The term "mashup" started in the music world but has rapidly been adopted in the World of the Web to mean an application that combines data from different sources into a whole new application.
Data mashups are one of those brilliantly elegant synergies that appear once in a blue moon. The Web now offers many services -- from Google Earth to real estate listings. Each has its own value, but mash them together and new synergies appear. You can, for example, create a map that shows all the houses for sale that are within walking distance of a particular school, or all the gas stations within the range of the fuel left in your tank.
Although many mashups are map-based, they can coalesce other kinds of data, too. For example, if you're looking at a book by an author at an online bookstore, a mashup might also show you the author's bibliography and biography drawn from another site.
Understanding data mashups within the enterprise
To date, mashups have been traditionally implemented as publicly exposed websites funded by advertising. That's fine, but don't overlook the fact that data mashups can have huge commercial value within an enterprise, safely concealed from prying eyes and secured behind the firewall. Let's start with the obvious (to me, anyway) – spatial data being combined with a mapping service.
Two prime examples of
So data mashups are not just toys, they can be serious business tools. But what have they got to do with business intelligence (BI)? Everything!
BI is about turning data into information. If we want to get philosophical about this, we can argue that plotting spatial data onto a map is, all by itself, an example of BI because it makes the data much more accessible to the user and therefore turns it into information.
But it gets even better because BI systems are already very good at sifting through (and/or aggregating) huge volumes of data and turning it into information. If we combine this with mapping, we can get a huge increase in the understandability of the information for very little effort.
Mashing up analytical business intelligence data
For example, suppose you are interested in the effect an advertising spend is having citywide. Your company invested in renting several prominent and expensive billboards in a city to advertise your latest products. Your current BI system can probably return the percentage sales growth by city area over the last month as a list of raw numbers from which you can create a graph and work out whether your billboards are earning their keep. Alternatively, you could mash up the same values as a heat map laid over the city and see instantly whether the high growth correlates with the billboards' locations.
With services like Virtual Earth and Google Earth, you can do this across one city, the country, the continent or the world. Better yet, all that mapping comes free of charge. And it keeps on getting better.
Business intelligence is also about turning information into a business advantage
Now suppose that your BI system tells you that, in the past, sales of hot dogs double whenever the local baseball team plays at home after winning three games in a row. It's a fascinating factoid, but it doesn't make you any extra profit unless you can get the extra dogs to the relevant stadium in time for the right game. Your company is very unlikely to store baseball results, but there are plenty of sites out there that do. And there are plenty of shipping companies that provide data about trucking availability and pricing. On its own, your BI system is simply predicting the future. But mash up that prediction with these external data sources and suddenly your BI system is able to predict the future in a way that you can act on quickly to increase profits.
But isn't all this hard to do?
Details. OK, so the mapping is free, but there is still the development cost and that has to be paid for. I have recently been working on one such project, and so I tried to (as accurately as possible) estimate the cost of developing a "bashup," my way of describing BI plus a data mashup.
The BI system in question was fully operational before we started. It's composed of a data warehouse that pulled together data from disparate sources and a BI system that could extract analytical data consisting of sets of spatial coordinates.
The development team for the bashup was just a database programmer and a Web programmer. In a total of five developer days, those two people created a Web service to extract the data from the database, the necessary stored procedures and views on the database, and the bashup that sent the data to Virtual Earth and displayed the result.
The Web service and the bashup were each about 75 lines of code (ignoring the machine-generated lines that take no effort), so about 150 handwritten lines in total, and much of that was cut, pasted and modified from existing code. In fact, it was working within two days – the other three were for testing and tweaking. How much that actually costs will depend on what you pay your developers – but whatever that is, we are still talking about a trivial investment for a major analytical improvement.
So, are data mashups combined with business intelligence here to stay? Of course. Have these bashups reached their full potential? We've barely scraped the surface. But don't sit back and watch this space -- think laterally, do some bashing, and grab some real competitive advantage while it's going.
Readers: What do you think of the "bashup" term -- data mashups plus business intelligence? Have you developed an interesting enterprise data mashup leveraging business intelligence data? What other questions do you still have? Share your questions and stories with the SearchDataManagement.com editors.-------------------------------------------------------------------------------------------------
About the author: Dr. Mark Whitehorn specializes in the areas of data analysis, data modeling, data warehousing and business intelligence (BI). Based in the U.K., he works as a consultant for a number of national and international companies, designing databases and BI systems. In addition to his consultancy practice, he is a well-recognized commentator on the computer world, publishing about 150,000 words a year, which appear in the form of articles, in publications such as PCW and Server Management Magazine, white papers and books.
He has written nine books on database and BI technology. The first one "Inside Relational Databases" (1997) is now in its third edition and has been translated into three other languages. The most recent is about MDX (a language for manipulating multi-dimensional data structures) and was co-written with the original architect of the language – Mosha Pasumansky. Mark has also worked as an associate with QA-IQ since 2000. He developed the company's database analysis and design course as well as its data warehousing course.