Business Intelligence System September 2013 BI

Making Fact-based Desicions Challenges More Data More Users Less Time Challenges More Data More Users Less Time Yet, their time for decision-making is decreasing Desicion-makers on all levels of management with different job roles need to understand this data Volume and complexity of data flowing into companies increasing

Implementing Business Intelligence in the Companies  The organization hierarchy may not be suitable for a new BI implementation project.  IT departments may not have the required BI and data warehouse skills.  Limited IT budget.  Different OLTP systems from different vendors with little or no integration between them.  Unstructured data which are stored in s, a high number of Excel Sheets and unconsolidated reports and query results from a number of OLTP systems Challenges

DATA SOURCES Online Databases (OLTP), Semi-structured data like Excel files. DATA SOURCES Online Databases (OLTP), Semi-structured data like Excel files. STAGING AREA Data storing, cleansing, selecting, transforming, aggregating, integration operations STAGING AREA Data storing, cleansing, selecting, transforming, aggregating, integration operations PRESENTATION LAYER OLAP Tools, Dashboards, Reports, Interactive Online Queries PRESENTATION LAYER OLAP Tools, Dashboards, Reports, Interactive Online Queries DATA WAREHOUSE / DATA MART Metadata Repository, OLAP cubes DATA WAREHOUSE / DATA MART Metadata Repository, OLAP cubes Data Extraction Data Loading Business Intelligence Applications Analysis

The main components of a BI system Business intelligence: set of tools and technologies that systematically exploit available data to retrieve information useful in supporting complex decision making processes Decisions Optimization Data Mining Data Warehouse / Data Mart Data Sources

bilişimBI’s Approach Agile business intelligence Simple and intuitive user interface. No “high-tech terminology”, no “data warehouse knowledge” needed to create multi-dimensional analysis. interactive dashboard-style communication. All ETL (extract, transform, load) functionality included. No additional license fees to be paid to third-party software. Speedy response with in-memory query execution. The bilişimBI works seamlessly with bilişimERP.

The Architecture of bilişimBI Solution Decisions Optimization Data Mining Data Exploration Data Warehouse/Data Marts Visual Query Tool (Real time data analysis) Visual Query Tool (Real time data analysis) Multidimensional Analysis Tool Multidimensional Analysis Tool ETL Wizard Data Sources bilişimERP Operational (OLTP) Data Personal semi-structured data META DATA REPOSITORY

Visualization. Data Integration Data Warehouse, Data Marts, OLAP Cubes Stage 3 Advanced Analytics & Data Mining Stage 2 Strategic Decision Support Applications Stage 1 Fundamental Data Warehouse & Visualization Functions 3 Staged Research & Development Project

Performance Monitoring, Balanced Scorecards Analytical Workflows Descriptive Analytics Dashboards, widgets Mobile BI Office Integration STRATEGIC DECISION SUPPORT APPLICATIONS Time Series Analysis Will be ready by the end of 2014 Stage 2

Case Study

Case Study The Analysis of Product Costs By clicking on product C….. COST TYPE TOTAL COST ($) Materials External Operations Internal Operations

Case Study To achieve the required analysis: 1. Build the metadata. (A prerequisite of all the analysis tasks) 2. Create the domain of analysis 3. Specify the data tables of the domain. 4. Determine the measures. 5. Determine the criteria. 6. Save the analysis domain and schedule the ETL tasks. 7. Configure the analysis widget (Graph, table, etc.) 8. Use the interactive dashboard to see the results.

Case Study Building the meta data

Case Study Creating the Analysis Domain You can update the existing Analysis simply by clicking on it. You can create new analysis (ETL tasks) by using the Add button.

What would you like to analyze?

What would you like to measure? Probable facts are listed automatically.

What are your analysis criteria?

Save and Schedule

Defining Data Quality Rules

Configure the dashboard settings

Interactive Dashboard Materials External Operations Internal Operations

Collaboration with the Industry ✔ Ability to see real-life analysis requirements ✔ High-quality OLTP data to work with ✔ Useful feedback in the early phases of the project ✔ Extensive testing ✔ Ability to verify the design desicions ✔ Motivated agile development with short iterations ✔ Guidence about the functionality of future releases Benefits for the Software Developers

Leveraged the value of their online enterprise data by applying data quality rules Eliminated most of their heavy Excel work Leveraged real-time insights in the context of their industry and role by using our Visual Query Tool. They even created “what-if” simulations to fully understand the business impact of their desicions. Collaboration with the Industry Benefits for the Industry