I previously asked who was sending all the email; well the next question is who receives it all? Once again the middle management features highly but then so do the senior managers. This sort of result is probably what I would expect thinking about the roles these groups perform but is this typical of most organisations?
Average emails received per person, grouped by rank.
This is a pretty simple metric to look at but might be quite revealing to your organisation. I’ve matched the sender of an email to their rank derived from the corporate directory. The rank is their position from the top of the directory e.g. A manages B manages C – if A is at the top of the directory then A=1, B=2 and C=3; this approximates their grade and roughly those ranked 1-2 are senior executives, 3-4 are middle management and 5+ get to do all the work. The chart shows the average number of emails sent at each rank over a number of months. Ranks 1 and 2 are combined because rank 1 is a very small sample.
In the above diagram red nodes are from the division of the organisation under study; green and blue are from two other divisions and grey nodes are uncategorised or central functions.
I’ve previously described determining a ‘score’ for social connections taking data from email, meetings, directory and timesheets. The question is how to combine them to produce as complete a picture as possible from the data at hand. I’m fairly sure the best answer is not to simply add the scores together but I’ve not found any guidance that would help do anything except that so that’s exactly what I have done…add them up. To summarise the score (or more correctly weight) given to each edge is made from:
I’ve pulled this data together over the following periods:
The coverage of the data also varies:
Some Observations using this approach:
Despite the rather simplistic approach the results appear to work quite well but I’d love to hear from anyone who has implemented, or read about, a smarter way to combine these types of SNA sources.
The previous discussion of Dunbar’s number suggests larger meetings will be less effective. Is there any data to support this? The following chart shows emails sent during meetings:
Seems to suggest the larger the meeting the less attention people are paying but not a particularly remarkable result. I expect looking at instant message traffic during meetings would be more revealing.
I’m no Exchange Server expert, maybe there are some better built-in or downloadable
tools for doing what I describe here, but if you’ve loaded emails into a
relational database, as I’ve described previously, the following is extremely
simple. In arranging to get log files from the Exchange server I asked the
administrator if there was anything they wanted to know about that the logs
might reveal and, indeed there were: it turned out they wanted to know who was
sending the most email as storing it all was becoming more and more of a
problem. After trying a few different ways of reporting on the logs we settled
on the following: every time new logs were taken (about every 2 weeks) two
queries are run: the first extracts the top 100 sender-to-recipient traffic by
email count and the second extracts the same but by total message size (which
is why I capture it). Quite often the culprits were system accounts including
one that went a bit mad and sent 300,000 emails in 72 hours! Using these
reports the Exchange administrator has managed to work with development teams
to reduce the number of mails being generated by applications that it seemed no
one was reading.
When working with email there are a few things that can trip you up, here are some tips for avoiding them:
The Exchange server logs contain a message size. I have not yet found any use for this in understanding the social network but it’s useful to have when making friends with the Exchange server administrator, see my next article!
If you want to perform analysis comparing sub-sets of a graph derived from your email logs you will need to bring in some additional data, for example department. You might be lucky in that the organisations addressing scheme provides some clues e.g. ‘[email protected]’ or ‘[email protected]’ but if not you will have to find another source. Now you might have an efficient HR department who could provide an extract of employees with department, role and any other data they don’t mind you having. Or they might not be so helpful. If HR won’t help there are some other sources: the directory service (e.g. Microsoft Active Directory) or maybe an intranet site that acts as a phonebook and/or organisational chart. Which one you select will depend on content and accuracy. In my case the intranet site provided both the most accurate and richest source of data. Each intranet is going to be different so maybe the following is not going to work for yours but I hope the approach provides some inspiration.
The intranet I have worked with is composed of two page types:
Luckily each page type could be bought up by knowing the employee and manager ID and constructing a fairly simple url.
I thought it would be useful to understand where an employee sits in the overall structure so I wrote a program to traverse the directory top-down. Given a known starting point (the ID of the most senior employee and their manager, effectively blank) the algorithm worked like this:
Now unfortunately this does not find all the employees we have email records for; this turned out to be because the intranet directory is incomplete so as a second exercise:
This second step could be used to populate the whole list but it does not provide such rich hierarchical information.
The directory information is best retained in its own table. This is because it changes over time and should be obtained periodically (e.g. monthly). However a refresh of the directory data SHOULD NOT overwrite, instead add a field that contains the date the data was fetched. This makes it possible to use directory data that corresponds to the period of data being analysed or to still derive a tie between people: if A used to manage B, even if they no longer do.
Example directory table. Note: RunDate is the date the directory was traversed (there will be multiple runs in 1 table); ID and manager are internal IDs for the intranet directory; rank is the position in the hierarchy (0 highest); Span is the number of direct reports.
In previous entries about analysis email logs I mentioned the message subject which can, optionally, be included. Now this can be considered sensitive information and how you deal with this will depend on the organisation concerned. The organisation I’ve previously described decided to allow the message subject to be extracted but not stored as-is; instead it was agreed that the message subject would be hashed (a one-way encryption) and then stored. This is useful because it allows conversations to be tracked so that metrics like the average response time can be collected. There are a couple of other useful things to make the best of this approach:
saw this on LinkedIn, it shows the flow of email between countries, see the link below for a further explanation
could this be applied to communities within an organisation?
Graph analysis relies on getting data into Nodes (sometimes called Vertices) and Edges. If you have, as I suggested, first built a relational database then a second extract-transform-load step is necessary to get the data into the graph structure. I’ll assume the simplest scenario to describe these steps, which is simply to take all the data. Extract is fairly simple; there are two queries that can be run against the relational database: the first to get the nodes is just a straightforward select against the nodes the second query is to get the edges. The query to get the edges is not so simple and depends how you want to analyse the data; I’ll assume almost the simplest approach: you need to join the email table to the recipients table, select the Sender ID , the Recipient ID and a count(*) this is then grouped by the Sender ID and Recipient ID. This gives an edge for each sender-receiver plus a count of the number of emails; note this is directional, A sends B x emails and B sends A y emails. The simplest possible query could have omitted the count(*) but this is very useful as it gives an indication of the strength of the connection between two nodes (often called the Edge weight). The Transform step could be omitted if the desired analysis can be performed with the nodes and directed edges, however this is not always what’s wanted. If you want to understand the strength of a connection between nodes but don’t care about direction then a transform may be necessary. Now this can be achieved in other ways but it’s useful to understand how to do this in an intermediary because this is useful when combining data from more than one source. Now bear in mind this works for 10,000 nodes: I like to use a hash table (C#) to build the edges. For each edge first re-order the nodes by ID and then create a key by combining the IDs then test the hash table to see if this key exists; if the key is not found create a new entry using the key and store the count as the associated value; if the key already existed retrieve the associated value, add on the count and save it back. The hash table will now contain undirected Edges and the associated combined message count; you can see it would be easy to add in additional counts from other sources to create a combined weight for the relationship. The Load step is going to vary depending on the target but it’s basically taking the nodes as selected from the relational table and the edges from the hash table and getting them into the target. I’ll briefly explore three targets:
I hope this has given an overview of how to get email logs into a graph-aware database or application by using and ETL step to first store an intermediate relational database and a second ETL step to move from a relational to graph structure. Keep an eye out for future postings that I intend to drill into some of the detail and expansion of the architecture described here.
Follow