Data Mashup in BI

Data mashup within Business Intelligence (BI) applications is one of the latest must have requirements that I have been asked to build into solutions that I have created for some major enterprise clients recently.  As mentioned in my last blog about the growth of NoSQL use within the BI area I believe that the mashup can be another useful tool to be added to the BI solution – but it must not be utilized to the detriment of the core principles of any BI solution.  The first types of mashup used mapping services or photo services and combined these with relational or excel data to create a visualization of the data.  In the beginning, most mashups were consumer-based, but recently the mashup has started to interest the wider enterprise.  Business mashups can combine existing internal data with external services to create new views on the data.

The problem with the clamour for the most up to date technology to be added to the BI application stack is as always the differing understanding between the business user and the developer to how they can be used.  The battle from the developer’s point of view is trying to get the people within businesses to understand what, how and where the mashup can be utilised within their organisations.  The problem from the business point of view is trying to emphasize the speed at which this development must be available to the business.  Mashups can enable nontechnical users to build dynamic views of disparate data that are personalized, context-rich, role-tailored, and ad hoc to explore this data in greater depth.  However the problem with most of the currently available BI Vendors mashup applications or plug ins is that they simply offer a numerical analysis of data via the normal OLAP cube route and then attach a search bar alongside this analysis to enable a search of separate silos of either textual, web or unstructured content to match up with the data already recovered and analysis.

The ability of a mashup to pull content from other sources is what most business users are excited about and this combined with the ability to store non structured data in a NoSQL environment which allows for rapid search and retrieval and storage of any and all linked data.  Most corporations are now requesting that BI systems have the ability to interrogate social networking sites to find out what is being said about their products – this is a perfect example of the ability of mashups to provide information that most marketing mangers and sales teams desperately need to understand to improve business productivity and sales success.  This requirement to link to all types of data also needs to be paired with the ability to interrogate all systems that are available within the corporate environment – there is no point in having a BI application which has the ability to mashup data if it cannot attach to all the clients information.  These results should also be shown not only in their normal context but in a context that is easy to understand and use for the customer.

I see mashups extending the current traditional data-driven BI solutions to incorporate traditional planned data from a normal RDBMS or OLAP cube adding in unstructured data and accessing further information from either RSS or the web utilising web services.  Most of the modern BI Solutions can solve the first two connections but to connecting to the web can require either a web service to be hand coded or the purchase of one of the specific connection applications available currently on the web.  As reported last week in the Briefing Room with Mark Madsen , Quest Software are about to bring to the market a group of tools aimed at both the Data Analyst and the Business Analyst/User which will allow for the full range of mashup capability to be available on the desktop for both those in IT and those in the Business.  This can only help to improve the harmony between these two areas of the business which will in turn allow them to deliver dramatically better business results than when utilising traditional business intelligence (BI) systems.


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Filed under Data Mashup, QuestBI

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