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.

I have just read a very intriquing paper called 'A Common Approach for OLTP and OLAP using an In-memory Column Database', written by Hasso Plattner. 

It's not a revolutionairy new technical approach for Data Warehousing a Business Intelligence. It's just a series of smaller (mostly technical and some are even quite old) innovations that together could lead to a paradigma shift [1] in the area of Data Warehousing.

This paper is focussing on the transactional world, because that's where the disruption will originate. In short;

• Ever increasing multi-CPU cores
• Growth of main memory
• Column databases for transactions (!)
• Shared Nothing approach
• In-memory access to actual data - historic data on slower devices (or not)
• Zero-update strategies in OLTP (recognizing the imporance of history as well as the importance of parallelism)
• Not in the paper; but I see datamodels for newly build OLTP systems increasingly resembling the datamodels of the HUB in the data warehouse architecture. 

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 aquaintance 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

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.

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.