PriceWaterhouseCoopers Spring Technology Forecast (Part 3)

This is the last in a series of posts summarizing the PriceWaterhouseCooper Spring Technology Forecast. I spent a lot of time on the report, since it highlights many important concepts about the Semantic Web and business.

The last featured article in the report is entitled 'A CIO's strategy for rethinking "messy BI"'. The recommendation is to use Linked Data to bring together internal and external information - to help with the "information problem". How does PwC define the "information problem"? As follows ... "there's no way traditional information systems can handle all the sources [of data], many of which are structured differently or not structured at all." The recommendation boils down to creating a shared or upper ontology for information mediation, and then using it for analysis, for helping to create a business ecosystem, and to harmonize business logic and operating models. The two figures below illustrate these concepts.

The article includes a great quote on the information problem, why today's approaches (even metadata) are not enough, and the uses of Semantic Web technologies ... "Think of Linked Data as a type of database join that relies on contextual rules and pattern matching, not strict preset matches. As a user looks to mash up information from varied sources, Linked Data tools identify the semantics and ontologies to help the user fit the pieces together in the context of the exploration. ... Many organizations already recognize the importance of standards for metadata. What many don’t understand is that working to standardize metadata without an ontology is like teaching children to read without a dictionary. Using ontologies to organize the semantic rationalization of the data that flow between business partners is a process improvement over electronic data interchange (EDI) rationalization because it focuses on concepts and metadata, not individual data elements, such as columns in a relational database management system. The ontological approach also keeps the CIO’s office from being dragged into business-unit technical details and squabbling about terms. And linking your ontology to a business partner’s ontology exposes the context semantics that data definitions lack."

PwC suggests taking 2 (non-exclusive) approaches to "explore" the Semantic Web and Linked Data:

• Add the dimension of semantics and ontologies to existing, internal data warehouses and data stores
• Provide tools to help users get at both internal and external Linked Data

And, as with the previous posts, I want to finish with a quote from one of the interviews in the report. This quote comes from Frank Chum of Chevron, and discusses why they are now looking to the Semantic Web and ontologies to advance their business. "Four things are going on here. First, the Semantic Web lets you be more expressive in the business logic, to add more contextual meaning. Second, it lets you be more flexible, so that you don’t have to have everything fully specified before you start building. Then, third, it allows you to do inferencing, so that you can perform discovery on the basis of rules and axioms. Fourth, it improves the interoperability of systems, which allows you to share across the spectrum of the business ecosystem. With all of these, the Semantic Web becomes a very significant piece of technology so that we can probably solve some of the problems we couldn’t solve before. One could consider these enhanced capabilities [from Semantic Web technology] as a “souped up” BI [business intelligence]."

PriceWaterhouseCoopers Spring Technology Forecast (Part 2)

This post continues the review and summarization of PwC's Spring Technology Forecast, focused on the Semantic Web.

The second featured article is Making Semantic Web connections. It discusses the business value of using Linked Data, and includes interesting information from a CEO survey about information gaps (and how the Semantic Web can address these gaps). The article argues that to get adequate information, the business must better utilize its own internal data, as well as data from external sources (such as information from members of the business' ecosystem or the Web). This is depicted in the following two figures from the article ...

I also want to include some quotes from the article - especially since they support what I said in an earlier blog from my days at Microsoft, Question on what "policy-based business" means ... :-)

• Data aren’t created in a vacuum. Data are created or acquired as part of the business processes that define an enterprise. And business processes are driven by the enterprise business model and business strategy, goals, and objectives. These are expressed in natural language, which can be descriptive and persuasive but also can create ambiguities. The nomenclature comprising
• ... the natural language used to describe the business, to design and execute business processes, and to define data elements is often left out of enterprise discussions of performance management and performance improvement.
• ... ontologies can become a vehicle for the deeper collaboration that needs to occur between business units and IT departments. In fact, the success of Linked Data within a business context will depend on the involvement of the business units. The people in the business units are the best people to describe the domain ontology they’re responsible for.
• Traditional integration methods manage the data problem one piece at a time. It is expensive, prone to error, and doesn’t scale. Metadata management gets companies partway there by exploring the definitions, but it still doesn’t reach the level of shared semantics defined in the context of the extended virtual enterprise. Linked Data offers the most value. It creates a context that allows companies to compare their semantics, to decide where to agree on semantics, and to select where to retain distinctive semantics because it creates competitive advantage.
  

As in my last post, I want to reinforce the message and include a quote from one of the interviews. This one comes from Uche Ogbuji of Zepheira ... "... it’s not a matter of top down. It’s modeling from the bottom up. The method is that you want to record as much agreement as you can. You also record the disagreements, but you let them go as long as they’re recorded. You don’t try to hammer them down. In traditional modeling, global consistency of the model is paramount. The semantic technology idea turns that completely on its head, and basically the idea is that global consistency would be great. Everyone would love that, but the reality is that there’s not even global consistency in what people are carrying around in their brains, so there’s no way that that’s going to reflect into the computer. You’re always going to have difficulties and mismatches, and, again, it will turn into a war, because people will realize the political weight of the decisions that are being made. There’s no scope for disagreement in the traditional top-down model. With the bottom-up modeling approach you still have the disagreements, but what you do is you record them."

And, yes, I did say something similar to this in an earlier post on Semantic Web and Business. (Thumbs up :-)

PriceWaterhouseCoopers Spring Technology Forecast (Part 1)

In an earlier post, I mentioned PriceWaterhouseCoopers' spring technology forecast and its discussion of the Semantic Web in business. In this and the following post, I want to overview and highlight several of the articles. Let's start with the first featured article ...

Spinning a data Web overviewed the technologies of the Semantic Web, and discussed how businesses can benefit from developing domain ontologies and then mediating/integrating/querying them across both internal and external data. The value of mediation is summarized in the following figure ...

I like this, since I said something similar in my post on the Semantic Web and Business.

Backing up this thesis, Tom Scott of BBC Earth provided a supporting quote in his interview, Traversing the Giant Global Graph. "... when you start getting either very large volumes or very heterogeneous data sets, then for all intents and purposes, it is impossible for any one person to try to structure that information. It just becomes too big a problem. For one, you don’t have the domain knowledge to do that job. It’s intellectually too difficult. But you can say to each domain expert, model your domain of knowledge— the ontology—and publish the model in the way that both users and machine can interface with it. Once you do that, then you need a way to manage the shared vocabulary by which you describe things, so that when I say “chair,” you know what I mean. When you do that, then you have a way in which enterprises can join this information, without any one person being responsible for the entire model. After this is in place, anyone else can come across that information and follow the graph to extract the data they’re interested in. And that seems to me to be a sane, sensible, central way of handling it."

Lots of Interest in Wolfram|Alpha, and Some Discussion of Microsoft's EDM

Wolfram|Alpha is cool and uses great, new technology to provide question-answer query capabilities. But, it still has a way to go. As Read-Write-Web pointed out in their post, "the areas where Alpha exceeds are in Mathematics, Engineering, Chemistry, Physics, and the Life Sciences." What is needed is to take this technology and use it with business vocabularies and their backing databases.

To do this, you first need the capture of the vocabularies (yes, I will get back to this in my postings :-) - and then mappings to the physical stores. Microsoft's EDM (Entity Data Model) and Entity Framework are a start in enabling the mappings. They allow you to define a conceptual model, a physical model and then map between the two - although they don't help you create the conceptual or physical models, are not focused on conceptual modeling, and are too focused on the physical structure of the data store. Specifically, some of the ideal mappings are not possible (at least the last time that I tried), and all the data and meta-data that I would like to capture about the conceptual model are not possible to do (without extensions). But, they exist, are usable today, and will definitely be improved.

Another cool thing is that EDM and the framework allow you to write queries in the conceptual model, that are then translated to the physical one and run against the store. Pretty neat. Now, let's put a better query capability up front (like Wolfram|Alpha) ....