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This is the time of the year for making predictions for the coming year. I guess I am a little late, but given it's still January (just) I guess it is still okay to add my 2 cents.
During 2007 both vendors and industry pundits said that operational BI and getting BI out to the masses were key directions. There was certainly a significant amount of discussion on these two related topics during the year, but did customers actually succeed in making progress here?
To my way of thinking moving BI out to the masses involves making it easier to use. From this perspective I think 2007 was a failure. Yes, products did make significant progress in supporting Microsoft Office, but is this the really a good measure of usability? Are less experienced users really major users of Microsoft Excel? I think not.
There are two models in the market. One is the IBM and Microsoft model, where the emphasis on product functionality and thus complexity. The other is the Google and Apple model where the focus is on usability. Apple Mac Leopard blows Microsoft Vista out of the water in terms of usability, for example. The IBM and Microsoft model is important and is likely to be the cornerstone of IT systems and infrastructure for many years to come, but we need to find more user-friendly solutions.
For less experienced users the way to go is the Google and Apple model. This may provide be less functionality and less stability from an IT perspective, but end user acceptance and growth is likely to benefit most from this model.
The issue for vendors and IT is how to marry the two models. Most BI vendors are still committed to the IBM and Microsoft model, and this opens the door to innovative new BI vendors and I believe in some cases open source products. Many software-as-a-service (really applications-as-a-service) analytical solutions are also starting to gain traction because they offer user-friendly options at a reasonable cost.
I think some of the mainstream BI vendors (e.g., Actuate) are beginning to realize that piling more function into a tired old architecture is not the way forward. Hopefully other vendors will soon realize this as well. However, the size of many BI vendors and the number of products they have is working against fast and easy.
My focus for 2008 then is on BI usability. I think the products that can provide this will be the winners.
We're only 16 days into the new year and already two important acquisitions have occurred: Oracle acquired BEA for $7.85 billion and Sun acquired MySQL for $1 billion.
The Oracle acquisition of BEA is no surprise. The battle for the infrastructure market is now clearly between Oracle and IBM, with Microsoft, SAP and Sun watching from the sidelines. One interesting aspect of the BEA acquisition is that Oracle now has four portal products. I am glad I'm not an Oracle salesperson!
Sun's proposed acquisition of open source database vendor MySQL was more of a surprise. To date, Sun has not been a database player. MySQL is the most popular open source database products on the market, and it is used by several major Web players including Google and Facebook. MySQL claims that 100 million copies of the product has been downloaded and that an additional 50,000 copies are downloaded daily.
The risk for any open source user is that the software can be acquired by a commercial company. The open source license usually ensures that the product source code remains freely available up to the development level at the time of acquisition. Sun says they will continue to develop the product on multiple platforms including Linux, Mac OS X, Microsoft Windows, and OpenSolaris. Given Sun's commitment to open source (e.g., OpenSolaris, Java System Portal Server/OpenPortal, Glassfish application server) there is no reason to disbelieve them.
One interesting aspect of the MySQL acquisition is that Oracle owns InnoDB, which is a storage engine for MySQL. InnoDB is not a standalone product: it is distributed with MySQL. InnoDB has a contractual relationship with MySQL. It will be interesting to see what happens here.
Data warehouse appliances are so-called because they are used for storing and managing the data associated with data warehousing projects. Strictly speaking though these products should be called database appliances, or simply data appliances, because they support only the database processing component of a data warehousing and business intelligence environment. Many of these appliances are missing the data integration software required to capture data from operational systems, and transform and consolidate it into a data warehouse. Given that the task of data integration is a significant percentage of the effort required to build a data warehouse, the cost savings of these appliances for data warehousing projects may not be as high as it first appears.
This point is brought home by the recent announcement by Vertica of a strategic relationship with Talend, an open-source data integration vendor. Vertica markets the column-oriented Vertica Database, and has a relationship with HP and Red Hat to offer a bundled hardware and software analytical database solution. Given that Talend also has relationship with open source BI tool vendor Jaspersoft, it means the combination of Vertica, Talend, JasperSoft and Red Hat software on top of an HP hardware platform provides a cost-effective and open source data warehouse and BI environment. If this package was offered as a single solution, and supported by a single vendor, it would represent the ideal data warehouse and BI appliance solution.
In my opinion, the appliance vendors must move toward offering these types of packaged software environments if they are to survive. Simply offering better price/performance is not a viable long-term strategy. The database machine vendors discovered this. In fact, I believe the vendors need to go one step further and provide application appliances that provide a complete business solution. Although it had a proprietary architecture, the IBM AS/400 was a tremendous success because it offered a complete application solution to business users.
It was just a matter of time before Cognos was acquired, and IBM purchased a BI company. It's a good match for IBM because there is no overlap between the product lines. This would not have been the case if IBM had acquired acquired Business Objects.
There are not many large independents BI vendors left. SAS, Information Builders, and Microstrategy are the main ones. The first two are private companies with CEOs that want to keep it that way. SAS's new relationship with Teradata becomes more important with the IBM acquisition. Information Builders has always been happy to do its own thing and makes a good living out of it.
The acquisition is both good and bad for Microstrategy. It's good because they can say they are one of the few independent vendors left. It's bad because all of the major infrastructure and database vendors now have significant BI and data integration products. This is going to make it tough for Microstrategy in large enterprise accounts, which are its sweet spot.
As I said when SAP acquired Business Objects, for smaller enterprises and SMB customers, open source BI and new BI vendors with modern technology are becoming increasingly attractive.
Master data is defined as data about the key business entities of an organization. Examples include customer, product, organizational structure, and chart of accounts. A common question about master data is, “What is the difference between master data and reference data?” Some people take the position that they are the same thing, but it can be argued that not all reference data is master data. For example, lookup and code tables that are used to encode information, such as state names and order codes, are not strictly master data tables. The diving line between master data and reference data is not always clear cut. One solution is to break master data into two types: master reference data and master business entity data. Master reference data has well defined and simple data structures, has simple keys and governance rules, is often standardized (US state codes, for example), involves only a few applications, and is reasonably stable. Master business entity data, such as customer, on the other hand, is usually ill-defined, has complex data structures and relationships, requires compound and intelligent keys and complex governance rules, is not usually standardized, involves many business processes, and changes frequently. Does this distinction really matter? When developing data quality management and master data management systems it can do. Cleaning and managing master reference status is a reasonable easy job. The opposite is true for master business entity data.
Any comments?
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