PREDICTING ANALYTICS FOR BUSINESS INTELLIGENCE PPT.pptx

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1. PREDICTING ANALYTICS FOR BUSINESS INTELLIGENCE By : CHETHAN . R

2. DATA ANALYTICS FOR BI : Before processing, Every DATA is known as “RAW-DATA ”. After processing, It converts into “Useful Essential INFORMATION ”. BI stands for “BUSINESS INTELLIGENCE” like “ARTIFICIAL INTELLIGENCE”.

3. CONT… DATA-ANALYTICAL tool, used to analyse the data(Excel sheet with 10,000 rows) to a Binary Format(X-axis & Y-axis) VISUALIZATION through “SHAPES”, “COLORS” & “SIZE ”. SEASONAL TRENDS. 360 DEGREE VIEW.

4. CONT… CLIENT , CUSTOMER convincing BY representing through GRAPH, CHART & MAPS BY avoiding LONG THEORITICAL PARAGRAPH . TO VIEW CURIOUS(interested) DATA by selecting(FILTERING).

5. CONT… IDENTIFYING weak-point through-out the BRANCHES of an ORGANIZATION, So that they can OVERCOME the LOSS by Using MAPS . It allows the creation of amazing and interactive visualization and that too without coding .

6. CONT… business analytics tool offers interactive visualizations and business intelligence capabilities through an easy-to-use interface for end-users . It allows users to connect to their data sources, generate reports and dashboards, and collaborate with others within their organization.

7. ARCHITECTURE OF DATA ANALYTICS :

8. Data Integration We can import data in many formats from different data sources. It gathers (extracts) data from many data sources and converts it into a regular format during the data integration step. It stores data in a familiar storage location, later integrated into Power BI.

9. Data Processing After it integrates and stores data in a safe location, had processed the raw data. Several processing or cleansing operations, like removing redundant values, modify the raw data. Later, we apply relevant business rules to the processed data that converts it to meet our business requirements . The processed data had moved into data warehouses.

10. Data Presentation In this final phase, the processed data had moved from the warehouse to Power BI platforms such as Power BI Desktop used to create reports, dashboards, and scorecards. Power BI provides a diverse set of visualizations. We may also use the marketplace to import custom visualizations. We can publish reports on the web or mobile apps using the report development platforms to share them with other company users.

11. DATA SOURCES : File: Excel, JSON, Text/CSV, XML, PDF, Folder. Database: SQL Server database, IBM, MySQL, Access database, Oracle database, SAP HANA database , Impala, Teradata Amazon Redshift, Google BigQuery , etc. Power BI: Power BI dataflows and Power BI datasets. Azure: Azure SQL, Azure SQL Data Warehouse, Azure Cosmos DB, Azure Analysis Services, Azure Data Lake etc.

12. CONT… Online Services: Google Analytics, Adobe Analytics, Salesforce, Azure DevOps, Dynamics 365, Facebook, GitHub, etc. Others: Python script, R script, ODBC, OLE DB, Active Directory, Web, Spark, Hadoop File (HDFS), etc.

13. DATA ANALYTICAL TOOLS FOR BI :