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Neil Raden

I hope that you will engage with me with your comments as we explore the future of business intelligence (BI), particularly its expanding role in the actual process of making decisions and running an organization. BI is poised for a great leap forward, but that will leave a lot of people and solutions behind so expect a bumpy ride. I also expect there will be a flurry of advice and methodologies for moving BI into a more active role, one that will widen the audience as BI meets more needs. But a lot of that advice will be thin and gratuitous, so hold on while we put it under the microscope. You can reach me directly if you prefer at nraden@hiredbrains.com.

About the author >

Neil Raden is an "industry influencer" – followed by technology providers, consultants and even industry analysts. His skill at devising information assets and decision services from mountains of data is the result of thirty years of intensive work. He is the founder of Hired Brains, a provider of consulting and implementation services in business intelligence and analytics to many Global 2000 companies. He began his career as a casualty actuary with AIG in New York before moving into predictive modeling services, software engineering and consulting, with experience in delivering environments for decision making in fields as diverse as health care to nuclear waste management to cosmetics marketing and many others in between. He is the co-author of the book Smart (Enough) Systems and is widely published in magazines and online media. He can be reached at nraden@hiredbrains.com.

December 2009 Archives

It's pretty clear that the term "OLAP" is losing its mojo. In a series of exchanges on Twitter, it was suggested that pivoting is the same as OLAP. I don't agree. Pivot tables were first introduced by Microsoft in Excel over ten years ago (I'm sure someone else used the term prior to that, probably that guy from IBM who invented Business Intelligence in the fifties) J. Other spreadsheet vendors have followed suit. Oracle introduced PIVOT to their non-standard brand of SQL, SQL*Plus, too, but more on that in a minute.


Essentially, a pivot in a spreadsheet is a little more than an a crosstab - transposing columns and rows, with some smarts added in to figure out the unique elements of each identifying "dimension" and performing some rudimentary calculations such as aggregation. It is essentially a linked report, in that changes to the original data are reflected in the pivot table, and this can go both ways.


OLAP, on the other hand, is quite a bit different. It includes:


Multidimensional model at its core: dimensions, hierarchies and attributes

Pivot: the ability to rearrange the display of same without code or script

Drill: at least to drill into detail and drill back up if not across dimensions

Cross-dimensional calculations


Collapsible browsing

Flexible definitions of time

Sparse matrix

Read &WRITE, though many OLAP tools cannot do the later

Query generation, no coding



This is no the clear definition of pivot tables or "analytics," but Oracle's SQL pivot fails on almost every criterion, especially on no code and fast. That isn't to say it isn't a useful feature in a database, but it's not OLAP and it isn't really pivot, either, which I assume is an interactive process. SQL is not, as far as I'm concerned, interactive.


OLAP as a term may be a little old-fashioned, but it is not the same as pivot tables. However, pivot tables do seem to be on a path to provide all or most of OLAP's functionality.


Interestingly, the energetic wars of ROLAP versus MOLAP of the 90's may have subsided, but OLAP has clearly settled into a cold peace between the two. The true MOLAP's, Essbase. Microsoft, TM1 for example, have settled in for the long haul while the ROLAP's of Microstrategy, SAP Business Objects and Oracle BI have their own adherents and are still viable. Not many people actually use OLAP, perhaps fewer than 10% of the workforce in organizations that own OLAP tools, but it would be reckless to say that OLAP is no longer relevant. If anything, its ascendant thanks to the acquisition of BI vendors by enterprise software companies.


One last thing to consider. Predictive analytics are the topic du jour, but before an analyst builds a predictive model, he/she spends a lot of time profiling the data. A great of this work is done on OLAP tools. In addition, the output from OLAP manipulations is not terribly different from applying models - analyzing data to derive some understanding. And lastly, after predictive models are run, there is a continuing need to examine the results. Fertile ground for OLAP.



Posted December 1, 2009 8:59 PM
Permalink | 3 Comments |

I enjoyed reading Nenshad Bardoliwalla's blog The Top 10 Trends for 2010 in Analytics, Business Intelligence, and Performance Management. I have to agree with most of the points, but I am a little skeptical of a few of them. For instance, 2010 is only a few weeks away. Some things here feel like they are, at best, 3-5 years away. Developments aren't like a tsunami that happens all at once. We may already see evidence for some of these things, but when will they reach fluorescence? At what point are they a "trend?" For example:


We will witness the emergence of packaged strategy-driven execution applications


Is an emergence a trend? Nevertheless, how different is that from packaged analytical apps? Strategic planning is still an oxymoron in most organizations. Granted, some people may have expressed a strategy, and its circulated in beautiful PowerPoint slides, but the nitty-gritty of putting numbers to a strategy is still a joke, an agonizing iteration of best guess forecasts combined with mandated goals. I'm not sure how this can connect to anything. So to imply that we are on the cusp of a smooth strategy-to-execution through packaged software products is, at best, a little optimistic. The following quote appeared in Harvard Business Review in an article entitled, "Who Needs Budgets".  It reinforces the need for fundamental changes to planning and budgeting processes to address business challenges. So long as the budget dominates business planning, a self motivated workforce is a fantasy, however many cutting edge techniques a company embraces.


Nenshad goes on to cite as an example that Oracle's Fusion technology "clearly portend(s) the increasing fusion of analytic and transactional capability in the context of business processes and this will only increase." There has been a lot of portent in this area for years, but I still can't see that 2010 is the year it will become a trend.



The holy grail of the predictive, real-time enterprise will start to deliver on its promises


Again, is a "start" a trend? I don't think so. Besides, predictive real-time organizations already exist, and have for some time, particularly in financial services and customer service applications. Business rules engines have been around for more than a decade and are usually primed with scored data from predictive models, but this is a niche. It represents a tiny proportion of operational decisions in an enterprise.


There is also danger in predictive models. Suppose a PM indicates that only the top 20% of your customers are profitable and the rest lose money. The "real-time enterprise" might close the accounts of the 80%. Suppose, however, that the PM was unable to understand WHY they were unprofitable, but it turned out to be excessive waste and poor quality caused by you and customer profitability was incorrectly measured? Quantitative methods are only as good as the data, methodologies employed and skill of the modelers. You'd have to be crazy to run your company on algorithms.


There is a lot of talk about CEP, but keep in mind that the domain of CEP is exceedingly narrow and driven by the discernible and codified rules that drive it. It doesn't run a company, just a few decisions. Likewise, in decision management, which I wrote about in Smart (Enough) Systems with James Taylor, we were extremely careful to point out that decision management as a technique only applied to a very small subset of operational decision types, that's why we called smart "enough." Though lots of small decisions add up, making some mistakes are acceptable, such as denying credit to an otherwise creditworthy consumer. But in those cases where even a single mistake can have severe consequences, decision automation approaches, whether decision management, CEP or other point solutions are clearly not acceptable. There are no solutions yet for "sensing and responding" approaches for those kinds of decisions.


This leads me to the next point. I believe that the distinction between exploratory BI (OLAP, reporting, visualization, etc.) and predictive analysis is rather artificial. To the greatest extent, they are used to understand things, not predict them. The predictive process is a very small part of the use of statistics in businesses. At the end of a statistical model is often the same process of BI - discussing the findings and deciding what to do. They just represent different methods. The exception is scoring models, a pretty widely used approach where large volumes of data are scored by a model such as a neural net and programmatic decisions are made without humans, such as mailing lists, next-best-offer, etc. But it's a real stretch to characterize this as a predictive, real-time enterprise.


SaaS / Cloud BI Tools will steal significant revenue from on-premise vendors but also fight for limited oxygen amongst themselves.


This already a trend. Well, maybe not if you use the word "significant." It is not clear to me that large enterprises are about to adopt SaaS / Cloud BI. Customers of Salesforce.com are way ahead in this regard, but only for applications derived of Salesforce.com data which is already in the cloud, so to speak. It's also not clear how the SMB's, whatever they are, are going to adopt this. Sure, cost is a major issue, but so is staff, attention, priorities, etc.


Open Source offerings will continue to make in-roads against on-premise offerings.


That's like saying I'll cut some calories from my diet or I'll work more diligently at blogging. How many? How much? You can't deny the claim, the question is, how significant is it?


So, with these small exceptions, I am agreement about these 10 points.

Posted December 1, 2009 3:05 PM
Permalink | 2 Comments |