One of Podio’s longstanding weaknesses has been dashboards and business intelligence visualizations. So I was pleasantly surprised to discover this app, ChartGrids.com
By drilling down through Podio Org to Workspace to the App whose data you want to visualize, you can combine charts from different apps into a dashboard. Right now it’s only bar charts. But area charts, pie charts and donughts charts are on their way.
Using flight data to guess at corporate merger activity. I think I saw that in House of Cards. Sifting through the tea leaves for analytical intelligence. I’d love to take a gander at this, but I’m afraid I’m not in their hedge fund target market. I’ll have to content myself with their free offerings. Which still have me intrigued about porting the feeds into PowerBI mashups.
If you’re interested in that kind of thing, here’s the blog posting for the Corporate Aviation Intelligence offering. In the meanwhile, there’s data fields to be a-playing in.
Enticing hints of what’s contained within – along with realistic expectation setting for something published so publicly.
So, after the movie explains why our ‘chaperoned’ romp through partially disguised data allows an alum in networking or job search mode to identify some prospects/targets, indicate their choices to a club administrator who balances privacy concerns against a stronger, more robust network by asking the target alums for specific permission to share (fortunately it’s almost always given)
Then on to a couple of reports & visualizations, all the while exhorting the alum to register for an access-controlled site for supervised exploration.
Salesforce Contacts, Employer Accounts and Non-Profit Edition Relationships carry the majority of the payload — supplemented by visualization tools like Google Maps Engine and SharePoint 2013’s robust security settings.
Click on the image to take a look at the public offerings.
When we last left ZoHo’s cloud-based Business Intelligence tool
it was with the promise –or was it a threat? — of coming back to discuss matters honestly. To jar your memory, here is the screenshot of the counts of record types:
So, going row by row:
University Alum Club has slightly more contacts than organizations — and that’s fine. From Columbia Business School (henceforth “CBS”) Club’s perspective, anyone in the file who’s an alum of another club is likely either a programming partner (in which case they should more likely be affiliated with that club’s Board – but that’s not universally true) or if not a subject matter expert helping us with programming development, s/he attended one of our events, we got some money from them, and aggregating those (relatively small) sums at their club level is as defensible a way as any — and might help us track who to be extra solicitous towards.
Alumni Club Head Hierarchies are a placeholder mechanism meant to unify relationships among clubs as is seen here. These records don’t represent anything real in the world — they’re just a convenient way to encode a hierarchical relationship in the database.
The other MBA clubs seem sound enough — (though truth be told there are a surprisingly large number of clubs, so I’ll have to go double check that with some troubleshooting). But logically those MBA club’s members/public would be the most likely of any alumni to attend my programming, so it would only stand to reason that they would be accumulating many more members with attendance and/or revenue association with them for each organization I tracked.
The CBS Alumni case is actually the weakest – and that phenomenon has two causes. But I’ll return to that explanation, which is lengthier, after I finish up the other lines.
I discovered today in the chrome webstore a nifty little business intelligence offering from Zoho.
So I uploaded some exports from Salesforce. First we’ll take a look at the login activity data, which begins to point towards how one audits things in the multidimensional space that is a database in the cloud, towards which lots of web services are making calls.
The summary function reporting of Zoho’s BI Tool is just like a SQL/MS Access GroupOn[Value] query. It enables us to take this table of 1,691 rows and look at the clustering of values. To tmake this interesting, I choose to Group On (and thereby collapse around) the LoginType field. And count the records to produce the following distribution histogram:
Absolute Automation is the name of an app by IHance, and it’s an email matching app that takes all email to my address and tries to find a Salesforce record to attach them to — it makes for a very thorough approach to CRM, which is rather exactly what we’d expect from Salesforce.com
Cirrus Insight is an app that syncs Google Apps contact data with Salesforce — and enables creating new accounts & contacts & leads from within the Gmail interface.. Those 175 entries via the browser — that’s me as the admin: a living, breathing mortal who is a mere piker in comparison to the hard working apps Such is the beauty and power of Cloud computing
Record Type Agreement between Salesforce.ACCOUNT object and Salesforce.CONTACT.
Record type agreement — after all my bellyaching about the importance of a record type schema that can handle the complexity of the milieu in which an Ivy League Alum Club operates. Record type agreement is one way to track if one’s practice lives up to one’s theory. Furthermore, this little exercise is providng an awfully convenient excuse to dig deeper in Zoho Reports. Pretty nifty the way it’s just a few short clicks until you can make some interesting discoveries.
The image below shows a portion of the 1700 plus rows in the table. The grey shaded portion are SF.CONTACT object fields; the light blue are SF.ACCOUNT object fields. And the dark blue are redaction on my part to safeguard my alumni data.
When I was enthusing earlier about dataloader.io, this is why: if you don’t pull over related records’ actual fields, to look at a Salesforce export — well for a human, it’s often not an easy read: long strings of digits in which upper v. lowercase actually counts!
One nice diagnostic test to run is to compare counts of Salesforce CONTACT records, by record type, against the number of organizational ACCOUNT records, by record type. The logic of the nature of the relatioships that are to be expected helps one to ascertain how well the coding schema is working. So, again, using GroupOn ACCOUNT.Organization Record type: what inferences can we make about the SF.CONTACT records by type?
Take a look at the entires in the report and, as Linda Richmond would say: “discuss amongst yourselves.”
I’ll use non-Linda-Richmond diction by noting that I’ll return to this anon.