I had an email yesterday from Blink Logic , which offers on-demand business intelligence. That could mean quite a few things but the most likely definition is indeed what Blink Logic provides: remote access to business intelligence software loaded with your own data. I looked a bit further and it appears Blink Logic does this with conventional technologies, primarily Microsoft SQL Server Analysis Services and Cognos Series 7.
At that point I pretty much lost interest because (a) there’s no exotic technology, (b) quite a few vendors offer similar services*, and (c) the real issue with business intelligence is the work required to prepare the data for analysis, which doesn’t change just because the system is hosted.
Now, this might be unfair to Blink Logic, which could have some technology of its own for data loading or the user interface. It does claim that at least one collaboration feature, direct annotation of data in reports, is unique. But the major point remains: Blink Logic and other “on-demand business intelligence” vendors are simply offering a hosted version of standard business intelligence systems. Does anyone truly think the location of the data center is the chief reason that business intelligence has so few users?
As I see it, the real obstacle is that most source data must be integrated and restructured before business intelligence systems can use it. It may be literally true that hosted business intelligence systems can be deployed in days and users can build dashboards in minutes, but this only applies given the heroic assumption that the proper data is already available. Under those conditions, on-premise systems can be deployed and used just as quickly. Hosting per se has little benefit when it comes to speed of deployment. (Well, maybe some: it can take days or even a week or two to set up a new server in some corporate data centers. Still, that is a tiny fraction of the typical project schedule.)
If hosting isn't the answer, what can make true “business intelligence on demand” a reality? Since the major obstacle is data preparation, then anything that allows less preparation will help. This brings us back to the analytical databases and appliances I’ve been writing about recently : Alterian, Vertica, ParAccel, QlikView, Netezza and so on. At least some of them do reduce the need for preparation because they let users query raw data without restructuring it or aggregating it. This isn’t because they avoid SQL queries, but because they offer a great enough performance boost over conventional databases that aggregation or denormalization are not necessary to return results quickly.
Of course, performance alone can’t solve all data preparation problems. The really knotty challenges like customer data integration and data quality still remain. Perhaps some of those will be addressed by making data accessible as a service (see last week’s post). But services themselves do not appear automatically, so a business intelligence application that requires a new service will still need advance planning. Where services will help is when business intelligence users can take advantage of services created for operational purposes.
“On demand business intelligence” also requires that end-users be able to do more for themselves. I actually feel this is one area where conventional technology is largely adequate: although systems could always be easier, end-users willing to invest a bit of time can already create useful dashboards, reports and analyses without deep technical skills. There are still substantial limits to what can be done – this is where QlikView’s scripting and macro capabilities really add value by giving still more power to non-technical users (or, more precisely, to power users outside the IT department). Still, I’d say that when the necessary data is available, existing business intelligence tools let users accomplish most of what they want.
If there is an issue in this area, it’s that SQL-based analytical databases don’t usually include an end-user access tool. (Non-SQL systems do provide such tools, since users have no alternatives.) This is a reasonable business decision on their part, both because many firms have already selected a standard access tool and because the vendors don’t want to invest in a peripheral technology. But not having an integrated access tool means clients must take time to connect the database to another product, which does slow things down. Apparently I'm not the only person to notice this: some of the analytical vendors are now developing partnerships with access tool vendors. If they can automate the relationship so that data sources become visible in the access tool as soon as they are added to the analytical system, this will move “on demand business intelligence” one step closer to reality.
* results of a quick Google search: OnDemandIQ, LucidEra, PivotLink (an in-memory columnar database), oco, VisualSmart, GoodData and
Autometrics.
Showing posts with label service oriented architecture. Show all posts
Showing posts with label service oriented architecture. Show all posts
Thursday, March 27, 2008
Thursday, March 20, 2008
Service Oriented Architectures Might Really Change Everything
I put in a brief but productive appearance at the DAMA International Symposium and Wilshire Meta-Data Conference running this week in San Diego. This is THE event for people who care passionately about topics like “A Semantic-Driven Application for Master Data Management” and “Dimensional-Modeling – Alternative Designs for Slowly Changing Dimensions”. As you might imagine, there aren't that many of them, and it’s always clear that the attendees revel in spending time with others in their field. I’m sure there are some hilarious data modeling jokes making the rounds at the show, but I wasn’t able to stick around long enough to hear any.
One of the few sessions I did catch was a keynote by Gartner Vice President Michael Blechar. His specific topic was the impact of a services-driven architecture on data management, with the general point being that services depend on data being easily available for many different purposes, rather than tied to individual applications as in the past. This means that the data feeding into those services must be redesigned to fit this broader set of uses.
In any case, what struck me was Blechar’s statement the fundamental way I’ve always thought about systems is now obsolete. I've always thought that systems do three basic things: accept inputs, process them, and create outputs. This doesn't apply in the new world, where services are strung together to handle specific processes. The services themselves handle the data gathering, processing and outputs, so these occur repeatedly as the process moves from one service to another. (Of course, a system can still have its own processing logic that exists outside a service.) But what’s really new is that a single service may be used in several different processes. This means that services are not simply components within a particular proces or system: they have an independent existence of their own.
Exactly how you create and manage these process-independent services is a bit of a mystery to me. After all, you still have to know they will meet the requirements of whatever processes will use them. Presumably this means those requirements must be developed the old fashioned way: by defining the process flow in detail. Any subtle differences in what separate processes need from the same service must be accommodated either by making the service more flexible (for example, adding some parameters that specify how it will function in a particular case) or by adding specialized processing outside of the service. I'll assume that the people who worry about these things for a living must have recognized this long ago and worked out their answers.
What matters to me is what an end-user can do once these services exist. Blechar argued that users now view their desktop as a “composition platform” that combines many different services and uses the results to orchestrate business processes. He saw executive dashboards in particular as evolving from business intelligence systems (based on a data warehouse or data mart) to business activity monitoring based on the production systems themselves. This closer connection to actual activity would in turn allow the systems to be more “context aware”—for example, issuing alerts and taking actions based on current workloads and performance levels.
Come to think of it, my last post discussed eglue and others doing exactly this to manage customer interactions. What a more comprehensive set of services should do is make it easer to set up this type of context-aware decision making.
Somewhat along these same lines, Computerworld this morning describes another Gartner paper arguing that IT’s role in business intelligence will be “marginalized” by end-users creating their own business intelligence systems using tools like enterprise search, visualization and in-memory analytics (hello, QlikView!). The four reader comments posted so far have been not-so-politely skeptical of this notion, basically because they feel IT will still do all the heavy lifting of building the databases that provide information for these user-built systems. This is correct as far as it goes, but it misses the point that IT will be exposing this data as services for operational reasons anyway. In that case, no additional IT work is needed to make it available for business intelligence. Once end-users have self-service tools to access and analyze the data provided by these operational services, business intelligence systems would emerge without IT involvement. I'd say that counts as "marginalized".
Just to bring these thoughts full circle: this means that designing business intelligence systems with the old “define inputs, define processes, define outputs” model would indeed be obsolete. The inputs would already be available as services, while the processes and outputs would be created simultaneously in end-user tools. I’m not quite sure I really believe this is going to happen, but it’s definitely food for thought.
One of the few sessions I did catch was a keynote by Gartner Vice President Michael Blechar. His specific topic was the impact of a services-driven architecture on data management, with the general point being that services depend on data being easily available for many different purposes, rather than tied to individual applications as in the past. This means that the data feeding into those services must be redesigned to fit this broader set of uses.
In any case, what struck me was Blechar’s statement the fundamental way I’ve always thought about systems is now obsolete. I've always thought that systems do three basic things: accept inputs, process them, and create outputs. This doesn't apply in the new world, where services are strung together to handle specific processes. The services themselves handle the data gathering, processing and outputs, so these occur repeatedly as the process moves from one service to another. (Of course, a system can still have its own processing logic that exists outside a service.) But what’s really new is that a single service may be used in several different processes. This means that services are not simply components within a particular proces or system: they have an independent existence of their own.
Exactly how you create and manage these process-independent services is a bit of a mystery to me. After all, you still have to know they will meet the requirements of whatever processes will use them. Presumably this means those requirements must be developed the old fashioned way: by defining the process flow in detail. Any subtle differences in what separate processes need from the same service must be accommodated either by making the service more flexible (for example, adding some parameters that specify how it will function in a particular case) or by adding specialized processing outside of the service. I'll assume that the people who worry about these things for a living must have recognized this long ago and worked out their answers.
What matters to me is what an end-user can do once these services exist. Blechar argued that users now view their desktop as a “composition platform” that combines many different services and uses the results to orchestrate business processes. He saw executive dashboards in particular as evolving from business intelligence systems (based on a data warehouse or data mart) to business activity monitoring based on the production systems themselves. This closer connection to actual activity would in turn allow the systems to be more “context aware”—for example, issuing alerts and taking actions based on current workloads and performance levels.
Come to think of it, my last post discussed eglue and others doing exactly this to manage customer interactions. What a more comprehensive set of services should do is make it easer to set up this type of context-aware decision making.
Somewhat along these same lines, Computerworld this morning describes another Gartner paper arguing that IT’s role in business intelligence will be “marginalized” by end-users creating their own business intelligence systems using tools like enterprise search, visualization and in-memory analytics (hello, QlikView!). The four reader comments posted so far have been not-so-politely skeptical of this notion, basically because they feel IT will still do all the heavy lifting of building the databases that provide information for these user-built systems. This is correct as far as it goes, but it misses the point that IT will be exposing this data as services for operational reasons anyway. In that case, no additional IT work is needed to make it available for business intelligence. Once end-users have self-service tools to access and analyze the data provided by these operational services, business intelligence systems would emerge without IT involvement. I'd say that counts as "marginalized".
Just to bring these thoughts full circle: this means that designing business intelligence systems with the old “define inputs, define processes, define outputs” model would indeed be obsolete. The inputs would already be available as services, while the processes and outputs would be created simultaneously in end-user tools. I’m not quite sure I really believe this is going to happen, but it’s definitely food for thought.
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