Telling Stories: Mashups in Action

Here at JackBe we’re always trying to move the state of the mashup art forward. In past posts we’ve described mashup best practices like the 5Cs of Enterprise Mashups, mashup security, the integration of mashups with important enterprise solutions technologies like your SOA, and products like Oracle and HP Systinet.

Last week, while listening to one of my customers enthusiastically describe his enterprise mashup, it occurred to me that we’ve neglected one of the simplest and most useful ways to move the state of the art forward: telling the mashup story. That is why we’re starting a regular blog topic we’re calling ‘Mashups in Action’. Here we’ll share any real-world story that shows the value of enterprise mashups. So here’s my inaugural entry...

I recently visited with a CIO at a major medical research facility. He described the complex processes his researchers did every day. Along the way he described how they manually pick research data and citations from public sources like PubMed (and other third-party biology/genome data sources) and manually matched this against an internal datasource of research results through some key like topic, date, publication, or author. Then he dropped the bombshell: this matching process could take anywhere from days to weeks, occasionally even months!

Of course doing this in a non-mashup-enabled way would take exceedingly long. But the issues don’t stop there. It’s also error-prone, tends to age quickly (the final dataset can be out-of-date as soon as the first cut-and-paste is done), and most important to the CIO I spoke with, it is incredibly insecure (emailing spreadsheets? c’mon!). And that’s why this is my first Mashup in Action.

This Mashup in Action serves as a good metaphor for a number of JackBe's customers. It is about connecting the ‘outside’ to the ‘inside’ and it is one of the premier usage patterns in the areas that are research-heavy like legal, medical, intelligence, and investment.

I’m sure you’ll quickly realize that knowledge workers everywhere do this all day long, day after day, into spreadsheets or something similar. They start in common outside-the-firewall sources like SaaS apps like Salesforce.com, websites like Google, publicly accessible data services like Xignite, or an inside-the-firewall app like SAP or Oracle. These users select a small subset of data from these very verbose data providers as the starting point for their analysis because that’s all they need to get the job done.

Next, they use the some kind of unique identifiers in these data set(s) to join the data to an internal source. The internal sources are ones you probably know well, including off-the-shelf apps like SAP/Oracle, homegrown client-server application, or even other mega spreadsheets. The result is a composite data view used for decision making.

In a mashup this can be done in minutes to hours, of course. And they get the added benefits of security and collaboration, allowing researchers to save, tag and share resulting mashups for use by peers without exposing the data insecurely in an emailed spreadsheet or HTML page. Even with the issues of distance and security, it can be done better than days, weeks or months through enterprise mashups.

We’ll be back in a few weeks with another Mashup in Action. And, as always, we’d love to hear your stories!