the after this I may be able to better answer the above questions.

Data, data and more data

This morning it was the turn of the NHS to be the focus of the morning TV discussion about how things aren’t going well.    I suppose I should be partially thankful as this takes the spotlight off education at least for a short while.    That said it also once again shows the superficial use of data.

This mornings TV took some time, along with fancy graphics, to outline how the NHS waiting times had increased.   The specific figure they presented being the percentage of patients at A&E who were seen within 4 hours.   This seems like a reasonable statistic to use from the perspective of a patient as it suggests the likelihood that should I need to turn up at A&E I would be seen in 4 hours of less.   I suspect the fact that it is so potential meaningful for prospective patients, the average TV viewer, is why they picked this statistic over others.

The issue with this is what it doesn’t tell us the additional context which may be important in interpreting the figures.    Over the period under consideration did the number of patients attending A&E remain static or did they in fact increase which may be a contributing factor to increased waiting times?     A briefing report by Carl Baker from November 2016 suggested that in 2016 the number of A&E patients at major A&E departments increased 6.3% over attendance levels in 2015.   Were there any changes in the demographics of patients attending A&E as an increase in elderly people attending may mean that patients are less likely to be able to be quickly seen and discharged, again contributing to increased waiting times.    What about the staffing levels of A&E over the period?   Did this change as a reduction in staffing may account for increased waiting times?   Also the figures look specifically at average data for the whole of England; were there any regional variations?   Personally I live in the South West and feel that it is difficult to access a doctor which may mean that I would attend A&E on occasions where someone with more ready access to a GP would not.    Are there also differences between A&Es serving urban and rural areas?   Are there differences between A&Es serving large versus those serving smaller populations or population densities?

In the current performance indicator and accountability led environment we often focus on specific figures such the percentage of patients seen in 4 hours or the number of pupils achieving A*-C or Progress 8, PISA, EMSA, TIMMS, PIPS or other measures.    Each of these pieces of data is informative and tells us something however equally there are a lot of things that it doesn’t tell us.    We need to ask what doesn’t this data tell us and seek data to add context.

Only with context is data useful.

Accident and Emergency Statistics: Demand, Performance and Pressure, C Baker (2016), House of Commons Briefing Library (6964)