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Ronald Damhof

I have been a BI/DW practitioner for more than 15 years. In the last few years, I have become increasingly annoyed - even frustrated - by the lack of (scientific) rigor in the field of data warehousing and business intelligence. It is not uncommon for the knowledge worker to be disillusioned by the promise of business intelligence and data warehousing because vendors and consulting organizations create their "own" frameworks, definitions, super-duper tools etc.

What the field needs is more connectedness (grounding and objectivity) to the scientific community. The scientific community needs to realize the importance of increasing their level of relevance to the practice of technology.

For the next few years, I have decided to attempt to build a solid bridge between science and technology practitioners. As a dissertation student at the University of Groningen in the Netherlands, I hope to discover ways to accomplish this. With this blog I hope to share some of the things I learn in my search and begin discussions on this topic within the international community.

Your feedback is important to me. Please let me know what you think. My email address is Ronald.damhof@prudenza.nl.

About the author >

Ronald Damhof is an information management practitioner with more than 15 years of international experience in the field.

His areas of focus include:

  1. Data management, including data quality, data governance and data warehousing;
  2. Enterprise architectural principles;
  3. Exploiting data to its maximum potential for decision support.
Ronald is an Information Quality Certified Professional (International Association for Information and Data Quality one of the first 20 to pass this prestigious exam), Certified Data Vault Grandmaster (only person in the world to have this level of certification), and a Certified Scrum Master. He is a strong advocate of agile and lean principles and practices (e.g., Scrum). You can reach him at +31 6 269 671 84, through his website at http://www.prudenza.nl/ or via email at ronald.damhof@prudenza.nl.

Business Intelligence vendors seem to embrace collaboration (I am still struggling whether this software is any different from the groupware we had in the 90's) . As an example please take a look at SAP streamwork at youtube. I am gonna be blunt here; this type of software is completely useless, unless the organization is willing to fundamentally change its decision making process.

Let me try to make my point here with the help of giants like Galbraith, Daft, Davenport and some..

There are basically two information contingencies; Uncertainty and Equivocality.
  • Uncertainty can be defined as the absence of information (e.g. Shannon and weaver) and can be overcome by simply asking the right question. The answer is out there.....
  • Equivocality is an ambiguity, the existence of multiple and conflicting interpretations about an organizational situation. Participants are not even sure about the questions that need to be asked, let alone the answers they need. I think this can also be regarded as 'wicked problems'.
Now, for overcoming uncertainty you can suffice with relatively blunt instruments. Reporting and the ever increasing possibilities in analytics really shine in reducing uncertainty.

Now, for overcoming Equivocality the Business Intelligence stuff like reporting and even analytics have diminishing usage. You need more 'richness' in the tooling. And with tooling I don't necessarily mean software. Examples of more rich tooling are group meetings, discussions, planning, creative (group) thinking, etc..Simply put; you need face-to-face contact.
Davenport wrote an article about 'Make Better Decisions' in the Harvard Business Review in 2009. He is advocating a more formalized approach towards decision making:

'Smart organizations can help their managers improve decision making in four steps: by identifying and prioritizing the decisions that must be made; examining the factors involved in each; designing roles, processes, systems, and behavior to improve decisions; and institutionalizing the new approach through training, refined data analysis, and outcome assessment.'

Davenport, in my opinion, is aiming towards the equivocality and a more formalized method of coming to an outcome. And frankly, I like it a lot. But organizations need to really be willing to change its decision making process. And this is a major organizational and cultural change in my opinion. If organizations are really committed (Davenport is naming a few of those companies - like Chevron, The Stanley Works) in making this change, collaboration software has the potential to shine in supporting such a decision making process.

I am however afraid that collaboration software from BI vendors will be sold as candy with the promise of better decisions. And that is just bullshit and my prediction is that it will fail big time. 

Posted March 30, 2010 12:17 PM
Permalink | 4 Comments |


I like the Davenport quote and the idea of institutionalizing a decision making approach, but surely if you are going to define a decisioning methodology why not go all the way and link 'roles processes and systems' in such a way that you can then automate the decision making. Until you can empirically test the products of the methodology it can only be of very limited value. On the other hand, provably correct models of institutional decision-making lead directly to automation. Decision automation provides substantial gains in development cost, time, and risk mitigation, and leads to improvements in business efficiency, agility, and strategic alignment. This paper has more details http://idiomsoftware.com/pdfs/IDIOM%20Decisioning.pdf.


Thx for your response!

I have read your pdf with great interest. Makes me (again) rethink traditional thinking. And that is good. Decisioning as you call it seems to resemble the ideas of James Taylor's Decision Management. Am I correct in this assumption?

Automating decisions in my opinion is extremely valuable in the operational environment, especially when the informational contingency is 'uncertainty' (defined in my post) and you can model/automate the process that leads to a desired outcome. Interesting enough McKinsey published an interview with Daniel Kahneman and Gary Klein (http://www.mckinseyquarterly.com/Strategy/Strategic_decisions_When_can_you_trust_your_gut_2557) that seem to acknowledge some of these findings.

But what if the needed decision is based on equivocality? A wicked problem....Highly uncertain and multiple ambiguous directions. For these problems (probably more strategic in nature - but with a large economical impact) I doubt whether the concept of decisioning - supported by software - is a good fit. I think you can manage the process of these decisions (like Davenport is saying), but the actual decision is made with very 'rich' communication media like face-to-face cntact, group meetings etc..

Forgot the conclusion; decisioning has the potential to really shine in more operational oriented environments where uncertainty is the information contingency.

Collaboration has the potential to shine in more strategic, highly uncertain and ambiguous environments where Equivocality is the information contingency.

The challenge however with collaboration software is that it needs a more 'decision-aware' organization. Otherwise, Collaboration software will fail.

Ronald, Yes, you are correct that James Taylor's and IDIOM's viewpoints are well aligned. James tend to put additional focus on analytics, whereas we focus more on operational decision making - that is, the specification of decision making within processes. I find your discussion very relevant to these viewpoints. From my perspective there is a continuum from equivocality (left hand side) through to uncertainty (and then certainty) (Right hand side). IDIOM's decisioning is at the right. Analytics is more relevant at the transition between equivocality and uncertainty. And you propositins of collaboration and intuition are at the equivocality end of the continuum. One of the benefits of the decisioning approach is that it can move the whole spectrum to the left. An an organization learns, it captures that learning into decision models, and deploys them into its operational processing, thereby linking the continuum directly to process. The act of capturing decision models transitions uncertainty into certainty, and the organization then focuses on the next layer of uncertainty. The cycle continues, moving the entire line to the left. In IDIOM's world view, strategy, policy, domain knowledge, decision models, and eventually processes are the practical manifestations of the continuum. The equivocality end of the sepctrum is addressed at the strategy level. Determining the relevant questions for THIS organization is a key purpose of strateg, and the results of this deliberation become objectives, KPI's, etc.. Applying the organization's domain knowledge to this strategy defined perspective results in 'policy'. Policy is then applied trhough decision models, and processes are built to support them- and so the cycle is applied to the day-to-day activities of the business. Best regards,

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