Business Intelligence 2.0

Business Intelligence 2.0 (BI 2.0) is a set of new tools and software for business intelligence, beginning in the mid-2000s, that enable, among other things, dynamic querying of real-time corporate data by employees, and a more web- and browser-based approached to such data, as opposed to the proprietary querying tools that had characterized previous business intelligence software.
This change is partly due to the popularization of service-oriented architectures (SOA), which enables for a flexible, composable and adaptive middleware. Also, open standards for exchanging data such as XBRL (Extensible Business Reporting Language), Web Services and various Semantic Web ontologies enable using data external to an organization, such as benchmarking type information.
Business Intelligence 2.0 is most likely named after Web 2.0, although it takes elements from both Web 2.0 (a focus on user empowerment and community collaboration, technologies like RSS and the concept of mashups), and the Semantic Web, sometimes called "Web 3.0" (semantic integration through shared ontologies to enable easier exchange of data).
According to analytics expert Neil Raden, BI 2.0 also implies a move away from the standard data warehouse that business intelligence tools have used, which "will give way to context, contingency and the need to relate information quickly from many sources."

Lessons from social media

According to president and CEO Greg Nelson, BI 2.0 has a lot to learn from social media, such as Twitter, Facebook, blogs etc. He lists, in the white paper Business Intelligence 2.0: Are we there yet?, lessons and opportunities that BI can learn from social media.
Lessons and opportunities:
From Facebook the following can be learned: Destination naturally forces people to login and participate. Continuous flow of information. Provides environment for developers to create their own applications. Post things of interest (reports, graphics, interpretations) to my personal page (things I have discovered.)
From Twitter the following can be learned: Real-time, continuous flow of decisions, status about the business, complex event processing. Platform evolves through unplanned usage/organic evolution of capabilities. Succinct explanation of the state of the business. Search commentary; generate word clouds that provide a visualization of the “vibe” or sentiment of the business. Tags and users comments. Submit note-worthy information (anything on the web you think is newsworthy) – associate it with data or objects.
From blogs the following can be learned: Information can be interpreted and informally published to a group of interested parties.
From RSS the following can be learned: Commentary should be available as an RSS feed. Reports or data updates could be delivered via RSS feed.

The future of BI 2.0

According to president and CEO Greg Nelson the future of BI will see a great change when it comes to BI 2.0 In his 2010 white paper he concludes the following:

1. Decisions, facts and context will be developed through crowdsourcing.
2. Similarly, data and reports will incorporate narrative context information supplied by users. For example, data points and graphs annotated with descriptive insight directly alongside the results.
3. Data will have a more direct linkage with action. When you see something wrong, the data will tell you where it is going wrong and why. Exceptions, alerts and notifications will be based on dynamic business rules that learn about your business and what you are interested in.
4. People will be able to directly act on information. Interactions with operational systems, requests for information, comments, “start a discussion”, provide supporting information, “become a follower” of the metric, and “rate or report a problem” will reside alongside the data.
5. Business decisions shall be monitored so that interventions and our hypotheses about business tactics will be tagged in the context of the data that measures its effect. Our ability to test a hypothesis will be integrated into our decision support systems. Say, for example, we see something in the data; we explore it; we understand its root cause; and design an intervention to deal with it. We will be able to tag interventions or events that have happened and have that appear in the context of the reporting of the data so that over time our collective knowledge about the world will be captured alongside the data and artifacts.
6. Visualizing data and complex relationships will be easier and more intuitive models of info-graphics will become mainstream. Tools will have the ability to create graphic representations of the data based on what it “sees” and displays the best visual display given what it has. Furthermore, the tools will learn what visualizations work best for you and your environment.
7. The ability to detect complex patterns in data through automated analytic routines or intelligent helper models will be built into analytic applications.
8. Finding information will be easier and search results will provide context so that we know when we have the right results. Users will have the ability to tag specific data elements at various levels (page, widget, some aspect of the data presentation – row, column, cell, line, point) or an abstract interpretation of the results. Anyone looking at the same data will see that context when viewed.
9. Linkages with unstructured contents such as documents, discussions and commentary as well as a knowledge base of previously answered requests will be key to ensuring collective knowledge and collaboration.
10. Technical, process and business event monitoring will allow streamlined operational processes (Business Process Engineering, Business Activity Monitoring, Business Rules Engineering) and learning models will be applied to organizational flow of data.* Nelson, Greg (2010).

What is Business Process Management ?

Business process management (BPM) has been referred to as a "holistic management" approach to aligning an organization's business processes with the wants and needs of clients. It promotes business effectiveness and efficiency while striving for innovation, flexibility, and integration with technology. BPM attempts to improve processes continuously. It can therefore be described as a "process optimization process." It is argued that BPM enables organizations to be more efficient, more effective and more capable of change than a functionally focused, traditional hierarchical management approach.  These processes are critical to any organization,^{[citation needed]} as they can generate revenue and often represent a significant proportion of costs. As a managerial approach, BPM sees processes as strategic assets of an organization that must be understood, managed, and improved to deliver value-added products and services to clients. This foundation closely resembles other Total Quality Management or Continuous Improvement Process methodologies or approaches. BPM goes a step further by stating that this approach can be supported, or enabled, through technology to ensure the viability of the managerial approach in times of stress and change. In fact, BPM offers an approach to integrate an organizational "change capability" that is both human and technological. As such, many BPM articles and pundits often discuss BPM from one of two viewpoints: people and/or technology.
Source : wikipedia
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BPM life-cycle

Business process management activities can be grouped into six categories: vision, design, modeling, execution, monitoring, and optimization.

Functions are designed around the strategic vision and goals of an organization. Each function is attached with a list of processes. Each functional head in an organization is responsible for certain sets of processes made up of tasks which are to be executed and reported as planned. Multiple processes are aggregated to function accomplishments and multiple functions are aggregated to achieve organizational goals.

Design

Process Design encompasses both the identification of existing processes and the design of "to-be" processes. Areas of focus include representation of the process flow, the factors within it, alerts & notifications, escalations, Standard Operating Procedures, Service Level Agreements, and task hand-over mechanisms.
Good design reduces the number of problems over the lifetime of the process. Whether or not existing processes are considered, the aim of this step is to ensure that a correct and efficient theoretical design is prepared.
The proposed improvement could be in human-to-human, human-to-system, and system-to-system workflows, and might target regulatory, market, or competitive challenges faced by the businesses.
The existing process and the design of new process for various application will have to synchronise as such will not effect the business in major outage. The business as usual is the standard to be attained when design of process for multiple systems is considered.

Modeling

Modeling takes the theoretical design and introduces combinations of variables (e.g., changes in rent or materials costs, which determine how the process might operate under different circumstances).
It also involves running "what-if analysis" on the processes: "What if I have 75% of resources to do the same task?" "What if I want to do the same job for 80% of the current cost?".

Execution

One of the ways to automate processes is to develop or purchase an application that executes the required steps of the process; however, in practice, these applications rarely execute all the steps of the process accurately or completely. Another approach is to use a combination of software and human intervention; however this approach is more complex, making the documentation process difficult.
As a response to these problems, software has been developed that enables the full business process (as developed in the process design activity) to be defined in a computer language which can be directly executed by the computer. The system will either use services in connected applications to perform business operations (e.g. calculating a repayment plan for a loan) or, when a step is too complex to automate, will ask for human input. Compared to either of the previous approaches, directly executing a process definition can be more straightforward and therefore easier to improve. However, automating a process definition requires flexible and comprehensive infrastructure, which typically rules out implementing these systems in a legacy IT environment.
Business rules have been used by systems to provide definitions for governing behaviour, and a business rule engine can be used to drive process execution and resolution.

Monitoring

Monitoring encompasses the tracking of individual processes, so that information on their state can be easily seen, and statistics on the performance of one or more processes can be provided. An example of the tracking is being able to determine the state of a customer order (e.g. order arrived, awaiting delivery, invoice paid) so that problems in its operation can be identified and corrected.
In addition, this information can be used to work with customers and suppliers to improve their connected processes. Examples of the statistics are the generation of measures on how quickly a customer order is processed or how many orders were processed in the last month. These measures tend to fit into three categories: cycle time, defect rate and productivity.
The degree of monitoring depends on what information the business wants to evaluate and analyze and how business wants it to be monitored, in real-time, near real-time or ad-hoc. Here, business activity monitoring (BAM) extends and expands the monitoring tools generally provided by BPMS.
Process mining is a collection of methods and tools related to process monitoring. The aim of process mining is to analyze event logs extracted through process monitoring and to compare them with an a priori process model. Process mining allows process analysts to detect discrepancies between the actual process execution and the a priori model as well as to analyze bottlenecks.

Optimization

Process optimization includes retrieving process performance information from modeling or monitoring phase; identifying the potential or actual bottlenecks and the potential opportunities for cost savings or other improvements; and then, applying those enhancements in the design of the process. Overall, this creates greater business value.^[9]

Re-engineering

When the process becomes too noisy and optimization is not fetching the desired output, it is recommended to re-engineer the entire process cycle. BPR has become an integral part of organizations to achieve efficiency and productivity at work.

Certification

Currently the certification is being offered by Global Association for Quality Management (GAQM) the Syllabus and Certificate is recognized, approved and managed by the International Accreditation Organization (IAO)

What is Expert System ?

expert system is a computer system that emulates the decision-making ability of a human expert. Expert systems are designed to solve complex problems by reasoning about knowledge, like an expert, and not by following the procedure of a developer as is the case in conventional programming.The first expert systems were created in the 1970s and then proliferated in the 1980s. Expert systems were among the first truly successful forms of AI software.
An expert system has a unique structure, different from traditional computer programming. It is divided into two parts, one fixed, independent of the expert system: the inference engine, and one variable: the knowledge base. To run an expert system, the engine reasons about the knowledge base like a human. In the 80s a third part appeared: a dialog interface to communicate with users.This ability to conduct a conversation with users was later called "conversational"

What is Management Information System ?

 
A management information system (MIS) provides information that organizations need to manage themselves efficiently and effectively.Management information systems are typically computer systems used for managing five primary components: hardware, software,data (information for decision making), procedures (design,development and documentation), and people (individuals, groups, or organizations). Management information systems are distinct from other information systems, in that they are used to analyze and facilitate strategic and operational activities. Academically, the term is commonly used to refer to the study of how individuals, groups, and organizations evaluate, design, implement, manage, and utilize systems to generate information to improve efficiency and effectiveness of decision making, including systems termed decision support systems, expert systems, and executive information systems.Most business schools (or colleges of business administration within universities) have an MIS department, alongside departments of accounting, finance, management, marketing, and sometimes others, and grant degrees (at undergrad, masters, and PhD levels) in MIS.

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Information Systems Management - Video Presentation

What is Business Analytics ?

Business analytics (BA) refers to the skills, technologies, applications and practices for continuous iterative exploration and investigation of past business performance to gain insight and drive business planning.Business analytics focuses on developing new insights and understanding of business performance based on data and statistical methods. In contrast, business intelligence traditionally focuses on using a consistent set of metrics to both measure past performance and guide business planning, which is also based on data and statistical methods.
Business analytics makes extensive use of data, statistical and quantitative analysis, explanatory and predictive modeling, and fact-based management to drive decision making. It is therefore closely related to management science. Analytics may be used as input for human decisions or may drive fully automated decisions. Business intelligence is querying, reporting, OLAP, and "alerts."
In other words, querying, reporting, OLAP, and alert tools can answer questions such as what happened, how many, how often, where the problem is, and what actions are needed. Business analytics can answer questions like why is this happening, what if these trends continue, what will happen next (that is, predict), what is the best that can happen (that is, optimize)


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What is Data Replication ?

Replication involves sharing information so as to ensure consistency between redundant resources, such as software or hardware components, to improve reliability, fault-tolerance, or accessibility.

Replication in distributed systems

Replication is one of the oldest and most important topics in the overall area of distributed systems.
Whether one replicates data or computation, the objective is to have some group of processes that handle incoming events. If we replicate data, these processes are passive and operate only to maintain the stored data, reply to read requests, and apply updates. When we replicate computation, the usual goal is to provide fault-tolerance. For example, a replicated service might be used to control a telephone switch, with the objective of ensuring that even if the primary controller fails, the backup can take over its functions. But the underlying needs are the same in both cases: by ensuring that the replicas see the same events in equivalent orders, they stay in consistent states and hence any replica can respond to queries.
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This Video show it in details :

What Is Data Management Maturity ?

The Data Management Maturity (DMM) Model defines the components, business processes and capability areas required for effective data management. It provides standard assessment criteria that organizations can use to evaluate data management goals against documented best practice. It defines the ‘what and why’ of data management at both an objective level and from the perspective of practical implementation.
The DMM was created via collaboration of data management practitioners, operations managers, IT professionals and representatives of lines-of-business across the financial industry. It establishes the assessment criteria and requirements for achieving alignment on strategy, implementing governance mechanisms, managing operational components, defining dependencies, aligning data with IT capabilities, ensuring data quality and integrating data into business processes.


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What is Data Warehouse ?

first step to know what is Data Warehouse is to see this image :






Second Step is to see this nice video
 
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and you can see the wiki definition too :
or enterprise data warehouse (DW, DWH, or EDW) is a database used for reporting and data analysis. It is a central repository of data which is created by integrating data from one or more disparate sources. Data warehouses store current as well as historical data and are used for creating trending reports for senior management reporting such as annual and quarterly comparisons.
The data stored in the warehouse are uploaded from the operational systems (such as marketing, sales etc., shown in the figure to the right). The data may pass through an operational data store for additional operations before they are used in the DW for reporting.
The typical ETL-based data warehouse uses staging, data integration, and access layers to house its key functions. The staging layer or staging database stores raw data extracted from each of the disparate source data systems. The integration layer integrates the disparate data sets by transforming the data from the staging layer often storing this transformed data in an operational data store (ODS) database. The integrated data are then moved to yet another database, often called the data warehouse database, where the data is arranged into hierarchical groups often called dimensions and into facts and aggregate facts. The combination of facts and dimensions is sometimes called a star schema. The access layer helps users retrieve data.
A data warehouse constructed from an integrated data source systems does not require ETL, staging databases, or operational data store databases. The integrated data source systems may be considered to be a part of a distributed operational data store layer. Data federation methods or data virtualization methods may be used to access the distributed integrated source data systems to consolidate and aggregate data directly into the data warehouse database tables. Unlike the ETL-based data warehouse, the integrated source data systems and the data warehouse are all integrated since there is no transformation of dimensional or reference data. This integrated data warehouse architecture supports the drill down from the aggregate data of the data warehouse to the transactional data of the integrated source data systems.
Data warehouses can be subdivided into data marts. Data marts store subsets of data from a warehouse.
This definition of the data warehouse focuses on data storage. The main source of the data is cleaned, transformed, cataloged and made available for use by managers and other business professionals for data mining, online analytical processing, market research and decision support (Marakas & O'Brien 2009). However, the means to retrieve and analyze data, to extract, transform and load data, and to manage the data dictionary are also considered essential components of a data warehousing system. Many references to data warehousing use this broader context. Thus, an expanded definition for data warehousing includes business intelligence tools, tools to extract, transform and load data into the repository, and tools to manage and retrieve metadata.

What is OLAP ?

OLAP is Online analytical processing

wikipedia says  :
online analytical processing, or OLAP , is an approach to answer multi-dimensional analytical (MDA) queries swiftly. OLAP is part of the broader category of business intelligence, which also encompasses relational database, report writing and data mining. Typical applications of OLAP include business reporting for sales, marketing, management reporting, business process management (BPM), budgeting and forecasting, financial reporting and similar areas, with new applications coming up, such as agriculture The term OLAP was created as a slight modification of the traditional database term OLTP (Online Transaction Processing).

OLAP tools enable users to analyze multidimensional data interactively from multiple perspectives. OLAP consists of three basic analytical operations: consolidation (roll-up), drill-down, and slicing and dicing. Consolidation involves the aggregation of data that can be accumulated and computed in one or more dimensions. For example, all sales offices are rolled up to the sales department or sales division to anticipate sales trends. By contrast, the drill-down is a technique that allows users to navigate through the details. For instance, users can view the sales by individual products that make up a region’s sales. Slicing and dicing is a feature whereby users can take out (slicing) a specific set of data of the OLAP cube and view (dicing) the slices from different viewpoints.
Databases configured for OLAP use a multidimensional data model, allowing for complex analytical and ad-hoc queries with a rapid execution time. They borrow aspects of navigational databases, hierarchical databases and relational databases.



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What is SaaS ?

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Software as a service (SaaS)pplications, including Office & Messaging software, DBMS software, Management software, CAD software, Development software, Virtualization ,accounting, collaboration, customer relationship management (CRM), management information systems (MIS), enterprise resource planning (ERP), invoicing, human resource management (HRM), content management (CM) and service desk management. SaaS has been incorporated into the strategy of all leading enterprise software companies. One of the biggest selling points for these companies is the potential to reduce IT support costs by outsourcing hardware and software maintenance and support to the SaaS provider.
According to a Gartner Group estimate, SaaS sales in 2010 reached $10 billion, and were projected to increase to $12.1bn in 2011, up 20.7% from 2010. Gartner Group estimates that SaaS revenue will be more than double its 2010 numbers by 2015 and reach a projected $21.3bn. Customer relationship management (CRM) continues to be the largest market for SaaS. SaaS revenue within the CRM market was forecast to reach $3.8bn in 2011, up from $3.2bn in 2010.
The term "software as a service" (SaaS) is considered to be part of the nomenclature of cloud computing, along with infrastructure as a service (IaaS), platform as a service (PaaS), desktop as a service (DaaS), and backend as a service (BaaS).


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Business intelligence

Business intelligence (BI) is a set of theories, methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information for business purposes. BI can handle large amounts of information to help identify and develop new opportunities. Making use of new opportunities and implementing an effective strategy can provide a competitive market advantage and long-term stability.
BI technologies provide historical, current and predictive views of business operations. Common functions of business intelligence technologies are reporting, online analytical processing, analytics, data mining, process mining, complex event processing, business performance management, benchmarking, text mining, predictive analytics and prescriptive analytics.
Though the term business intelligence is sometimes a synonym for competitive intelligence (because they both support decision making), BI uses technologies, processes, and applications to analyze mostly internal, structured data and business processes while competitive intelligence gathers, analyzes and disseminates information with a topical focus on company competitors. If understood broadly, business intelligence can include the subset of competitive intelligence.

Applications in an enterprise

Business intelligence can be applied to the following business purposes, in order to drive business value.^{[citation needed]}

1. Measurement – program that creates a hierarchy of performance metrics (see also Metrics Reference Model) and benchmarking that informs business leaders about progress towards business goals (business process management).
2. Analytics – program that builds quantitative processes for a business to arrive at optimal decisions and to perform business knowledge discovery. Frequently involves: data mining, process mining, statistical analysis, predictive analytics, predictive modeling, business process modeling, complex event processing and prescriptive analytics.
3. Reporting/enterprise reporting – program that builds infrastructure for strategic reporting to serve the strategic management of a business, not operational reporting. Frequently involves data visualization, executive information system and OLAP.
4. Collaboration/collaboration platform – program that gets different areas (both inside and outside the business) to work together through data sharing and electronic data interchange.
5. Knowledge management – program to make the company data driven through strategies and practices to identify, create, represent, distribute, and enable adoption of insights and experiences that are true business knowledge. Knowledge management leads to learning management and regulatory compliance.

In addition to above, business intelligence also can provide a pro-active approach, such as ALARM function to alert immediately to end-user. There are many types of alerts, for example if some business value exceeds the threshold value the color of that amount in the report will turn RED and the business analyst is alerted. Sometimes an alert mail will be sent to the user as well. This end to end process requires data governance, which should be handled by the expert