The Business Intelligence

Online Analytical Processing (OLAP)

2009-12-05T00:10:00.000-08:00

OLAP or online analytical processing is another component of BI. It enables a user to easily and selectively extract and view data from different points-of-view. OLAP data is stored in a multidimensional database. The main component of OLAP is the OLAP server, which sits between client and database management systems (DBMS). OLAP tools enable users to analyze different dimensions of multidimensional data. For example, it provides time series and trend analysis views. OLAP often is used in data mining.

Online Analytic Processing is the capability to store and manage the data in a way, so that it can be effectively used to generate actionable information.

OLAP provides the building blocks to enable analysis (like rich functions, multi-dimensional models, analysis types). Mostly the end-user tools (like business modeling tools, Data mining tools, performance reporting tools..), which sit on top of the OLAP to provide rich user Business Intelligence interface.

Most OLAP systems typically utilize one or many of the following three storage paradigms to support multidimensional analysis. Let’s take closer look at each of these:-

MOLAP:-

The “M” in MOLAP refers to Multidimensional. Multidimensional OLAP (MOLAP) stores the aggregated values in a multidimensional structure on the OLAP server.

An aggregated value is a pre-calculation of a cell within an OLAP cube defined by its multidimensional definition. Aggregations are pre-calculated summaries of data that improve query response time by having the answers ready before the questions are asked. For example, when a data warehouse fact contains hundreds of thousands of rows, a query requesting the weekly sales totals for a particular product line can take a long time to answer if the fact table has to be scanned to compute the answer.

The benefit of a MOLAP storage mechanism is query speed. The retrieval of information from a MOLAP cube is fast, especially when compared to a ROLAP storage mechanism.

ROLAP:-

The “R” in ROLAP refers to “relational.” Relational OLAP (ROLAP) stores aggregations in a relational structure and leaves the partition’s source data in its existing relational structure.

This architecture places a heavy burden on the relational database system (RDBS) where the source tables for the OLAP cubes is stored. Every query issued against the OLAP information first passes through the OLAP definition, then is processed by the underlying RDBS prior to returning a result.

The primary driver to use ROLAP is data storage limitations. The benefit of ROLAP is twofold. First, there is very little additional disk storage needed to accommodate a ROLAP implementation. Unlike MOLAP, the ROLAP storage mechanism doesn’t cause data explosion. Second, the processing time is very short when compared to MOLAP. ROLAP does not aggregate (pre-calculate) the data available in a cube, so there is very little processing that occurs when creating the cube.

HOLAP:-

The “H” is for “hybrid.” Hybrid OLAP (HOLAP) stores aggregations in a multidimensional structure on the OLAP server and leaves the partition’s source data in its existing relational structure.

In the hybrid model, the aggregated (or pre-calculated) values are stored in the MOLAP section and queries that can be answered by the aggregated values have fast response times. When a query retrieves information below the aggregated values and accesses the ROLAP section, response time slows down as the relational system retrieves the data prior to passing it back through the OLAP system.

For the Business users the storage mode is transparent – or at least it should be. Business people don’t care whether an OLAP system uses MOLAP, ROLAP or HOLAP. What business users will pay attention to is how much effort they need to expend and how long they have to wait in order to perform fast and flexible analysis of their data.

Dashboards

2009-11-28T22:23:00.000-08:00

A business intelligence dashboard is a user interface that somewhat resembles an automobile's dashboard and organizes and presents information in a way that is easy to read. However, a computer dashboard is more likely to be interactive than an automobile dashboard. To some extent, most graphical user interfaces (GUI) resemble a dashboard.

Dashboards provide "at-a-glance" information about business performance across the enterprise. They are typically generated for managers and executives who need an overall view of business performance.

Dashboards provide alignment, visibility and collaboration across the organization by allowing business users to define, monitor and analyze business performance via key performance indicators (KPIs).

Dashboards are the summarize collections of relevant metrics on single computer screen. It also shows big picture and all the details. With the dashboard user gets the ability to navigate with one interface. Dashboards are visually interactive.

Data Visualization

2009-11-27T22:22:00.000-08:00

Data visualization tools help users to see data clearly. It is the graphical representations of data, including complex three-dimensional data pictures. Data visualization tools interpret information and data relationships. It combines representations of multiple data sets simultaneously, or gives multiple views of a single data set, even a set that encompasses millions of data points. Tools include of data visualization include charts and graphs, dashboards, and scorecards.Data Visualization technique helps user or knowledge worker to get the snapshot of the data. It helps the user to analyse the data properly without breaking their head in the report, which shows only the number i.e. user will get off from the legacy reports and reporting system.

History and Current Status...

2009-11-23T07:42:00.000-08:00

In seventies and eighties, accessing information systems was very tedious and it was rarely permitted to end users. Query and reporting was cumbersome and analytical reporting was spreadsheet based. Reports printed from mainframe computers were infamously poor at pinpointing critical information. Moreover, they often arrived on managers’ desks a week or so after month-end, and were a far cry after the fact that they were “rear view” reports. The whole process of accessing information was time consuming, and delayed reporting didn't achieve results.

During that period reporting systems was static, two dimensional, and had no analytical capabilities. The demand for dynamic multidimensional reporting systems for predictive and intelligent decision-making pushed BI to develop.

In nineties, organizations have stored huge amounts of data by building online transaction processing (OLTP) systems and ERP systems, call centers, and the Internet. In pursuit of better data management enterprises build data warehouses (DW), data marts, and installed extract transform load (ETL) tools to work with data warehouses. But very few of these data were processed into information, and even less was used for decision support systems, largely because of the lack of tools to access and analyze the data for business users

ERP systems have positively transformed many enterprises’ business processes, yet many users feel oversold because ERP appears to inhibit access and lock up vital information. In most traditional ERP systems operational activities are grouped together to form artificially created processes, which bear little resemblance to the actual business activities. For example, the focus of ERP has often been to get the correct figures into the general ledger (GL) and create a transactional glut.

Conversely, BI, which has for few decades been called executive information systems (EIS), offers a new breed of similar, but more insightful and functional tools to help enterprises operate more efficiently and profitably. Many manufacturing and distribution enterprises of all sizes are amenable to leveraging software that would not only sense the daily pulse of the operations, but would also spot incongruities, analyze the performances of multiple areas, and initiate corrective adjustments. BI tools promise to help “rank and file” employee’s harness data too complicated for manual manipulation. For instance, few departments are as hard pressed for new tools as purchasing and sourcing, where rapid increases in materials costs, greater deviations in lead times, and supplier base growth and instability require ever increasing buyer dexterity. BI can provide this dexterity.

The demand for dynamic multidimensional reporting systems for predictive and intelligent decision-making pushed BI to develop. With the advent of new technologies and applications, BI came to its present state and is continuing to grow every day. BI today is capable of multidimensional analysis of data to see 360 degree business insight, statistical analysis, and forecasting to help better decision support systems.

The advent of technology and the increasing demand from companies, regulatory concerns to have better information, pushed BI systems to evolve.

Nowadays, popular uses of BI include management dashboards and balanced scorecards, collaborative applications, workflows and alerts, analytics, enterprise reporting, financial reporting, and customer and partner extranets. These solutions, some of which will be described in more detail later in this report, will enable companies to gain visibility into their business, acquire and retain profitable customers, reduce costs and access time. They will also be able to detect patterns, optimize the supply chain, analyze project or product portfolios, increase productivity, and improve financial performance.

Furthermore, most of the BI software vendors have now released web-based versions of their solutions. Companies can now easily and at a low cost give users access to large amounts of corporate data and sophisticated analytical tools. By providing access to the Internet or an intranet connection, a person can investigate and analyze data from home, when traveling, or from any other location at which they may happen to be.

Why Business Intelligence ?

2009-11-23T07:33:00.000-08:00

BI has a tremendous impact on business once installed. It produces the right information at the right time, which is key element for the success of any business enterprise. BI is the art of knowing and gaining the business advantage from data. Whether it is marketing competition, customer retention, inventory control, financial modeling, or even in national security, BI is the answer. BI can answer a company’s critical questions such as, why market shares are going to competitors; which products contribute the most to profit; how can business become more profitable; why some divisions are not profitable; which plants produce at the lowest cost; how can productivity improve; which parts of the world are the most profitable; who are best and worst customers; where is money being lost or made, etc.

BI answers these questions by analyzing and comparing business historical data. Data is created by business activities or data from outside sources like environmental, demographic, immigration data, etc. to study a particular group of people or customers. Such information is used by businesses to understand their business trends, their strengths and weakness, and to analyze competitors and the market situation. The information can also be used by government or secret agencies, especially like US Homeland Security, that need to have access to financial, immigration, transportation, and any kind of related data that can be analyzed to determine probable attacks on its citizens or property.

In addition to determining trends, another push to implement BI comes from, the Sarbanes-Oxley legislation, which affects corporate financial reporting, and accounting rules for publicly-held companies. To be in compliance with Sarbanes-Oxley, BI systems will be needed to insure the timely and accurate analysis of business data. Thus real time BI is not only relevant but key to achieving compliance.

Since business intelligence is a multifaceted concept, we will examine it from 3 different perspectives:

Making better decision faster:
The primary goal of business intelligence is to help people make decisions that improve a company’s performance and promote its competitive advantage in the marketplace. In short, business intelligence empowers organizations to make better decisions faster.

In the best of all worlds, managers, from the lower lead supervisor to the CEO, make decisions by considering their experiences, their understanding of the business, their business plan and information. Often the experience, understandings, and strategies that go into decision making are pretty static; that is they change very slowly. Making better decisions means improving any or all parts of the process; this also results in fewer poor decisions and more superior ones. Better decisions result in better achievement of corporate objectives, such as improving share holders value.

Converting Data into information:
To make better decision faster, executives and managers need relevant and useful facts at their fingertips. But there is often a large gap between the information that decision makers require and the mountains of data that business collect every day. We call this as analysis gap.

To bridge this analysis gap, organizations make significant investments in the development of BI systems to convert raw data into useful information. The most effective BI systems access huge volumes of data and deliver relevant subsets instantly to decision makers. Some call this “analysis at the speed of thoughts”.

BI solutions at the enterprise level are charged with collecting and reporting a company’s most important metrics sometimes called key performance indicators (KPIs).

Using rational approach to management:
Business intelligence can be described as an approach to the management. People and organization adopt the BI attitude because of belief that a fact-based, rational approach to making decisions, to the extent this is possible, is basically a good thing.

The BI attitude is characterized as follows:-

Seeking objective measurable quantitative facts about the business
Using organized methods
Inventing and sharing methods that explain the cause and the effect relationships between operational actions and the effects theses have on reaching the goals of the business
Experimenting with alternate approaches and monitoring feedback on results

What is Business Intelligence (BI)?

2009-11-22T05:06:00.001-08:00

BI is neither a product nor a system. It is an umbrella term that combines architectures, applications, and databases. It enables the real-time, interactive access, analysis, and manipulation of information, which provides the business community with easy access to business data.

Businesses generate a sea of data. Every datum carries a small piece of the business’ story. This data is scattered everywhere, in disparate systems like enterprise resource planning (ERP), customer relationship management (CRM), supply chain management (SCM) etc. It is held captive in dead hard drives, and can even be situated in geographically different regions. However, it is in data, where the true nature of business—its trends, strengths, and weaknesses—lie. BI gathers all the related data to turn it into information and information that is analyzed properly can be used for decision making which can finally go into action. In other words, BI transforms data into information, information into decisions, and decisions into action.

Thereby, BI doesn’t produce any data, but it uses data produced by other business applications like enterprise resource planning (ERP), customer relationship management (CRM), supply chain management (SCM) etc. All of these are a part of BI. They are all dependent on BI tools, but it should be noted that BI is not dependent on them.

BI analyzes historical data—the data businesses generate through transactions or by other kinds of business activities—and helps businesses by analyzing the past and present business situations and performances. By giving this valuable insight, BI helps decision makers make more informed decisions and supplies end users with critical business information on their customers or partners, including information on behaviors and trends.

Introduction

2009-11-22T05:00:00.001-08:00

Business intelligence (BI) is a big buzzword in today’s IT world. It is a concept understood by some, but misunderstood by many.

The purpose of this report is to clarify what BI is and to discuss its main components, present Market scenario in

India

In today’s highly competitive business, the quality and timeliness of business information for an organization is not the choice between profit and loss, it is a question of survival or bankruptcy.

Today’s information environment is forcing organizations to reach for a new level of integrated analytics to stay competitive in the market place. With the integrated analytics, organizations will be able to aggregate, analyze and share information from and with sources inside and outside the organizations. In the future successful organizations will gain sustainable competitive advantage by creating an environment that integrates all analytics across the entire value chain quickly and efficiently.

The market for Business Intelligence tools is still in the development stage. Companies that have implemented applications such as ERP, SCM, CRM etc, often face a situation where they have a huge amount of data and information but no helping tool or clearly defined MAP, which can be put to use for making strategic decisions.

The goal with improving your organization’s business intelligence processes and tools should be to support financial leadership. Before you dig into the details of BI, we therefore hope that this blog can be an important building block for you to help support your corporation’s growth and success.

This means that your BI initiative should be part of a bigger picture, which includes the following objectives:

Creating a vision for financial leadership
Coaching the organization to set realistic goals
Supporting optimal decision making

Good BI tools are needed to improve the decision-making process. Historically (and still true for the vast majority of companies today), companies have spent too much time closing their books and preparing data and financial reports, and too little time on analysis and review. This is causing a disconnect between the analysis and review process and action (decision making).

After having spent the last decade implementing enterprise resource planning software and other solutions that support their operations, companies now have large databases with transactional data sitting in their computer rooms. Thousands of consulting hours and sometimes millions of dollars later, the return-on-investment scorecards range from poor to very good. However, one desire is common across all industries: to be able to quickly and easily analyze the data i.e. financial, operational etc in the corporate databases in order to be able to make more intelligent decisions about the future. Transactional data is good for keeping track of what is happening in an organization, but is not well suited to finding out why things are happening or predicting future performance. In other words, after years of putting potentially valuable data into your corporate databases, it is now time to put the tools in place to get the data out of the same systems and organize it in useful ways to support the decision-making process.