Business Intelligence In General
- 1 What is Business Intelligence...
- 2 Why is Business Intelligence important?
- 3 History of Business Intelligence
- 4 Designing and implementing a business intelligence program
- 5 BI Includes
- 6 RoadMap...
What is Business Intelligence...
Business intelligence (BI) is a business management term which refers to applications and technologies which are used to gather, provide access to, and analyze data and information about their company operations. Business intelligence systems can help companies have a more comprehensive knowledge of the factors affecting their business, such as metrics on sales, production, internal operations, and they can help companies to make better business decisions. Business Intelligence should not be confused with competitive intelligence, which is a separate management concept.
Business Intelligence is not a product nor a system. In fact it is an architecture and a collection of integrated functional and operational decision-support applications and databases that provides the business community easy access to business data. Of course it specifically focuses on the decision-support applications and databases as well.
Why is Business Intelligence important?
We all know that an organization is like the human body with close integration amongst the various organs of the body. We also know that there are communication problems, which create confusion amongst the various activities. Sometimes, what may seem as nonsense will be the process and common sense will be ignored. We also know that the top management may require something else and the middle and junior management provides something else, resists to provide or is unable to provide.
The top management drives the business direction, for a business to thrive, the top management has to make the correct decisions, correct decisions can only be made based on data available. The more relevant this data is, the better decision making process will be. Business Intelligence tools fulfill this requirement of businesses. Business intelligence applications and technologies can help companies analyze the following: changing trends in market share, changes in customer behavior and spending patterns, customers' preferences, company capabilities and market conditions. Business intelligence can be used to help analysts and managers determine which adjustments are most likely to affect trends.
BI systems can help companies develop consistent and "data-based" business decisions--producing better results than basing decisions on "guesswork." In addition, business intelligence applications can enhance communication among departments, coordinate activities, and enable companies to respond more quickly to changes (e.g., in financial conditions, customer preferences, supply chain operations, etc.). When a BI system is well-designed and properly integrated into a company's processes and decision-making process, it may be able to improve a company's performance. Having access to timely and accurate information is an important resource for a company, which can expedite decision-making and improve customers' experience.
In the competitive customer-service sector, companies need to have accurate, up-to-date information on customer preferences, so that the company can quickly adapt to their changing demands. Business Intelligence enables companies to gather information on the trends in the marketplace and come up with innovative products or services in anticipation of customer's changing demands. Business Intelligence applications can also help managers to be better informed about actions that a company's competitors are taking. As well, BI can help companies share selected strategic information with business partners. For example, some businesses use BI systems to share information with their suppliers (e.g., inventory levels, performance metrics, and other supply chain data). BI systems can also be designed to provide managers with information on the state of economic trends or marketplace factors, or to provide managers with in depth knowledge about the internal operations of a business.
History of Business Intelligence
Sun Tzu's The Art of War highlighted the importance of collecting and analyzing information. Sun Tzu claimed that to succeed in war, a general should have full knowledge of his own strengths and weaknesses and full knowledge of the enemy's strengths and weaknesses. Lack of either one might result in defeat.
Prior to the start of the Information Age in the late 20th century, businesses had to collect data from non-automated sources. Businesses then lacked the computing resources to properly analyze the data, and as a result, companies often made business decisions primarily on the basis of intuition.
As businesses started automating more and more systems, more and more data became available. However, collection remained a challenge due to a lack of infrastructure for data exchange or to incompatibilities between systems. Analysis of the data that was gathered and reports on the data sometimes took months to generate. Such reports allowed informed long-term strategic decision-making. However, short-term tactical decision-making continued to rely on intuition.
In modern businesses, increasing standards, automation, and technologies have led to vast amounts of data becoming available. Data warehouse technologies have set up repositories to store this data. Improved Extract, transform, load (ETL) and even recently Enterprise Application Integration tools have increased the speed of collecting the data. On Line Analytical Processing (OLAP) reporting technologies have allowed faster generation of new reports which analyze the data. Business intelligence has now become the art of sifting through large amounts of data, extracting pertinent information, and turning that information into knowledge upon which actions can be taken.
Business intelligence software incorporates the ability to mine data, analyze, and report. Some modern BI software allow users to cross-analyze and perform deep data research rapidly for better analysis of sales or performance on an individual, department, or company level. In modern applications of business intelligence software, managers are able to quickly compile reports from data for forecasting, analysis, and business decision making.
In 1989 Howard Dresner, a Research Fellow at Gartner Group popularized "BI" as an umbrella term to describe a set of concepts and methods to improve business decision-making by using fact-based support systems. Dresner left Gartner in 2005 and joined Hyperion Solutions as its Chief Strategy Officer.
Designing and implementing a business intelligence program
When implementing a BI programme one might like to pose a number of questions and take a number of resultant decisions, such as:
- Goal Alignment queries: The first step determines the short and medium-term purposes of the programme. What strategic goal(s) of the organization will the programme address? What organizational mission/vision does it relate to? A crafted hypothesis needs to detail how this initiative will eventually improve results / performance (i.e. a strategy map).
- Baseline queries: Current information-gathering competency needs assessing. Does the organization have the capability of monitoring important sources of information? What data does the organization collect and how does it store that data? What are the statistical parameters of this data, e.g. how much random variation does it contain? Does the organization measure this?
- Cost and risk queries: The financial consequences of a new BI initiative should be estimated. It is necessary to assess the cost of the present operations and the increase in costs associated with the BI initiative? What is the risk that the initiative will fail? This risk assessment should be converted into a financial metric and included in the planning.
- Customer and Stakeholder queries: Determine who will benefit from the initiative and who will pay. Who has a stake in the current procedure? What kinds of customers/stakeholders will benefit directly from this initiative? Who will benefit indirectly? What are the quantitative / qualitative benefits? Is the specified initiative the best way to increase satisfaction for all kinds of customers, or is there a better way? How will customers' benefits be monitored? What about employees,... shareholders,... distribution channel members?
- Metrics-related queries: These information requirements must be operationalized into clearly defined metrics. One must decide what metrics to use for each piece of information being gathered. Are these the best metrics? How do we know that? How many metrics need to be tracked? If this is a large number (it usually is), what kind of system can be used to track them? Are the metrics standardized, so they can be benchmarked against performance in other organizations? What are the industry standard metrics available?
- Measurement Methodology-related queries: One should establish a methodology or a procedure to determine the best (or acceptable) way of measuring the required metrics. What methods will be used, and how frequently will the organization collect data? Do industry standards exist for this? Is this the best way to do the measurements? How do we know that?
- Results-related queries: Someone should monitor the BI programme to ensure that objectives are being met. Adjustments in the programme may be necessary. The programme should be tested for accuracy, reliability, and validity. How can one demonstrate that the BI initiative (rather than other factors) contributed to a change in results? How much of the change was probably random?.
BI decision-support applications facilitate many activities, including:
Multidimensional Analysis is a data analysis process that groups data into two basic categories: data dimensions and measurements. For example, a data set consisting of the number of wins for a single football team at each of several years is a single-dimensional (in this case, longitudinal) data set. A data set consisting of the number of wins for several football teams in a single year is also a single-dimensional (in this case, cross-sectional) data set. A data set consisting of the number of wins for several football teams over several years is a two-dimensional data set.
In many disciplines, two-dimensional data sets are also called panel data. While, strictly speaking, two- and higher- dimensional data sets are "multi-dimensional," the term tends to be applied only to data sets with three or more dimensions. For example, some forecast data sets provide forecasts for multiple target periods, conducted by multiple forecasters, and made at multiple horizons. The three dimensions provide more information than can be gleaned from two dimensional panel data sets.
Another term for multidimensional analysis is multivariate analysis. So This term is derived from a specific aspect of this type of analysis, i would say, to analyze measures in another words facts from the perspective of multiple variables or characteristics. These variables (characteristics) usually describe business objects or dimensions. For example, in Adempiere Product Types like Item, Resource, Expense and Service describes product , Assume product being the business objects or dimensions. Occasionally, the variables can become dimensions in their own right. For example, the variables Product Type can be accepted as dimensions. In other words, a dimension can be built for a business object or for a variable of that business object as well.For More Info Click Here
Data mining capability requires building a BI decision-support application, specifically a data mining application, that is using a data mining tool. The data mining application can then use a sophisticated set of advanced components like artificial intelligence,pattern recognition, databases, traditional statistics, and graphics to present hidden relationships and patterns in Adempiere between system elements found in the organization's data pool.Data mining is the analysis of data with the goal of discovering gems of hidden information in the large quantity of data that has been captured in the running business. Data mining is different from statistical analysis, They both have strengths and weaknesses. For More Info Click Here.
Perform detailed analysis of the business problem or business opportunity to gain a solid understanding of the business requirements for a potential solution (product).Analysis of BI projects emphasizes business analysis rather than system analysis, and analysis is the most important activity when developing a BI decision-support environment.Business analysis issues such as defining the organization's information needs,identifying data sources, and analyzing the current and desired quality of data.Also Business analysis issues Define the business analysis issues and the information needed to meet the strategic business goals by stating the high level information requirements for the business.For More Info Click Here.
Balanced Scorecard, a concept for measuring a company's activities in terms of its vision and strategies, to give managers a comprehensive view of the performance of a business. The key new element is focusing not only on financial outcomes but also on the human issues that drive those outcomes, so that organizations focus on the future and act in their long-term best interest. The strategic management system forces managers to focus on the important performance metrics that drive success. It balances a financial perspective with customer, process, and employee perspectives. Measures are often indicators of future performance.
Since the original concept was introduced, balanced scorecards have become a fertile field of theory and research, and many practitioners have diverted from the original Kaplan & Norton articles. Kaplan & Norton themselves revisited the scorecard with the benefit of a decade's experience since the original article.
Implementing the scorecard typically includes four processes:
1. Translating the vision into operational goals; 2. Communicate the vision and link it to individual performance; 3. Business planning; 4. Feedback and learning and adjusting the strategy accordingly.
Data Visualization is the use of interactive, sensory representations, typically visual, of abstract data to reinforce cognition, hypothesis building and reasoning.
Information visualization is a complex research area. It builds on theory in information design, computer graphics, human-computer interaction and cognitive science.
Practical application of information visualization in computer programs involves selecting, transforming and representing abstract data in a form that facilitates human interaction for exploration and understanding.
Important aspects of information visualization are the interactivity and dynamics of visual representation. Strong techniques enable the user to modify the visualization in real-time, thus affording unparalleled perception of patterns and structural relations in the abstract data in question.
Querying, reporting, and charting
Business Intelligence require a series of queries be run on the data available within the organization. Just like the old saying, "Ask the right questions to get the right answers." A good BI tool should allow the end users to query the data effectively and intuitively.
The reports that the BI tool generates should be easily understood and the important parameters should clearly be visible.
Charting is a way of depicting the various business parameters in a graphical manner using various forms of charts.
Knowledge management comprises a range of practices used by organisations to identify, create, represent, and distribute knowledge for reuse, awareness, and learning across the organisations.
Knowledge Management programs attempt to manage the process of creation or identification, accumulation, and application of knowledge or intellectual capital across an organisation. Knowledge Management, therefore, attempts to bring under one set of practices various strands of thought and practice relating to:
- intellectual capital and the knowledge worker in the knowledge economy
- the idea of the learning organization
- various enabling organizational practices such as Communities of Practice and corporate Yellow Page directories for accessing key personnel and expertise
- various enabling technologies such as knowledge bases and expert systems, help desks, corporate intranets and extranets, Content Management, wikis and Document Management
Data mining (DMM), also called Knowledge-Discovery in Databases (KDD) or Knowledge-Discovery and Data Mining, is the process of automatically searching large volumes of data for patterns using tools such as classification, association rule mining, clustering, etc. Data mining is a complex topic and has links with multiple core fields such as computer science and adds value to rich seminal computational techniques from statistics, information retrieval, machine learning and pattern recognition.
A digital dashboard, also known as an enterprise dashboard or executive dashboard, is a business management tool used to visually ascertain the status (or "health") of a business enterprise via key business indicators. Digital dashboards use visual, at-a-glance displays of data pulled from disparate business systems to provide warnings, action notices, next steps, and summaries of business conditions.
Some benefits to using digital dashboards include:
* Elimination of duplicate data entry. * Ability to identify and correct negative trends. * Measure efficiencies/inefficiencies. * Ability to generate detailed reports showing new trends. * Increase overall revenues. * Ability to make more informed decisions based on collected BI (business intelligence) * Align strategies and organizational goals.
Forecasting is the process of estimation in unknown situations. Prediction is a similar, but more general term, and usually refers to estimation of time series, cross-sectional or longitudinal data. In more recent years, Forecasting has evolved into the practice of Demand Planning in every day business forecasting for manufacturing companies. The discipline of demand planning, also sometimes referred to as supply chain forecasting, embraces both statistical forecasting and consensus process.
Business Forecasting is typically data driven to have an ballpark estimate of Sales, Production, Demand Planning, Capacity Planning.
RoadMap In Progress
Now i am focusing on editing the proper Roadmap for Integrating Adempiere with Pentaho Open BI Suite.So the Adempiere map will be uploaded soon.