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Real time business intelligence in agent oriented

ETL allows businesses to consolidate their disparate data from any source while moving it from place to place. ETL can transform not only data from different departments but also data from different sources altogether. Today ETL technology is heavily used for stand-alone data warehousing. Traditional ETL tools tend to fall short on the performance, productivity and even cost savings aspects because additional hardware is required; it is this latter reason especially that has moved the industry away from traditional ETL vendors to support improved architecture considerations.

This also allows for greater flexibility, improved scalability and greater performance.

  1. The key issues facing the implementation team included.
  2. A unified data integration architecture can alleviate this burden by eliminating custom code, consolidating IT infrastructure and finally eliminating some of the key risk from poor quality and inconsistent data. In addition, these systems draw from a wide range of internal sales, customer and financial data applications as well as third-party systems.
  3. In 2010, Google published a paper describing an ad hoc querying system called Dremel that it had developed for internal use against data stored in Hadoop. Visibility into static data is no longer sufficient; users must be empowered to directly act on this data based on the available information.
  4. The best solutions available for unifying data across data-centric applications are ones that provide rapid productivity gains for the integration of data. The product still remains operational to support remaining loyal users.

ELT approaches can also reduce costly IT infrastructure costs. The following should also be considered when evaluating an ELT solution: Performance optimizations for set-based transformations. Optimizations for database appliances. No hardware requirements; run-time agents should be deployed on the databases themselves.

Data that always goes from source to target through optimized database pathways; data should never move through the intermediary. Extensible support for standard Java and service-oriented architecture SOA environments.

1. Citing SARL

Design tools that support out-of-the-box optimizations. Users should not have to write special scripts or custom code to enable optimized performance. Figure 2 Not all ELT approaches are equal. Most pushdown optimization transformations still occur inside the ETL engines and require the physical data to transit over the network and through their engines. When selecting an ELT solution, it is important to discern between brittle proprietary technologies that can easily break and open ELT platforms that can dramatically improve performance while simultaneously lowering cost of ownership.

In addition, the demand for trusted data continues to be driven by investments in packaged applications and especially BI software. This is partly because of the complexity of data typically used for performance management as well as the auditing requirements for that data. Data quality is important for BI applications, but for different reasons than data warehousing initiatives. Technical data managers think of data quality differently than a business manager building out a business report.

Database administrators care about the semantics of data, such as broken validations at the field level, whereas the business user considers holistic patterns or matches in the context of customer or performance data as part of a business process. It is this aspect that data architects should consider for comprehensive data quality and data profiling as a complete life cycle.

Profiling data means reverse-engineering metadata from various data stores, detecting patterns in the data so that additional metadata can be inferred and comparing the actual data values to expected data values.

Profiling provides an initial baseline for understanding the ways in which actual data values in your systems fail to conform to expectations.

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Figure 3 mouse over image to enlarge Once data problems are well understood, the rules to repair those problems can be created and executed by data quality engines. Data quality rules can range from semantic integrity to sophisticated parsing, cleansing, standardization, matching and de-duplication.

After data quality rules have been generated, fine-tuned and tested against data samples from within a design environment, those rules can be added to data integration processes so that data can be repaired either statically in the original systems or as part of a data flow. See Figure 3 for an example. By implementing data quality as a life cycle, flow-based control minimizes disruption to existing systems and ensures that downstream analysis and processing works on reliable, trusted data.

Real-time, Consistent Data To understand how data real time business intelligence in agent oriented fits within the BI landscape, look at detailed examples where applications consume real-time data and turn it into in-depth analytics and information for improved decision making.

In many such scenarios, change data capture CDC plays a key role in keeping data consistently updated without impacting the target or source performance. In addition, these systems draw from a wide range of internal sales, customer and financial data applications as well as third-party systems. There are many ways to extract data from a database management system DBMSincluding queries, replication, table dumps, storage snapshots and calls to the API of an application that sits over the database.

Change data capture CDC is an alternate data extraction method that has recently become of interest, primarily because it enables data integration to operate closer to real time. A few vendors have built CDC into their products, but many organizations use the data modeling and log capabilities of a DBMS to build their own solutions.

CDC has been around for many years, but its ability to solve some of the most difficult data integration challenges is driving interest among Real time business intelligence in agent oriented professionals today. A simple example of CDC in action follows in Figure 4. Three separate data sources for a web storefront one for customer data, one for order data, one for product data are consolidated into a single data warehouse.

To simply update the order details in real-time, only the delta or set of orders and new customer info needs to be propagated across to the data warehouse. This does not require moving all the data for both systems. Without CDC, business managers would not be able to see daily trends. In addition, business managers would be forced to wait for the next batch of data to load into the data warehouse before they could look at the results.

By then, it might be too late to make important informed decisions. Figure 4 mouse over image to enlarge Next Generation Interoperability Integrating data means communicating through several integration styles: In some cases moving data directly from databases to different databases is more optimal than passing information over the wire as a service.

  1. After data quality rules have been generated, fine-tuned and tested against data samples from within a design environment, those rules can be added to data integration processes so that data can be repaired either statically in the original systems or as part of a data flow.
  2. But just because social media data and other forms of unstructured information collected in Hadoop will be able to be analyzed in real time doesn't mean that doing so is crucial to the success of an analytics process, said Cindi Howson, founder of BI Scorecard, a research and consulting company in Sparta, N.
  3. In addition, the demand for trusted data continues to be driven by investments in packaged applications and especially BI software. Integrating Spreadsheets and transactional databases for instant data updates in spreadsheet applications such as Excel.
  4. Database administrators care about the semantics of data, such as broken validations at the field level, whereas the business user considers holistic patterns or matches in the context of customer or performance data as part of a business process.
  5. Small changes in BI applications would resort to immense changes, re-coding and re-testing of the entire integration life cycle. Compete initiated the opening of the multidimensional spreadsheet.

But in addition to these generic modes of interoperability, BI applications require a more explicit form of connection that can help automate discovery and mapping of data into and out of systems.

Without this type of automated step, companies would be forced to build these integrations by hand. Small changes in BI applications would resort to immense changes, re-coding and re-testing of the entire integration life cycle. These next generation data integration solutions see Figure 5 contain pluggable modules to better connect these disparate applications, sources, targets and systems.

Figure 5 Actionable Business Intelligence Another key trend in the data integration market sector is actionable BI or also thought of as real-time data warehousing. Visibility into static data is no longer sufficient; users must be empowered to directly act on this data based on the available information. For example, a BI solution might report that a partner is no longer meeting a service-level agreement, but how does the company act on that data?

OLAP and Business Intelligence History

The partner needs to be demoted from a platinum-level profile to a lower-profile category. See Figure 6 for an example. It is an important trend as more solutions take advantage of interoperability points in SOA, BI and data warehousing. One of the key prerequisites for actionable BI is a comprehensive data management approach.

At the core of any data integration solution is the need for metadata management, master data management MDM and data modeling. Figure 6 mouse over image to enlarge Metadata management improves data visibility so managers can understand how data is used and how it relates to other data within a global data-centric system.

Metadata management and data relationship management are cornerstones for MDM-based solutions that reveal data relationships within a single source of truth. Data lineage is a key example of metadata management often used by BI utilities to allow business users to independently track data sources.

'Big data' strains, stresses real-time business intelligence systems

If the data lineage falls short of the actual source and has not integrated properly to the data integration solution, it will be unable to allow business users to identify gaps in the data. Data integration is a critical enabler of this data integrity.

Example A growing manufacturing company implemented a BI solution with the goal of achieving near-real-time analysis of data and reducing the amount of time spent aggregating and extracting data.

The methodology used required the company to implement data integration and data quality in conjunction with the BI solution. Data quality is imperative for BI applications.

Real Time Business Intelligence for the Adaptive Enterprise

Without it, business users build insight and reports on the wrong information. Hence, the methodology used required the company to implement data integration and data quality in conjunction with the BI solution. The key issues facing the implementation team included: Specifically, the company can generate standardized and ad hoc reports, freeing employees to focus on data analysis rather than data retrieval.

This has led to an increase in data accuracy and the ability to slice and analyze it in many different ways. The company also has the ability to budget and forecast based on business drivers.

The Benefit of Cost Savings and Improved Responsiveness Business leaders who demand the most from their BI projects can often be tangled up by the IT burden of a complex, fragmented and misbehaving data architecture.

This puts additional burden on real time business intelligence in agent oriented and IT managers to create custom code or point-based integrations. A unified data integration architecture can alleviate this burden by eliminating custom code, consolidating IT infrastructure and finally eliminating some of the key risk from poor quality and inconsistent data.

In addition, accurate, manageable and transparent data allows organizations to more quickly identify and respond to internal and external events. However, these solutions must be well governed to ensure that data is incorporated into business processes and that data integration becomes part of the change management process. Data quality, data profiling and data governance are essential components to establish and maintain the improved flexibility provided by complex data-centric architecture.

The best solutions available for unifying data across data-centric applications are ones that provide rapid productivity gains for the integration of data. Look for solutions that provide solution add-ons, or knowledge modules, for rapid-deployment and eliminate code creation.

Also look for solutions that provide flexibility to work with multiple applications, databases, data warehouse frameworks and BI applications. Conclusion The strongest BI offerings embed versatile data integration solutions that increase the value of the information delivered to the business user. Optimizing data integration within a BI solution delivers consolidation across complex applications, clean and consistent data, real-time data access and actionable BI.