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Service Oriented Business Intelligence
- 1. Service Oriented Business Intelligence Richard Veryard
- 4. Traditional View of Business Intelligence © 2003 Everware-CBDI Inc June 2003
- 5. Closed-Loop Business Intelligence © 2003 Everware-CBDI Inc June 2003
- 6. Real-Time Enterprise © 2003 Everware-CBDI Inc June 2003
- 7. A Broader Framework for BI © 2003 Everware-CBDI Inc June 2003
- 9. Three Levels of Management Capability © 2005 Everware-CBDI Inc October 2005 From Stand-Alone BI to Collaborative BI Should we be extending credit to customers at all? Strategic Capability From Stand-Alone BI to Integrated BI Are we extending credit to the right customers Command & Control Capability Embedded BI Is this customer creditworthy? Execution Capability Solution Options Typical Enquiry Capability
- 10. Three Types of BI Services © 2005 Everware-CBDI Inc October 2005 Strategic Capability Command & Control Capability Execution Capability First Order Information Second Order Information Factory services Business management services Information Mapping services
- 11. Extended MIS Pyramid © 2005 Everware-CBDI Inc October 2005 operational command and control strategic operational command and control strategic Internal Data External Data aggregation attenuation Derived Analytical Constructs
- 12. Four Modes of Business Intelligence © 2005 Everware-CBDI Inc October 2005 Longer-term SOA opportunity Orchestrate BI services across a federated management or governance structure Collaborative BI Medium-term SOA opportunity BI activities are coordinated with one another, and synchronized with business and operations. Integrated BI Short-term SOA opportunity BI enquiries are wrapped as services and invoked from the operational systems Embedded BI Current BI practice BI activities are decoupled and desynchronized from the business and from operational systems. BI activities are performed by independent knowledge workers. Stand-Alone BI Timescale Description Mode
- 13. Five Styles of Business Intelligence derived from Microstrategy © 2005 Everware-CBDI Inc October 2005 But what about this quadrant? Number and range of users High Low Alerting and Report Delivery Enterprise Reporting Low Cube Analysis Adhoc Query and Analysis Statistical Analysis and Data Mining High Analytical sophistication and user interactivity
Hinweis der Redaktion
- Traditional Enterprise Reporting As we have already seen, traditional enterprise reporting is faced with: increased complexity of input. increasing complexity of output. increasing complexity of use. SOA can therefore be used to decouple the output (formats) from the input (formats) to encapsulate the method for calculating the output from the input to deliver the results in a flexible way to a broader community of users We can define a standard set of SOA architectural patterns for data extraction, composition and delivery. Report Delivery and Alerting Report delivery and alerting contributes to a series of event-driven systems. SOA can be used to design the performance of the whole monitoring and control system. We can use a standard set of patterns for this. we need to define the range of possible responses at appropriate levels of granularity/specificity. we need to decouple the events to allow for meaningful and independent action by different recipients. and/or we need to coordinate multiple responses to complex events.
- Business Intelligence Framework The Traditional View: Query, Reporting and Analysis At the core of most BI systems are software products providing query, reporting and analysis functionality, sometimes referred to as OLAP. Traditionally, these functions have been based either directly on operational systems, or more commonly on a data warehouse or data mart, which assembles and restructures data from one or more operational data stores. The result of the data combination can be stored in another data store, or may remain virtual. In many cases, the data are obtained from both relational and multidimensional OLAP schemas, and are combined into a hybrid schema (Hybrid OLAP or HOLAP) which is then usually virtual. Web services can be used to make this functionality available remotely over the internet, and a number of BI vendors offer web service front-ends to their basic OLAP products, including Crystal Decisions, Cognos and Microstrategy. Some BI vendors offer a reduced version of this, in which the remote services merely provide access to a set of predefined reports, or generate predefined management alerts, with no ability to perform analysis dynamically. This is the case for many so-called portals or dashboards, in which the service can be understood simply as a one-way information flow from the data store to the manager. For example, Microsoft's new product SQL Server Reporting Services (SSRS) is presented as a Business Intelligence tool, but is essentially a server-based enterprise reporting engine, providing a useful .NET data hub but with no on-line analytic functionality. Some BI vendors appear to think that the BI process terminates with information dissemination. And if the aim is solely to display information on remote users' screens, then it isn't obvious why delivering the information using web services is any better than simply delivering the information in HTML form to a standard browser - the so-called zero footprint solution that has been commonplace for some time. Fortunately, there are some BI vendors that take a broader view.
- Closed Loop BI Even with full functionality for query, reporting and analysis, the traditional view of business intelligence only gives us a partial picture of the process, lacking a broader system purpose and context of management control and action. Figure 1 encourages a misleading view of business intelligence as a passive activity by individual managers. It is a known pitfall for business intelligence to be taken over by clever number-crunchers, identifying fascinating statistical patterns with no practical relevance for management action. In contrast, Figure 2 shows how business intelligence can be understood as a closed control loop. Managers use tools to process and interpret information; they then act upon this information and monitor the effects of their actions. If the actions have the expected effect on business performance, this helps to confirm the original interpretation; if management intervention doesn't work in the expected way, then this should trigger further analysis. This management feedback and learning loop is a key element of true business intelligence. Closed loop business intelligence also includes the possibility of management actions whose primary purpose is to gain more information/intelligence - to learn something or to test a hypothesis. A major retailer offers a special price for a specified product in selected stores, and then watches the effect on sales volumes. The FBI may arrest a few minor members of a criminal gang in order to provoke the gang leaders into a detectable reaction. MicroStrategy's Transactor product is designed to support such closed-loop business intelligence, combining monitoring and control into a single on-line transaction. Users view business intelligence information on-line and can directly initiate actions against an operational system, such as database, ERP or legacy. Typical applications include: inventory control & reordering, authorization, and budgeting.
- Real-Time Enterprise As well as web services for business intelligence, it is also worth mentioning the converse: business intelligence for web services. We have previously referred to the growing complexity of SOA solutions, and the need for increasingly sophisticated management tools to remain on top of this complexity. Some specialized intelligence is already found in some system management platforms, such as IBM's Tivoli, especially to monitor security threats. However, there is clearly an opportunity to develop this kind of functionality further to achieve a broader range of autonomic system behavior. Thus in the Real-Time Enterprise, as shown, transactions and events can be intercepted and analyzed as they happen, without waiting for them to reach a data store, and this leads to the possibility of a real-time or near-real-time system response. Time Enterprise, this usually needs to be placed within a broader business intelligence context. This is especially important where security is concerned, since a system that relies on a fixed set of rules and policies is usually vulnerable to intelligent attack. Further off-line analysis will allow the policies to be continually reviewed and refined. It is also important to provide a context for making sense of events and trends. Regular readers will recall our analysis of the Kodak case , in which the online retailer found itself obliged to supply a large number of digital cameras at an incorrect price. Real-time business intelligence can help identify a sudden increase in demand for a given product, and may place some constraints on automatic supply until the increase can be explained. But we need some context for this. It is only when we can link a sudden increase EITHER to a marketing campaign by Kodak OR to some hostile activity on an internet newsgroup that we know what to make of it - and therefore how to respond. Thus there is still a role for stored data for business intelligence in the Real-Time Enterprise. Among other things, we usually need to be able to analyze data for the current period, and this requires reviewing transactions for the past 24 hours, or the past 7 days or whatever. Business intelligence is often about detecting changes, trends and patterns, finding "the difference that makes a difference", and is not just about the absolute numbers prevailing at the present. If we want to perform sophisticated statistics, we need to have enough data to produce statistically significant results. We need to be able to distinguish meaningful events from random noise. There is also a role for off-line processing. Although we can expect a progressive shift towards autonomic systems in which some self-correction and self-protection functions can be coded as policies and executed automatically, at least for the foreseeable future there is always going to be a management/learning loop that goes through human brains and therefore requires some thinking time. One of the features of Tivoli is the ability to reduce large volumes of security data to rather smaller volumes of security information, which is better for human inspection. Business intelligence software may be used for filtering and selecting things for human attention - but these filtering rules will themselves need some a management/learning loop. It may well be that human intervention now operates at a higher level of abstraction - perhaps by refining policies. But any intervention should still have a detectable effect on WIGO (what is going on). Sometimes there will be a considerable delay before this effect is discernable. This is yet another reason why the BI system as a whole needs to have a memory.
- A Broader Framework for BI Putting the closed-loop business intelligence together with the real-time enterprise, we have produced the general framework for business intelligence
- Provide query & reporting services to users. Provide dynamic analysis services to users. Obtaining real-time data via web services. Intercepting XML documents in transit. Presenting a virtual data store as a web service. Exercising management action through web services. Disseminating management action through web services. Using web services to support peer-to-peer communications between knowledge workers. With this broader view of business intelligence, we can now see several additional ways in which web services can play a useful role and help integrate the BI process. Some of these steps may also involve grid technology. (See our report in the CBDI Journal for January.) Grid concepts may be used to present a heterogeneous array of data stores as a single virtual data store. Grid concepts may also be used to link an array of managers and other knowledge workers, collectively making sense of large amounts of complex information.
- Three Types of BI Services These models lead to the identification of at least three types of business service business management services that correspond to the higher level capabilities (Command&Control and Strategic) information services that establish mappings between first-order information (BI style 1 and 5) and second-order information (BI style 2, 3 and 4) "factory" services that use second-order analysis to dynamically create first-order change - for example if data mining identifies that customer demand is dependent upon the weather, then a "factory" service can automatically create a weather-tracking service as a plug-in to existing operational monitoring systems (BI style 1 and 5). In addition, the data mining will drive the instrumentation of the operational systems. This way of integrating BI with the operational systems is particularly attractive for agile security, since it allows emerging security threats to be identified using BI tools and instantly reflected in additional security traps in the operational systems. But it clearly has value for the whole spectrum of "on-demand". This implies a layered architecture of BI services that plugs into a general layered architecture.
- MIS Pyramid Many readers will be familiar with a simple MIS pyramid, where data are summarized upwards from large quantities of operational data to smaller quantities of highly condensed information, used for strategic purposes. The top level of this simple pyramid is sometimes called Strategic. But of course strategic decisions cannot be made solely on the basis of this summary information. Top management need to combine information from many different sources – external as well as internal. Figure shows how this simple MIS pyramid can be set against another pyramid based on external data. Often the external data are only available in some summarized form. The important analytical task is then to create intelligent support for business decisions by combining internal and external data, often in highly complex ways. So the challenging aspects of business modeling is to analyze the derived bits in the middle.
- Four Modes of Business Intelligence In previous table we identified four modes of Business Intelligence, which we now summarize, and this implies a roadmap for implementing Service-Oriented Business Intelligence. From this table, I've singled out a couple of specific short-term and medium term opportunities for creating synergies between web services and BI. Business Opportunity # 1 Many organizations such as retail and banking collect large amounts of data about customer transactions, which are available for analysis within the organization. This analysis is used for a range of purposes from marketing and product management to customer relationship management. But many consumers would like to analyze their own spending. A bank might provide a flat transaction file which you can analyze yourself, but a retail organization probably doesn't even provide that. But with embedded BI, it becomes possible to provide trend analyses and other complex information services directly to customers. For example, a consumer may wish to compare the amount spent on children's clothing with a benchmark of other households with the same number/ages of children, or track expenditure trends against various user-defined categories. Here's an opportunity for a commercial organization to provide some significant added value to its loyalty scheme customers, at relatively little cost. Furthermore, the organization itself may gain valuable insight about its customers (both individually and collectively) from the nature of the analyses that they carry out. Business Opportunity # 2 The second area is BAM – which variously stands for Business Activity Management, Modeling or Monitoring. We are currently seeing initiatives from a range of vendors including Microsoft and Oracle. This technology starts to introduce new modes of integration into the business process – including data collection (e.g. RFID and web services used to instrument physical processes such as manufacturing and logistics), orchestration and workflow (BizTalk, BPEL), analytics (statistical analysis) and information dissemination (dashboard, portal). The next step will be to provide web-service-enabled management actions back into the operational business process. There is likely to be some resistance towards doing this for core business processes, at least in the short term, but we expect to see further experimentation in some non-critical business processes. Summary of Approach In general terms, Information Needs Analysis is a very powerful technique because it is non rigorous and business oriented. It can be used to define derived Business Types (one set of information types) that form the basis for answering complex questions. Traditional approaches to Information Needs Analysis (such as Information Engineering) can be extended by referencing the business process world in richer and more diverse ways, relying on many more sources of analytical input as well as many more ways of constructing composite information objects.
- Five Styles of Business Intelligence The BI vendor Microstrategy has identified five types of business intelligence, which are (not surprisingly) covered by its own BI tools. These are differentiated in two ways: firstly by the number and range of users, and secondly by the degree of analytical sophistication and user interactivity. According to Microstrategy, these two are inversely proportional – thus the greater the number and range of users, the lower the sophistication and interactivity. Microstrategy's five types of BI can therefore be arranged in a 2x2 matrix with the top right quadrant empty. This reflects a common attitude about BI – that because the complex stuff is hard to use, and costly to service, therefore it is only going to be used by a small number of highly skilled users working on stand-alone problems. One of the opportunities of SOA is that it potentially allows us to expose BI services to a much wider user community, and to embed BI services into composite end-user services. The challenge is to produce semantic models, at suitable levels of aggregation/granularity, that support interoperability and composition.