BUSINESS INTELLIGENCE

Business intelligence analysis can help you unleash the power of your data to do more of what works, and less of what doesn’t. The term Business Intelligence (BI) refers to technologies, applications and practices for the collection, integration, analysis, and presentation of business information. The purpose of Business Intelligence is to support better business decision making. Essentially, Business Intelligence systems are data-driven Decision Support Systems (DSS). Business Intelligence is sometimes used interchangeably with briefing books, report and query tools and executive information systems.
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Business intelligence is no longer a “nice to have.” Nearly every organization needs to employ some form of data-driven decision making to achieve their business goals and exceed their competition.

Unfortunately, more than half of BI  initiatives fail due to a combination of challenges, ranging from poor data quality and management to lack of business intelligence expertise to not adapting reporting and analysis as the business matures. Such failure can mean months or years of lost opportunities, significant setup costs, and frustration.

An expert BI consultant such as Forelogix not only will mitigate this risk, but more so enable you to Evolve your organization and achieve new levels of success.

Leveraging a BI analyst can help you unleash the power of your data to do more of what works, and less of what doesn’t.

Develop a solid BI vision and strategy

Improve your overall data quality

Streamline access to multiple data sources

Adapt to business requirements and opportunities

Implement effective BI tools

Maximize ROI with proper BI training

Take action on new insights and recommendations

Creating a Business Intelligence Infrastructure

Business Intelligence
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Successful BI consulting focuses first on the data and then on the analysis, in three key phases:

  1. Data Management
  2. Data Visualization and Dashboards
  3. Business Analytics

Proper data governance is the first step towards BI and includes:

  1. Defining the data structure based on business requirements
  2. Defining data governance policies
  3. Specifying the data collection process and methods
  4. Processing and preparing the data for analysis
  5. Warehousing the data
  6. Validating the data through quality assurance testing

Solid data management leads to the next phase, visual analytics, which includes report automation, visualizing key performance indicators, alert monitoring (red flags), and answering general business questions through custom dashboards.

Documenting the strategy for visual analytics will improve both your decision making and internal communication throughout the company.

Some of the best questions surface after stakeholders have been able to visualize and digest their data over time. A strong understanding of performance measures and directional trends pave the way for more advanced analytics, in areas such as:

Customer segmentation

Data Mining

Predictive Analytics

Machine learning

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