This entry was held by Mr. Jørgen Steines and represents his view of what is hip in the world of Business Intelligence, or more specifically – the hip technologies, existing as well as emerging ones.
According to Mr. Steines, all companies today have one or two datawarehouses. Terabyte datawarehouses are more the standard than an exception today.
When talking about what is popular in the BI landscape we have to look at what the primary drivers are for the businesses that employ business intelligence systems to improve their profits and competitive abilities.
Driver #1: Business process integration
BI is moving out of the analytic departments into all divisions and operations of businesses and thus needs to become integrated into the existing operational systems so all users can get transparent access to BI information.
Driver #2: Flexibility
Users need to combine data in new ways to get the right context for decision making. Therefore there is a great need for flexibility in the tools that provide BI information.
Driver #3: Real-time data
In many business processes it is required for data to be real-time because critical business decisions must be made on a minute-to-minute basis. Examples of this could be logistic planning processes.
Driver #4: Growing number of users
BI will become pervasive for growing numbers of users and systems.
Driver #5: Growing data volumes
Data volumes are growing and growing and the number of source systems seems to grow as well.
Mr. Steines added another interesting perspective: The hefty consolidation taking place in the BI market with the acquisitions of Business Objects, Hyperion and Cognos being some of the more prominent, could change the BI landscape dramatically. Mr. Steines hopes that SAP will hold true its promise to let BO be an “independent” vendor of BI tools due to its breadth of tools and technologies.
So, according to Mr. Steines, these technologies are the answers to the business requirements:
Hip #1: SOA (Service Oriented Architecture)
Data must be decoupled from the applications and be provided through open data services that can be combined much the same way as LEGO bricks. The layer of services will make it transparent where data comes from whether it is a datawarehouse, ODS, Master Data or even data hosted at partners.
The SOA angle is the approach preferred by most of the companies that have acquired BI companies with products that now need to be integrated into complete product suites.
Hip #2: EII (
Much like traditional DW architecture but the main difference is that the common integrated data model is a virtual view of the data sources. Sources are mapped to common views and then dynamically extracted to applications, reports, portals, message queues etc.
The concept was made popular by IBM’s Web Sphere Federator. The key aspect of EII from a BI perspective is the ability to join data from the data sources. (Now, this has a remarkable resemblance with the concept of “Virtual datawarehouse” developed by no less than Acinta, doesn’t it?). To keep up performance an EII system must employ advanced caching mechanisms provided by specaialized services in the data sources.
An example of this is to combine data from the datawarehouse with live data from the operational systems.
Hip #3: Datawarehouse appliances
First there was the monolithic mainframes that were used for everything. Later appeared the OLAP dedicated servers in various flavors.
Lately we have seen column-based databases, in-memory databases and now datawarehouse appliances: Everything needed for a datawarehouse built in to one combined product. The operating system, the hardware, disk systems etc. are all built into one box that has very low maintenance costs and increased performance. This is achieved by designing systems for the sole purpose of DW – everything is tuned from the factory and it will not work well with other uses.
Hip #4: In- memory tools
All data in memory – this is especially interesting after the introduction of 64–bit systems that allow for very large memory address spaces.
Currently there are two types of tools with this technology:
- In memory BI applications (e.g. ClikTech)
- Caching of traditional RDBMS (e.g. Oracle Times Ten, SAP BI accelerator)
According to Gartner: “By 2012, 70% of Global 1000 org. will load detailed data into memory as the primary method to optimize BI application performance”
Hip #5: Open source BI tools
The primary drivers for using open source tools would include that they are cheap, may offer special functions/features, are vendor independent, allow access to source code and are mostly standard-based.
Survey: 43% are already using OS. 24% are uses OS BI. 31 consider OS BI.
Hip #6: Mash-up technologies
Mashups started in the music industry by sampling parts of different songs and combine it into new songs. The idea is the same for IT: Various services are mixed into dedicated applications. E.g. combine BBC news with a Google map to see where the news event took place.
In BI this translates to integrating internal and external data sources. But what is the difference between Mashup and SOA? Mainly, SOA is very formalized and contains a large framework of standards, whereas mashups are “rule-less” - there are some basic techniques for connecting things. So they can be made without expert assistance.
Hip #7: BI Search
BI data must be searchable the same way as in Google. Google already provides certain BI features in its OneBox enterprise search product by integrating with the metadata repositories of various BI vendors.
Summing it all up
- Business process integration: Is already happening. Many companies require this by default
- SOA: Interesting but doesn’t solve the data quality problem
- EII: Can be useful in certain scenarios especially when live data is combined with datawarehouse data
- Data Warehouse appliances: Yes, yes, yes!
- In memory tools: Are special purpose tools. Good for some, not for others.
- Open Source BI tools: The question is, will consultants be more expensive when software is free?
- Mashup: It will get inside companies due to flexibility and user-friendliness
- BI search: Try it.
Remember: Don’t try to prohibit emerging technologies. Instead remember that technology is the easy part and should never be the first consideration. For example business requirements and data quality must be considered first.
Question from the audience: “What about security?” It can be applied at multiple levels, for example the database and/or the application interface. BI systems are not special from all other IT systems.
Mr. Steines provided a good overview of what is going on in the BI landscape. However, one of the proposed technologies is questionable in value mostly because it is a fault engineering of a good idea. EII in one of its corner stones builds on the idea of virtually coupling data sources with applications. The primary motivation for doing such a thing would be to keep system complexity down, but this is contradicted by requiring such a system to be able to handle and integrate all sorts of data from all sorts of sources, require source systems to provide special interfaces and services and so forth.
Thus, as a Danish saying has it: “Den person kan med rette kalde sig ekspert, som tager det lette og gør det svært”. (Translation: That person can truly call himself an expert, who can take the simple and make it hard).
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