Business Intelligence

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What Does Business Intelligence Mean?

Business intelligence (BI) is the set of techniques and tools for the transformation of raw data into meaningful and useful information for business analysis purposes. BI technologies are capable of handling large amounts of unstructured data to help identify, develop and otherwise create new strategic business opportunities. The goal of BI is to allow for the easy interpretation of these large volumes of data. Identifying new opportunities and implementing an effective strategy based on insights can provide businesses with a competitive market advantage and long-term stability.[1]

The Process of Business Intelligence[2]

Reporting and analysis are the central building blocks of business intelligence and the arena in which most BI vendors compete by adding and refining features to their solutions. The general process of business intelligence is as follows:

  • Gathering data and organizing it through reporting: The raw material of business intelligence is the data that records the daily transactions of an organization. Data may come from such activities as interactions with customers, management of employees, running of operations, or administration of finance. According to the traditional model, data from the daily transaction is recorded in three main transactional databases: customer relation management (CRM), human resource management (HRM), and enterprise resource planning (ERP). For instance, a sales transaction would be recorded and stored as a piece of data in the CRM database. A piece of data, in itself, is neutral–i.e. neither “good” nor “bad.” For instance, if you knew that rep X had received Y dollars worth of orders year to date, you wouldn’t necessarily know whether it’s a cause of panic or celebration. Just like raw material, data needs to be processed through analysis to become meaningful. The same piece of data in the example above would become meaningful (for instance) if compared to the year-to-date sales target for rep X. By doing this, the piece of data has become part of the process of analysis.
  • Turning it into meaningful information through analysis: Analyzing data means asking it questions and getting meaningful answers. For example, the simple command “sort in descending order” on a column of data in Excel representing year-to-date orders taken by a sales rep would answer the questions “Who is taking the most orders? The least orders?” The sort command has contextualized the data, making it much more meaningful in terms of the strategic goals of the business. Of course, analysis in BI is much more complex and varied than this. The powerful and interactive analysis tools of today’s better business intelligence solutions make it easier to ask data an increasing number of questions and get meaningful answers–including “what-if” scenarios, multidimensional slicing and dicing (XOLAP analysis), mashing up of data with geographic mapping and much more. For example, data analysis features can answer such questions as:
    • How is my product performing by product line, by territory, or by demographics?
    • What is the untapped potential of sales territory X?
    • What would be the likely impact of revenue if I eliminated territory Y and relocated Y’s rep to territory X?
    • Are my reps balancing face time with their customers with “windshield time” in an efficient way? Is there a way to improve this?

In any case, the goal of even the most sophisticated analysis features is always the same: enabling decision-makers to understand data, to spot patterns between numbers, to identify trends and the reasons behind them–simply put, to contextualize data and answer questions about it.

  • Making actionable decisions aimed at fulfilling a strategic goal: Interestingly, most BI projects fail not because of faulty technical implementation, but because of a lack of a strategic focus. Business intelligence should be a lever that enables a company to “lift” itself more efficiently toward its strategic goals. But all too often, BI becomes an end-in-itself proposition, with project managers, CIOs, or CTOs failing to look at it in light of the company’s mission.

Tools and Features of Business Intelligence[3]

As the term Business Intelligence (BI) mainly refers to the collection of processes, technologies, and tools used to gather, store, access, and analyze data about a company, there are a number of useful tools that a company could approach to build up its Business Intelligence (BI) capabilities. According to Robert J. Thierauf (2001, p. 163), a mixture of Business Intelligence (BI) tools could include data warehouse, data mining, enterprise application integration (EAI), development tools, OLAP (On-line Analytical Processing), pure analytical applications and enterprise resource planning (ERP) and so on. With the provision of Pivot table, Cross Tabs, Custom calculations, Query Wizard Expertise, Layout Comments, and analytical features like ranking, filtering, sorting, group, and so on ( 2011), Business Intelligence (BI) is receiving an enhanced and increased popularity among the business entities and many industries. In the following, we will analyze the advantages and disadvantages of Business Intelligence (BI) with relevance to business performance.

Business Intelligence Model
Business Intelligence Model
source: University of Toronto

Benefits of Business Intelligence[4]

  • Reduced labor costs: The most tangible benefit of BI is the time and effort saved by manually producing standard reports for the organization. It is rarely the largest benefit though. However, because it is so tangible it is often part of the equation when a decision must be made about implementing BI, and if it turns out that these savings alone can justify the BI system, then it is the easiest way to justify it. BI systems reduce labor costs for generating reports by:
    • automating data collection and aggregation
    • automating report generation
    • providing report design tools that make programming new reports much simpler
    • reducing training needed for developing and maintaining reports
  • Reduce information bottlenecks: The BI system allows end-users to extract reports when they need them rather than depending on people in the IT or finance department to prepare them. The BI system will even allow authorized users to design new reports to match their requirements. BI systems reduce information bottlenecks by:
    • providing individualized, role-based dashboards that collect the most important data for daily operations
    • letting the user open and run reports autonomously
    • providing documentation of KPIs and other information
    • allowing users to analyze and validate the data without involving IT specialists
    • allowing users to create new views of data as needed
  • Make data actionable: What happens when employees in an organization get too much data, too little data, too old data, too detailed data, or just irrelevant data? Nothing. Nothing happens. Everybody is just wasting time and resources. Most organizations use extensive amounts of resources to put together piles of standard reports that are delegated throughout the company. When employees try to find the head and tail of the data they even often find that the numbers are not comparable between different reports and end up analyzing the differences instead of interpreting the actual numbers. And since trust in data is lost, nobody dares to make a decision based on the numbers.

BI systems make information actionable by:

    • providing information through unified views of data where KPIs are assembled and calculated using a central repository of definitions - a data model - to prevent conflicting definitions and incomparable report data
    • providing to-the-minute information in real-time reports that show the state of the business in this very moment - not a historical view of how it looked days or weeks ago
    • allowing users to search and design reports autonomously instead of being dependent on specialists in the IT department
    • showing data in a context, e.g. by benchmarking KPIs values against comparable values (e.g. averages, budgets/target, and last period) to let the user interpret whether the KPI value is acceptable or needs corrective action
    • using rules to highlight KPI thresholds as "good" or "bad"
    • providing integrated documentation to help the user understand the meaning and definition of the KPIs
    • providing links back to the operational systems that make it easy for the user to carry out corrective actions (closed loop) making data collaborative, e.g. let the user forward and share selected data with other users and assign targets and responsible persons to KPIs
    • only showing data relevant to the specific user in a role-based environment to avoid "Information overload"
    • showing data on a high, aggregated level where overall trends can be easily spotted and then letting the user drill down to detail data in a top-down manner
    • using intuitive visualizations that enhance the nature of the data such as graphs/charts and gauges
    • forwarding relevant information based on the occurrence of predefined events, i.e. only sending certain reports when specific business events occur, such as too-high stock levels, customer churn, etc.
    • shortening the analyze-decision loop to avoid losing the train of thought
  • Better decisions: Decisions need to be made every day and, as we all know, decisions have varying qualities. Good decisions can provide tremendous benefits. Bad decisions provide no benefits - they may even cause you losses. BI systems help make better decisions by:
    • providing decision-makers with rich, exact, and up-to-date information
    • letting users dive into data for further investigation
      In this context the term decision maker needs to be seen from a broad perspective; it is not only management that makes decisions. In fact, the decisions that affect an organization the most are those made by people all over the organization, from the salesperson who decides to give a customer a discount to the procurement assistant who decides to buy certain products for inventory.
  • Faster decisions: A decision can be made the moment you have all the relevant information in your hands. In other words, the faster the relevant information gets into your hands the faster you can make a decision. Fast decisions are important for two reasons:
    • It makes the organization more responsive to threats and opportunities
    • It shortens the time between thought and action. Most people will lose their train of thought if they need to wait a long time for further information about the problem they are dealing with.
      BI systems enable fast decisions by:
    • combining multiple data sources in common reports, thus saving the user from manually combining data in spreadsheets, etc.
    • providing analytical and ad-hoc reporting capabilities that allow users to quickly retrieve new or different combinations of data as needed instead of having to request new reports in the IT or finance departments.
    • providing reduced system response times by using pre-aggregated data or other techniques for fast data aggregation
  • Align the organization towards its business objectives: The most successful organizations are those that succeed to make every person in the organization work toward a common goal.
    BI systems help organizations align all parts of the organization towards common business objectives by:
    • centralizing KPI definitions. BI reports don't calculate KPIs using autonomous queries and scripts. They retrieve KPI values and definitions through a central repository and thus prevent conflicting KPI definitions and values
    • guiding information presentation using advanced visualizations, benchmarks, and KPI thresholds thus ensuring a common interpretation of the KPIs
    • providing a single source of information. All reports collect their data from one source - the BI system.
    • "pushing" selected information throughout the organization. By enabling organizations to push KPIs and other information to the end-users the BI system helps focus employees' attention on the most critical success factors.
    • assigning targets to KPI values for each organizational unit to be used for measuring the ability to achieve the goals set forth and thus pushing the organization towards the defined goals
  • New insights: Traditional reporting systems aim to give users data according to a fixed and predefined structure. This rigid approach gives the organization answers to exactly the questions it is able to specify in advance. And no more. Modern business intelligence systems on the other hand provide ad hoc query capabilities that allow users to poke randomly around in data to get answers to any question that comes to their mind. This allows users to strengthen their understanding of the underlying patterns of the business and thus gain new insights into the dynamics that lead to success or failure. Such analysis is often referred to as OLAP: Online Analytical Processing. In another application, some BI systems provide special mathematical algorithms for finding hitherto unknown patterns in data - so-called data mining. Such algorithms comprise Cluster Analysis, Decision Trees, Neural Networks, and Rule Induction.

See Also

Business Intelligence (BI) involves technologies, applications, strategies, and practices for the collection, integration, analysis, and presentation of business information. The objective of BI is to support better business decision-making. Essentially, BI systems enable data-driven decision-making processes based on factual data and analytical findings. These systems can include historical, current, and predictive views of business operations, often using data gathered from various sources within and outside the organization to provide a comprehensive view of business performance.

  • Data Warehouse: Discussing centralized repositories of integrated data from one or more disparate sources, which store current and historical data in one place for the creation of analytical reports for knowledge workers throughout the enterprise.
  • Data Mining: Covering the process of discovering patterns and knowledge from large amounts of data. The data sources can include databases, data warehouses, the internet, and other sources.
  • Data Analytics and Data Science: Explaining how analytical methodologies and scientific principles are applied to data sets to create meaningful insights and predictions based on data.
  • Dashboard and Data Visualization Tools: Discussing tools and software applications for visually analyzing, tracking, and displaying key data points, metrics, and KPIs by presenting them in an intuitive and interactive visual format.
  • Predictive Analytics: Covering techniques that use historical data to predict future events. Typically, historical data is used to build a mathematical model that captures important trends.
  • Big Data: Explaining large or complex data sets that traditional data processing applications are inadequate to deal with. Big Data challenges include capturing, storing, analysis, data curation, search, sharing, transfer, visualization, querying, updating, and information privacy.
  • ETL (Extract, Transform, Load): Discussing the process of copying data from one or more sources into a destination system which represents the data differently from the source(s) or in a different context than the source(s).
  • Business Performance Management (BPM): Covering the process of monitoring and managing an organization's performance according to key performance indicators such as revenue, ROI, overhead, and operational costs.
  • Decision Support System (DSS): Explaining computerized programs used to support determinations, judgments, and courses of action in an organization or a business.
  • Market Intelligence: Discussing the information relevant to a company’s markets, gathered and analyzed specifically for the purpose of accurate and confident decision-making in determining market opportunity, market penetration strategy, and market development metrics.
  • Customer Intelligence (CI): Covering the process of gathering and analyzing information regarding customers; their details and their activities, in order to build deeper and more effective customer relationships and improve strategic decision-making.
  • Competitive Intelligence (CI): Explaining the action of defining, gathering, analyzing, and distributing intelligence about products, customers, competitors, and any aspect of the environment needed to support executives and managers in strategic decision-making for an organization.


Further Reading