The term SOA is introduced already many years ago by Roy Schulte and Yefim Natis - also from Gartner. And even at the time of the introduction of SOA one could start a dispute about the differences between SOA and CBD – Component Based Development. And there are similarities between that dispute and the dispute between WOA and SOA. My take on it is that already from the beginning of ICT or software engineering one is trying to conquer complexity and still remain the benefits of flexibility or agility. The more the technology becomes mature, the more we are able to tackle this paradox.

David Cearley’s take on this strategic technology is that the coming 5 years the standards and technologies still significantly. That web-based solutions will improve that much that global-class solutions that can fulfill enterprise computing needs – the web as the ultimo service oriented architecture. In the data value space we see already an increasing demand and supply of web-based solutions that seamlessly fit in such service oriented solutions – think about all the Cloud and SaaS initiatives. Technologies like – and the global adoption of – SOAP, REST and AJAX definitely gave the right boost to transform SOA into WOA.

Nice sidetrack on SOA vs WOA is that in particular The Netherlands WOA is not a disease anymore (Dutch collective noun for all Sexual Transmitted Diseases). In the past analyst misinterpreted the relevance of Service Oriented Architecture per country on the hits they got on Google, because of that The Netherlands was longtime seen as leading country on Service Oriented Architecture. With WOA that miscalculation can be solved immediately. Value of data – always fun.

Number 6 in Gartner’s top 10 Strategic Technologies for 2009 is all about Social software and networks. Their focus is mainly on the benefits of using the growing virtual social networks for commercial reasons. For Data Value I see 2 observations:

1. The data value within and accross social networks. Within because it becomes harder and harder to find the right person, e.g. LinkedIn and search on Henk de Ruiter you will see actual multiple entries for the same person, and … no wonder you will find actually the same person via the ID Henk deRuiter. The cause for these duplications are as for any other datasource. Users (the actual individuals) didn’t know how to use the system (e.g., make a new ID per company that you worked for) or the systems cannot handle the linguistical and cultural challenges of the world. Since a couple of years we see more and more of these networks in coopetition. Where the networks ‘smartly’ use the ID – firstname + lastname – to collect personal and network information. This is not always correct. The challenge is even more in the synchronization, nobody changes things on all networks and everybody trusts that ‘the systems’ will do the right synchronizations. Initiatives of OpenSocial can help in this area.
   
2. The data within the social networks can be used to enrich your view on your contact. Mind that the data in the networks is not always that accurate, but for the most part, and by combining networks via OpenSocial, you can really bring more value in yur contact data – and that’s the value of enrichment. The moment you automatically link your contact data with the social networks it will be your contact that will enrich your data – every time he or she updates one of it’s networks. As that data is used for multiple purposes it will also give you some guarantee on the content as well. Let’s hope that OpenSocial or any other alike standard can circumvent their technical challenges and will be fully adopted in this area.

• Having the right contact details will reduce waste of natural resources because we bring the deliveries immediately at the right place, and it’s not only the deliveries that can be optimized, we can also avoid that deliveries get lost and natural resources are actually piped for /dev/null !
• By valueing our data through deduplication we can in general avoid to spoil needless energy – both by humans and other resources – and use the sparse energy only for those who actually need it. Here I feel the same remark as David in his blog. There comes a moment in the near future, with an rising energy prices and increasing emission penalties, that that aspect will win in the equation from the actual spoil of goods and human energy.
• Saving resources is now also done by concentrating or centralizing services – optimizing the service per energy unit. For data we see this happening in the Virtualization of data amd Master Data Management technologies. Strong place in your centralizing strategy will be the role of your data quality – that will bring your real value

I encourage you all to think out-of-the-box how data-value can help to make it a better world for the future. But I’m afraid that in this economic climate the short term is ruling and not the long(er) term.

No need to convince ourselves that data really brings value in BI! Still we see BI projects struggling with the foundation. The obvious statements as “garbage in is garbage out”, can we really trust the actual figures generated from our BI tool, and did the change management investments on the people providing the data really convert them in ‘data friendly persons’. I still need to smile internally if people complain about the garbage quality of the other departments and are completely convinced of their clean data - challenge them to cleanse and dedup their outlook contacts!

Another pitfall related to data quality is that people trust too often on keys or IDs. I’m sorry but data quality is much more than matching on keys or IDs, even on official keys like social security numbers.

A recent blog from Oracle’s Frank Buytendijk “The Crisis Was Caused By ‘Performance Management’, And Performance Management Will Have To Solve It Too.” brings this whole strategic technology in the right after ‘crisis dip’ perspective. How come that with all these new BI tools we didn’t see a economic crisis popping up in our windscreen, or were we too much focused on the rear mirror. Hope to read soon more from his analysis!

Cloud Computing or Cloud Services that’s the ultimate dream we have. It doesn’t bother anymore where services are running, we need a handle to it and it can start raining. Pending on which part of the world you live, you like clouds or not. For someone with lastname “van Holland” it’s almost hard to believe that he needs to trust the clouds!

Number 3 on Gartner’s list on Top technology strategies for 2009 is dealing about Cloud Computing. And a more detailed blog with the challenging title “Delivering Cloud Services: ISVs – Change or Die or both!” on that topic can be found on the blog of Daryl Plummer.

Cloud computing and cloud services will change the data quality landscape. More and more smaller companies can now utilize the analyzing, cleansing, standardization, enrichment and deduplication services that in past were only available om premise for larger companies. By using the cloud, massive parallel computations can be executed to bring solutions for huge volumes of data belonging to large companies. Next steps will be that the cloud will execute data cleansing more efficient. Why cleanse in total 200 million contacts for a dozen companies while the region itself has only 100 million citizens (or households).

Steps we still face in light of Cloud Computing are how to differentiate the services for individual need per customer, and the whole serious issue of privacy and security.

David Cearley at Gartner comes with a fascinating other angle. Basically he sees virtualization also as strategic technology to virtualize the data. And by that twist, data quality and data governance appears annoyingly in the middle of your radar screen. In order to use this strategy for your operational excellence, to eliminate the number of redundant data on your real storage devices, and make a virtual layer between your applications and this virtual data storage, you need to be sure that all your applications can work seamlessly with that virtual data.

To significantly reduce your amount of redundant data you need to make it more agnostic. Your apps will still expect the data in a specific format, while your virtual data is stored in another way. You need tools to convert it quickly – back and forward – from the standard virtual way (e.g. first-name, prefix, last-name, zip-code, thoroughfare, etc) to the specific application way (e.g. name in one field, zip-code, street).

Virtualization of data is not only about deduplication of the data! The challenge is more on how to use the virtual data in your existing applications in a non-invasive way. This can be done by using good data quality tools for your cleansing and deduplication, and for your – back and forward – standardisation.

In general I agree with the top 10 of technological strategies, be there some slight personal priority adaptations, but let’s focus on that in later blogs. The missing point is in my opinion the lack of emphasis on risk mitigation, and I do realize that things changed since October 2008. Which technologies can we adopt to avoid that we provide services, products, at the end money to the wrong contacts, or that we are sure to deliver it to the right contacts. The technology strategy of Master Data Management, Know your customer, Single View of X, or how we call it, will need our attention in 2009!