Business Intelligence

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Business Hanamaki Intelligence

Business intelligence (BI) can be described as “a set of techniques and tools for the acquisition and transformation of raw data into meaningful and useful information for business analysis purposes”.[1] The term “data surfacing” is also more often associated with BI functionality. BI technologies are capable of handling large amounts of structured and sometimes 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.

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.

Components

Business intelligence is made up of an increasing number of components including:

  • Multi-dimensional aggregation and allocation
  • De-normalization, tagging and standardization
  • Real-time reporting with analytical alert
  • A method of interfacing with unstructured data sources
  • Group consolidation, budgeting and rolling forecasts
  • Statistical inference and probabilistic simulation
  • Key performance indicators optimization
  • Version control and process management
  • Open item management

Applications in an Enterprise

Business intelligence can be applied to the following business purposes.

  • Measurement – program that creates a hierarchy of performance metrics  and benchmarking that informs business leaders about progress towards business goals (business process management).
  • 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, data lineage, complex event processing and prescriptive analytics.
  • 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.
  • Collaboration/collaboration platform – program that gets different areas (both inside and outside the business) to work together through data sharing and electronic data interchange.
  • 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 the above, business intelligence can provide a pro-active approach, such as alert functionality that immediately notifies the end-user if certain conditions are met. For example, if some business metric exceeds a pre-defined threshold, the metric will be highlighted in standard reports, and the business analyst may be alerted via e-mail or another monitoring service. This end-to-end process requires data governance, which should be handled by the expert.

Project Prioritization

It can be difficult to provide a positive business case for business intelligence initiatives, and often the projects must be prioritized through strategic initiatives. BI projects can attain higher prioritization within the organization if managers consider the following:

  • BI manager will determine the tangible benefits such as eliminated cost of producing legacy reports.
  • Data access for the entire organization must be enforced.In this way even a small benefit, such as a few minutes saved, makes a difference when multiplied by the number of employees in the entire organization.

Implementation Success Factor

There are three critical areas that organizations should assess before getting ready to do a BI project.

  • The level of commitment and sponsorship of the project from senior management.
  • The level of business need for creating a BI implementation.
  • The amount and quality of business data available.

The quality aspect in business intelligence will cover all the process from the source data to the final reporting. At each step, the quality gates are different:

  • Source Data:

    Data Standardization: make data comparable (same unit, same pattern…)

    Master Data Management:unique referential

  • Operational Data Store (ODS):

    Data Cleansing: detect & correct inaccurate data

    Data Profiling: check inappropriate value, null/empty

  • Data warehouse:

    Completeness: check that all expected data are loaded

    Referential integrity: unique and existing referential over all sources

    Consistency between sources: check consolidated data vs sources

  • Reporting:

    Uniqueness of indicators: only one share dictionary of indicators

    Formula accuracy: local reporting formula should be avoided or checked

Benefits of Implementing BI: By implementing BI into your business, you can achieve the following benefits.

  • Increase in the productivity of your business
  • Effective business decision-making
  • Better understanding of your customer needs and preferences
  • Develop deep insight of your business
  • Drive growth through
  • Deeper customer insights
  • Product/service innovation
  • Enhance cost management through
  • Optimized operations
  • Better financial performance analysis
  • Improve risk management through
  • Enhanced regulatory compliance
  • Internal risk and control