Business Intelligence (BI)


Business intelligence (BI) refers to skills, processes, technologies, applications and practices used to support decision making. BI technologies provide historical, current, and predictive views of business operations. Common functions of Business Intelligence technologies are reporting, OLAP (Online analytical processing), analytics, data mining, text mining, predictive analytics, business performance management and benchmarking.

BI is the number one investment priority among CIOs. Business intelligence connects people with information in an easy-to-use way so they can make better decisions. With BI solutions you can:
  • Set targets, see results and understand what drives the numbers.
  • Identify trends that may be benefits or threats.
  • Take action with a common context for decision-making across every department.
  • Identify and analyze opportunities, and trends.
Initially, users expected ERP systems to provide needed reporting. When these systems couldn’t meet requirements due to backlog and overload, users turned to data warehouses. Traditional BI satisfies most strategic reporting and analysis, but not real-time operational reporting with its associated needs for high-volume real-time data updates, high availability needs and high throughput rate of operational queries. Operational reporting has high overhead and often ties up data warehouses, preventing other analytics from running. However, we are seeing operational reporting as a top business initiative, and increasing interest in the use of a data provisioning platform as companies need to extend data warehouses to more operational use.

Common Functions

  • Reporting
  • OLAP (Online Analytical Processing)
  • Analytics
  • Data Mining
  • Text Mining
  • Predictive Analytics
  • Business Performance Management
  • Benchmarking
OLAP (Online Analytical Processing)

OLAP is an approach to quickly answer multi-dimensional analytical queries. 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 and hierarchical databases that are faster than relational databases. The output of an OLAP query is typically displayed in a matrix (or pivot) format. The dimensions form the rows and columns of the matrix; the measures form the values.

The typical applications of OLAP are in business analytics and reporting for:
  • Sales and Marketing
  • Budgeting and Forecasting
  • Business Process Management
  • Financial Reporting
Predictive Analytics

Predictive analytics encompasses a variety of techniques from statistics, data mining and game theory that analyze current and historical facts to make predictions about future events. In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions.

The following are areas that predictive analytics has shown positive impact in recent years:
  • Analytical Customer Relationship Management (CRM)
  • Cross-Sell
  • Customer Retention
  • Direct Marketing
  • Collection Analytics
  • Fraud Detection
  • Underwriting
  • Clinical Decision Support Systems
  • Portfolio, Product or Economy Level Prediction

Business Challenges

Organizations worldwide demonstrate that BI improves the decision making process and directly improves the bottom line. These organizations also show that the value of business intelligence increases as more decision makers access complete, consistent, and trustworthy information. And yet, these same smart companies often find business intelligence adoption lagging among business users.

Business intelligence epitomizes the classic struggle between IT and business. Business users will not embrace business intelligence until they can get it on their own terms. They protest that they can't wait for IT and need direct access to information. They say they need to see things their own way. They may say BI is too hard too use and they prefer a different interface.

Still, IT has its own important job to do. IT delivers the platform that secures information, integrates data sources, provides a foundation that masks the complexity of heterogeneous data sources and provides complete, consistent access to information throughout the enterprise. Only IT can ensure the security, availability and reliability of business intelligence.

Bridging this gap between business and IT is what enables successful companies to realize the full promise of business intelligence. Business users need simpler user interfaces that enable them to engage in the business and find answers to their own business questions. IT needs the simplicity of a business intelligence solution that fits with key infrastructure and architecture and enables them to cost effectively scale. They also need to know that the platform aligns with strategic plans for enterprise architecture.

Limits of Traditional BI

BI tools are widely used to complement enterprise applications. Application specific BI tools do a great job of surfacing after-the-fact data captured in, say, an ERP or SCM application. Transactional data is stored and aggregated in a repository, such as an operational data store, for reporting, analysis and visualization. Application BI comes preconfigured to support the data schemas, application programming interfaces (APIs), security mechanisms and business logic of the particular transactional system. This approach speeds the extraction of information and helps maintain the integrity of source applications. Unfortunately, it also limits the scope of BI in decision making.

Another example of unnecessarily limited BI utilization occurs when data from multiple applications is homogenized in a data warehouse. In this situation, reporting and analytics provide enterprise-wide visibility, but the data loses business applicability – that is, the ability to facilitate collaboration and support decision-making in the context of a specific business process. An example of this is the different ways customer data appears within a data warehouse and outside of it. Data homogenized from sales force automation software (sales opportunity data) and a separate order management system (customer order data) provides good insight into customer buying trends and fits well in the structure of a data warehouse. However, it is missing the granular customer contact data required for the business process of escalation.

Traditional BI also has a latency problem: The information is not available quickly enough to support individuals making immediate decisions. Often the stored data is out of date for operational response; for example, a bank today needs to complete loan approvals in hours, not days. Hourly and nearreal-time information is important for operational responsiveness to such competitive pressures. Also, traditional BI information is not delivered in the context of a business process, but rather at the individual, entity or activity level. Information should be in the context of causal activities preceding a situation and consequential activities following it. The latency and context issues compound another problem: BI information is not always actionable. Individuals receiving analytic information also need options for possible next steps. For example, call center managers must be given information that is timely (such as that call volumes are in excess now), and notification must be in a form that supports an active response (telling where to route those alls).

The future of BI

A 2009 Gartner paper predicted these developments in business intelligence market:
  • Because of lack of information, processes, and tools, through 2012, more than 35 percent of the top 5,000 global companies will regularly fail to make insightful decisions about significant changes in their business and markets.
  • By 2012, business units will control at least 40 percent of the total budget for business intelligence.
  • By 2010, 20 per cent of organizations will have an industry-specific analytic application delivered via software as a service as a standard component of their business intelligence portfolio.
  • In 2009, collaborative decision making will emerge as a new product category that combines social software with business intelligence platform capabilities.
  • By 2012, one-third of analytic applications applied to business processes will be delivered through coarse-grained application mashups.

Top trends in BI for 2009

IDC estimated, most analysts see business intelligence (BI) as a relative bright spot in the IT spending forecast, with a projected growth rate of between 2% and 10% in 2009 (vs. the 9–12% projected in early 2008).
  • BI increases in importance and impact making data governance and data quality more critical than ever;
  • BI buyers are more scrutinizing;
  • Market demands lower BI complexity;
  • Analytics moves to the front office — More sophistication in the hands of business users;
  • Data integration focus gaining new momentum;
  • The line is blurring between data warehouse, operational data store (ODS) and operational systems;
  • Convergence of structured and unstructured data;
  • Complex Event Processing (CEP) comes of age;

Decision factors of BI implementation

As Per Dr. Saadia Asif (2009), following are the factors that affect the decision making process of an BI implementation:
  • Reporting and Analysis Tools
    • Features and functionality
    • Scalability and deployability
    • Usability and manageability
    • Ability to customize
  • Databases
    • Scalability and performance
    • Manageability and availability
    • Security and customization
    • Ability to write back
  • ETL Tools
    • Ability to read any source
    • Efficiency and productivity
    • Cross platform support
  • Costs involved
    • Hardware costs (actual or opportunity)
    • Costs of software (ETL, databases, applications and front-end)
    • Internal development costs
    • External developments costs
    • Internal training
    • Ongoing maintenance
  • Benefits
    • Time savings and operational efficiencies
    • Lower cost of operations
    • Improved customer service and satisfaction
    • Improved operational and strategic decision making
    • Improved employee communications and satisfaction
    • Improved knowledge sharing

Critical Success Factors of BI implementation

Although there could be many factors that could affect the implementation process of a BI system, a research by Naveen shows, the following are the critical success factors for an business intelligence implementation:
  • Business driven methodology & project management
  • Clear vision & planning
  • Committed management support & sponsorship
  • Data management & quality issues
  • Mapping solutions to user requirements
  • Performance considerations
  • Robust & extensible framework

How to choose BI solutions?

For a single business intelligence solution to fulfill the full promise of BI, it must access all pertinent performance data, regardless of platform, and deliver the resulting information and analysis to all appropriate users, regardless of location. It can access virtually any corporate data source. And it provides detailed, understandable views of that data for all users, from executives to analysts to casual BI users, with innovative tools that allow them to access the information from mobile devices (like iPhone) while on the road.

The full promise of BI means that neither IT nor business users feel limited by their BI systems. Business users need to see relevant information about their business in ways that are the most meaningful to them and the easiest to understand. They need to see the big picture and, when necessary, the detail. They do not want to drown in irrelevant information.

At the same time, IT's ability to control costs and deliver value should not be undermined by maintaining multiple BI systems. IT does not want BI that only covers some of the data. And they need software that has room to grow to meet tomorrow's demands.

Best BI software:
  • Delivers trusted information for a single version of the truth.
  • Lets you work with information the way you want—reports, dashboards, scorecards.
  • Puts tools in your hands to author and share information as you require.

How our BI solutions can help?

Our BI solutions meet the needs of business and IT, and address the demands of business for usability, accessibility and control. Our BI solutions open windows into all your corporate systems and data. BI system managers, professional authors, business and financial analysts, line of business managers, executives, and casual BI users can all find value through these multiple windows. We can deliver the best available value to all users with one business intelligence platform - making it simpler, faster and easier to manage.

This agility enables BI to cost effectively scale with:
  • A full range of BI capabilities: All user communities receive relevant information how, when and where it is needed.
  • An open enterprise-class platform: IT leverages a platform that cost effectively scales to meet growing user demands.
  • Frameworks and proven practices: Projects get off to the right start with expertise to ensure success on the journey to performance management.