What we all knew was true, but could not get across to management, is now more scientifically proven. The decision process regarding the outsourcing of a DSS is influenced by significantly other characteristics, when compared to OLTP. If you are interested in the details, the theory and the underlying data, please read:

Factors considered when outsourcing an IS system:an empirical examination of the impacts of organizations size, Strategy and the object of a decision (DSS or OLTP).

B.Berg and A.Stylianou in the European Journal of Information systems (2009 18, 235-248)

I still encounter organizations who are stuck in the OLTP world, even when the object of decision regarding outsourcing is completely different on many dimensions. They tend to use the same decision process regarding the outsourcing as they always did...whether they outsource an ERP, a CRM system a data warehouse or a more elaborate BI system.

Lately I come across vendors of OLTP systems that engage highly in tied selling. Now - if there is complete transparency to the customer- this is off course no problem. But, the sad thing is, this transparency is usually extremely bad or even non existent. 

This tied selling combined with (complete) lack of transparency has caused severe damage to the economy as we have seen especially in the financial sector. In Europe the European Commission is highly active in forcing vendors to stop tied selling (for example Internet Explorer and the OS of Microsoft). Vendors receive huge fines if they engage in these practices.

Unfortunately these practices are also quite mainstream in the data exploitation industry.

Yep - bringing up an oldie, but still o so true.

Financial assets: A single Euro can only be spend once.....

Human captial: They can just walk out of the door and leave or be hunted by your competitor

Not so with your data; It is uniquely yours, closely connected to your business language, full of nuance, in the proper context and the ability to procreate new data. Every organization can use it's data differently to create it's own niche. When data is used by anyone in your organization, it will not be consumed and it will not expire (a patent will). There is simply no other asset that anyone can think one that owns these capabilities.

To make the case even stronger; the movement of data in your organization is your lifeblood. Any company will simply seize to exist if this data is of bad quality. Orders will not get through, customers credit information can not be seen and invoices can not be send.

Another fascinating attribute of data is that it defines the other assets; financial data, property data, employee data. Badly managed data will jeopardize the management of the other assets big time. So, it's kind of a 'super', ultimate asset.

Nicholas Carr wrote in 2003 in the Harvard Business Review 'IT doesn't matter'. Systems, infrastructure, services, hardware, software, ERP...whatever...they will and probably are already commodities. They are not a differentiating factor in competitive advantage, sustainability or reliability.  Data is however always unique, competitors can not get hold of it (well....unless you got a security problem) and if they got it, it will probably be hard to use, since data is often very closely linked to the organizational lingua franca.

Data must be nurtured, managed aggressively and - most of all- must be enabled to be exploited to the max in order to get a good return.It is by far the ultimate proprietary asset.

I believe that in business nowadays huge amounts of money are spend on very sophisticated technology, financial controls and process management  (the process taliban). The marginal returns from ever increasing investments into these developments will be small. Investments in data are quite the opposite. The opportunities are limitless. And, more importantly, any investment you make is proprietary to your organization. This is the true definition of competitive advantage. 

This posted is inspired and sometimes quoted by T.Redman and his book 'Data Driven'

July 2007 I had a first aquantaince with Dan Linstedt - via LinkedIn.
I worked for a large government organisation as the architect for Data
Warehousing and Business Intelligence. I seriously considered Data
Vault as the standard methodology for the Enterprise Data Warehouse. I
worked for over 2 years for this govenment organisation and my choice
for the Data Vault is still a very good one.

November 2007 I got
Dan Linstedt to the Netherlands and got the first 27 peeps trained and
certified of that same government organisation. I had dozens of
discussions with architects, management, developers, etc...but in the
end the consensus on choosing Data Vault was huge.

Since november
2007 Data Vault sky rocketed in the Netherlands. Together with the
Genesee Academy and DNV I think we certified over 200 people and I dare
to say that most new EDW projects nowadays in the Netherlands have
chosen Data Vault as the prime methodology.

Before I get
criticised for sanctifying one methodology despite the various (non)
functional requirements; I do believe that ANY architecture needs to be
in line with the business-, application- and technical architecture. So
choosing one methodology on forehand is just plain wrong. There has to
be sound justification and alignment for any choice in your
architecture. Whether it's a tool, a methodology or whatever.

Since
2 years we (Genesee Academy, me, DNV) certified over 200 people - most
of them extremely seasoned and respected experts in our field. On
average these consultants are very critical on any new 'trick in the
book'. I dare to say that most of the experts that were certified were
convinced that Data Vault has got extreme value and potential. The
proof of the pudding is in the eating; Data Vault got many
implementations in the Netherlands the last two years and it's growing
fast.

I am confident that Data Vault in the years to come will hit Europe as hard as it has hit the Netherlands.

But
I wonder......I and am still baffled by it. The attention for Data
Vault in the United States is extremely limited, why is that? Even a
respected institute as TWDI is not paying much attention. Why? Experts
in the field do not mention Data Vault what so ever. I just don't get
it. Can anybody enlighten me?

For those of you not familiar with Data Vault --> read my articles.

In the 'Neverland' of decision support, Business Intelligence has completely taken over all attractions.

Let's name a few; BI, Neuro BI, Ambient BI, Sentient BI, Process driven BI, process Intelligence, Pervasive BI, BI 2.0, BI as a Service, BI for SOA, SOA for BI, Mobile BI, google BI, Personal BI, BI-Tools-where-you-do-not-need-a-DWH-and-ETL-Buy-it-now, Mashups-the-new-BI-Desktop (oh my god), BI-in-the-box,Business Analytics,BI-in-the-cloud (love this one!), BI-virtualization, Agile-BI,Operational-BI,Decision Intelligence, Decision Management, Even driven analytics, Complex Event Processing, BAM, Collabarative Decision Making......

Do not get me wrong; I certainly do not dismiss them all.....just most of them :)

Most of these 'attractions' are highly sponsored, look super-dooper on the outside and if you won't sit in them you will loose out big time (so they say). However, when you finally decide to ride them you feel that they are not that stable, not really safe and there is hardly any enjoyment when you exit them (but you can tell you neighbour you dared to ride it!). To put it in other words, not really grounded in theory and the relevance for practice is extermely hard to find.

There are however some attractions in this Decision Support 'Neverland' that are very much dusty, spiderwebs all over the place, but the attraction is still extremely solid and if we would overhaul it with new architectural insights and technology, it could be a smash-hit. These attractions are named DSS, EIS, ESS.... 

I feel that we - as an industry - failed miserably in continuing on the path that was made for us by people like John Dearden, John Rockart, David Delong, Ralph Sprague, Hugh Watson, Steven Alter, Daft and Lengel, Peter Keen, Michael Scott Morton, Herbert Simon, Henry Mintzberg and many more.

Dan Power is one of those brave souls who is standing with the ticketbox - selling tickets for his 'attraction'. I recommend people to read his last written article as well as the blog post written by Wouter van Aerle.

Finally, I wanna contribute to the Decision Support 'Neverland': BI goes Retro

In 1986 Daft and Lengel published an article in Management Science:

ORGANIZATIONAL INFORMATION REQUIREMENTS, MEDIA RICHNESS AND STRUCTURAL DESIGN.

A very interesting article that combines the processing of information by decision makers, the use of structural mechanisms and organizational design. The latter one - organizational design - I will not discuss thoroughly in this blog. Just remember that the choice of structural mechanism to overcome uncertainty/equivocality will impact the organizational design.

It is only by means of good and respectfull discussion that knowledge
and insight will evolve. This post should be regarded as such.

This post is a second reaction to the first article in a series of three
which were written by a highly respectfull thoughtleader in the field
and publisher on the B-Eye-Network; Rick van der Lans. The papers are
titled 'The Flaws of the Classic Data Warehouse Architecture'.

This blog post is a reaction to the first part. It deals with the flaws of the classic data warehouse architecture (CDWA).

Rick signals five flaws which will lead in article two and three to a new architecture. This post is addressing the second flaw.

- My reaction to flaw #1 can be read here
- My reaction to flaw #2 can be read here

Flaw 3 according to Rick
Rick signals the need to do analytics on external data and on datastores that are unstructured. I quote Rick: 'Most vendors and analysts propose to handle these two forms of data as follows: if you want to analyze external or unstructured data, copy it into the CDW to make it availablefor analytics and reporting'. Ricks is wondering why? Unstructured data can be 'handled' on the source and external data can be done by mashup tools.

My reaction to flaw 3
Where are these vendors and analysts that propose to copy unstructured data into the CDW? I do not know them....really I don't. And if they exist - I agree with Rick; don't do it. Especially for the unstructured data I think other architectural choices are more optimal at the moment. But where is the flaw in the CDWA architecture? The CDWA was not meant for unstructured data and still is not. I still do not see the flaw...

But for the external data, I really believe in the years to come that there is still a solid business case of getting this data into your data warehouse. Sure enough - especially for more situational BI - mashups offer very fast time to market for new informational products. Although I believe the securtity issue is not to be underestimated as well as the need to perform analytics on the combinations of internal and - multiple sources of - external data.  

Mashups also need solid architectures.......

So:
- I challenge the notion that the vendors and analysts in data warehousing massively propose to put unstructured data in the DWH. The CDWA was not meant for that purpose. Not a flaw.
- There are solid business cases for getting your external data into your DWH. The CDWA is still a valid approach. Not a flaw.
- Mashups - as is new (BI) technology in general - surely offer new features and promising functionality.

The above quotes I got from one paper in one paragraph in Decision Support Systems (paper is from 2008 - very recent), which is an important journal in our field of expertise. Since I encounter these statements over and over again, I decided to follow up on the citations.

Let me begin by dismissing the quote refering to Gartner's press release. That's not really a sound scientific basis.....So we are left with two more quotes.

[43] C.Hall Corporate use of data warehousing and Enteprise Analytic Technologies, Arlington, Massachusets, 2003 URL: www.cutter.com
Ronald: can not validate the information. You need to buy a report......

[63] S.Kotler When enterprise hit open road: move beyond the silos and let the idead roll, Teradata Magazine 3 (3) 2003
Ronald: I read this article...now it becomes shocking. Let me quote this article:

Ok....this citation is actually referencing the first one [43] (I think...can't access it). We got a loop here....

[80] M.Quaddus, A.Intrapairot, Management Policies and the diffusion of DWH: a case study using dynamic based decision support systems, Decision Support Systems 31 (2) 2001, 223-240
Ronald: ok, this is becoming complex. I quote this paper:

So a citation is used in a paper that refers to a claim in another paper but with different citations (you guys still with me here?). So we end up with three more citations which are off course even further in time, let's examine these:

[24] R. Hackathorn, Data warehousing energises your enterprise, Datamation 41 (2) 1995. 38-42
- I could not find this article, so I was not able to validate the claim that was being made. It sounds to me like some sort of column though. But again - can not validate.

[25] C. Horrock, Making the Warehouse Work, 1996, available from http://www.computerworld.comrsear . . . -htm1r9606r960624DW1SL96dw10.html.
- I could not find this article, so I was not able to validate the claim that was being made.

[67] The Siam Commercial Bank's Staff, Data Warehouse Questionnaire and Interview, (6 January-28 February 1998). Personal Communication.
- I could not find this 'article', so I was not able to validate the claim that was being made.

To summarize things; I was not able to establish any (empirical) evidence that would support the claim made in the paper of DSS (and in several other papers as well) - which is by the way quite a recent paper (2008). Somehow we are being made to believe that Data Warehousing are failure prone. Increasingly I encounter consultants and analysts that fuel this negative sentiment surrounding data warehousing. I challenge anyone to deliver some real (empirical) evidence. As for now - I suggest we all use caution in communicating that Data Warehouse undertakings tend to fail a lot.

On the subject, there is one interesting piece of paper from TDWI, written by Hugh J.Watson (I think in 2006) that seems to be relevant and hitting the nail on the head by saying - and I quote:

It is only by means of good and respectfull discussion that knowledge and insight will evolve. This post should be regarded as such.

This post is a second reaction to the first article in a series of three which were written by a highly respectfull thoughtleader in the field and publisher on the B-Eye-Network; Rick van der Lans. The papers are titled 'The Flaws of the Classic Data Warehouse Architecture'.

This blog post is a reaction to the first part. It deals with the flaws of the classic data warehouse architecture (CDWA).

Rick signals five flaws which will lead in article two and three to a new architecture. This post is addressing the second flaw.

- My reaction to flaw #1 can be read here.

Flaw 2 according to Rick
The CDWA stores a lot of redundant data. The more redundant the data, the less flexible the architecture is. We could simplify our data warehouse architectures considerably by getting rid of most of the redundant data. Hopefuly, the new database technology on the market, such as data warehouse appliances and column-based database technologies, will decrease the need to store so much redundant data. Rick commented on this flaw in his closing keynote statement on a BI event we had last week, stating basically that the DWH professional did an extremely lousy job last decades in building these redundancy monsters. Like in his article he strengthened this argument by research done by Nigel Pendse claiming that the average BI application only needed a fraction of the stored (redundant) data. 

My reaction to flaw 2
First of all, I agree that new technologies can limit the volume of redundant data considerably.

But to say that in the last decades the data warehouse professional did an etremely lousy job because of the huge redundancy they created in their data warehouses...well, that's just plain stupid and for the people that are applauding this statement I would like to say; 'I bet you never actually build a data warehouse'.

BI populism.....thats what it is.

As for the flexibility argument; more redundant data kills flexibility. Hmm...it's a bit of a bs-argument. Because flexibility is not only affected by redundant data. If I had build my data warehouses in the last decades without redundant data I would have ended up with huge complex transformation rules and a big strain on processing capacity. Both issues woud have killed the flexibility big time and I am leaving aside the degradation of performance, degradation in ease of use, degradation in maintainability and the degradation of the testability of the system. But I agree - I would not have redundant data...I would not have any quality of service either....but who cares.

BI populism.....thats what it is.

But is the CDWA architecture flawed by this redundancy problem? I do not think so at all. We would still need a datastore of some kind (Rick seems to acknowledge that by advocating the use of appliances), we would still have several layers after this datastore, preparing the data for several different functionalities (reporting, mining, advanced analytics, datasharing to third parties, etc.). Let's take the datamart layer, will it dissapear? I don't think so. The question is whether it needs to be materialized. And that's where new technology will be extremely valuable. It seems that Rick is translating the word 'Architecture' with 'Technical Architectue' as a 1:1 relationship.

The hub-spoke architecture of the CDWA model is still extremely valid. Off course, technology within this architecture will evolve and will enable us to deliver an even better quality of service.