Differentiation in business intelligence
Parts of the business intelligence differentiation story resemble the one I just posted for data management. After all:
- Both kinds of products query and aggregate data.
- Both are offered by big “enterprise standard” behemoth companies and also by younger, nimbler specialists.
- You really, really, really don’t want your customer data to leak via a security breach in either kind of product.
That said, insofar as BI’s competitive issues resemble those of DBMS, they are those of DBMS-lite. For example:
- BI is less mission-critical than some other database uses.
- BI has done a lot less than DBMS to deal with multi-structured data.
- Scalability demands on BI are less than those on DBMS — indeed, they’re the ones that are left over after the DBMS has done its data crunching first.
And full-stack analytic systems — perhaps delivered via SaaS (Software as a Service) — can moot the BI/data management distinction anyway.
Of course, there are major differences between how DBMS and BI are differentiated. The biggest are in user experience. I’d say:
- For many people, BI is the user experience over the underlying data store(s).
- Two crucial aspects of user experience are navigational power and speed of response.
- At one extreme, people hated the old green paper reports.
- At the other, BI in the QlikView/Tableau era is one of the few kinds of enterprise software that competes on the basis of being
- This is also somewhat true with respect to snazzy BI demos, such as interactive maps or way-before-their-day touch screens.*
- Features like collaboration and mobile UIs also matter.
- Since BI is commonly adopted via quick departmental projects — at least as the hoped-for first-step of a “land-and-expand” campaign — administrative usability is at a premium as well.
* Computer Pictures and thus Cullinet used a touch screen over 30 years ago. Great demo, but not so useful as an actual product, due to the limitations on data structure.
Where things get tricky is in my category of accuracy. In the early 2000s, I pitched and wrote a white paper arguing that BI helps bring “integrity” to an enterprise in various ways. But I don’t think BI vendors have done a good job of living up to that promise.
- They’ve moved slowly in accuracy-intensive areas such as alerting or predictive modeling.
- “Single source of truth” and similar protestations turned out to be much oversold.
Indeed, it’s tempting to say that business intelligence has been much too stupid. 🙂 I really like some attempts to make BI sharper, e.g. at Rocana or ClearStory, but it remains to be seen whether many customer care about their business intelligence actually being smart.
So how does all this fit into my differentiation taxonomy/framework? Referring liberally to what has already been written above, we get:
- Scope:
- For traditional tabular analysis, BI products compete on a bunch of UI features.
- Non-tabular analysis is much more primitive. Event series interfaces may be the closest thing to an exception.
- Collaboration is in the mix as well.
- Accuracy: I discussed this one above.
- Other trustworthiness:
- Security is a big deal.
- Mission-critical robustness is usually, in truth, just a nice-to-have. But some (self-)important executives may disagree. 🙂
- Speed:
- For some functionality — e.g. cross-database joins — BI tools almost have to rely on their own DBMS-like engines for performance.
- For other it’s more optional. You can do single-RDBMS query straight against the underlying system, or you can pre-position some of the data in memory.
- Please also see the adoption and administration section below.
- User experience: I discussed this one above.
- Adoption and administration:
- When BI is “owned” by a department, especially one that also doesn’t manage the underlying data, set-up and administration need to be super-easy.
- Sometimes, departmental BI is used as an excuse to pressure central IT into making data available.
- Much like analytic DBMS, BI adoption can sometimes be tied to huge first-time-data-warehouse building projects.
- Administration of big enterprise-standard BI is, to re-use a term, much like DBMS-lite.
- Cost: The true cost of BI usage is commonly governed more by the underlying data management (and data acquisition) than by the BI software (and supporting servers) itself. That said:
- BI “hard” costs — licenses, servers, cloud fees, whatever — commonly have to fit into departmental budgets.
- So do BI people costs.
- BI people requirements also often have to fit into departmental skillets.
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