We all struggle with complexity of designing, building and maintaining BI apps. Why? Among many other reasons, the simplest one is that there's just too many components involved. Just to name a few
Data modeling (star schemas, cubes)
Delivery (portals, schedulers, emails, etc)
For years there were many attempts to automate some of these steps via metadata. So rather than than coding source to target SQL transformations or DDL for DW generation vendors came up with, what I know call "1st generation" metadata driven BI tools, such as
ETL tools where metadata auto-generated SQL scripts for data extraction, loading and transformation
BI tools where metadata auto-generated SQL for queries
Data modeling tools where metadata auto-generated logical data models and DDL for physical data models
But, the "2nd generation" metadata driven BI apps (note apps vs tools now) do much more. For example, they:
Use metadata to generate multi vendor apps (like BalancedInsight, Kalido and BIReady do), and having a single place where changes can be made
Use metadata to generate all three (ETL SQL, BI SQL, DW DDL, like Cognos, Wherescape, BIReady do), and having a single place where changes to all 3 can be made
Using metadata to generate report layouts (like Cognos does)
Decisions are a very human investment of attention to a problem, and gut feel--the stream of intuition, impulse, memory, and emotion behind all behavior--is the impetus driving every decision that people make
When a user of a BI application complains about the application not being useful - something that I hear way too often - what does that really mean? I can count at least 11 possible meanings, and potential reasons:
1. The data is not there, because
It's not in any operational sources, in which case the organization needs to implement a new app, a new process or get that data from an outside source
It is in an operational source, but not accessible via the BI application.
The data is there, but
2. It's not usable as is, because
There are no common definitions, common metadata
The data is of poor quality
The data model is wrong, or out of date
3. I can't find it, because I
Can't find the right report
Can't find the right metadata
Can't find the data
I don't have access rights to the data I am looking for
4. I don't know how to use my application, because I
Was not trained
Was trained, but the application is not intuitive, user friendly enough
5. I can't/don'thave time do it myself - because I just need to run my business, not do BI !!! - and
My colleague, Holger Kisker, just posted a very insightful blog on the convergence of BI and BPM technologies. Yes, Holger, BPM vendors definitely have some BI capabilities. And so do some search vendors like Attivio, Endeca and Microsoft FAST Search. And so do some middleware vendors like TIBCO, Vitria and Software AG. And so do rules vendors like FairIsaac, PegaSystems. Should I go on? I have a list of hundreds of vendors that "say" they are a BI vendor.
But it’s not that simple. First of all, let’s define BI. In the last BI Wave we defined BI as “a set of methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information used to enable more effective strategic, tactical, and operational insights and decision-making”. To provide all these capabilities a vendor should have most of the necessary components such as data integration, data quality, master data management, metadata management, data warehousing, OLAP, reporting, querying, dashboarding, portal, and many, many others. In this broader sense only full BI stack vendors such as IBM, Oracle, SAP, Microsoft, SAS, TIBCO and Information Builders qualify.
Even if we define BI more narrowly as the reporting and analytics layer of the broader BI stack, we still want to include capabilities such as 11 ones we use to rate BI vendors in the BI Waves:
Here now is the broader conceptual model that I promised in the prior blog post. As I said, I built conceptual hooks in my decision support ROI model to address broader requirements for decision automation and decision management.
I’m developing a return on investment (ROI) calculator for data warehousing (DW) appliances, using the Forrester Total Economic Impact methodology.
At the heart of that is a conceptual ROI model that can be applied to any decision support infrastructure, not just DW appliances (though indeed high-quality decision support is the raison d’etre for DW appliances).
That said, and not wanting to bog down forthcoming syndicated TEI study with a lot of this conceptual material, here are the core principles of this conceptual model , plus a discussion of how, net-net, they map to the key benefits of a DW appliance: