Business Intelligence Solution Powerpoint Presentation Slides
A corporation or other organizations data warehousing is the safe electronic storing of information. The objective of data warehousing is to build a treasure mine of historical data that can be accessed and analyzed to offer helpful insight into the businesss operations. Here is a professionally designed template on Business Intelligence Solution that presents the companys current situation, gap analysis, the need for a data warehouse in the business, OLAP, OLTP, ETL, Schemas, MPP, etc. In this template, we have covered the features of data warehouse different architectures such as primary, three tier, etc. Moreover, in this DSS, we have included various types of data warehouses, cloud and modern data warehouses, components, general stages, etc. In addition, this PPT contains working of data warehouse, data warehouse design guidelines, approaches such as top-down and bottom-up, implementation of data warehouse, etc. Furthermore, this template includes comparing data warehouse with other storage systems such as database, operational database system, Data Lake, and data mart. Lastly, this deck comprises the impacts of data warehouse implementation on business, a 30-60-90 days plan, a roadmap to implement a data warehouse, and a dashboard. Get access now.
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Deliver this complete deck to your team members and other collaborators. Encompassed with stylized slides presenting various concepts, this Business Intelligence Solution Powerpoint Presentation Slides is the best tool you can utilize. Personalize its content and graphics to make it unique and thought-provoking. All the eighty nine slides are editable and modifiable, so feel free to adjust them to your business setting. The font, color, and other components also come in an editable format making this PPT design the best choice for your next presentation. So, download now.
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Content of this Powerpoint Presentation
Slide 1: This slide introduces Business Intelligence Solution. State your company name and begin.
Slide 2: This slide states agenda of the presentation.
Slide 3: This slide shows Table of Content for the presentation.
Slide 4: This is another slide continuing Table of Content for the presentation.
Slide 5: This is another slide continuing Table of Content for the presentation.
Slide 6: This slide highlights title for topics that are to be covered next in the template.
Slide 7: This slide depicts the current situation of our company by displaying the ratio of unstructured and structured data.
Slide 8: This slide presents gap in the organization by showing how big data is causing challenges.
Slide 9: This slide highlights title for topics that are to be covered next in the template.
Slide 10: This slide displays need for a data warehouse in the organization, such as data quality, single point, etc.
Slide 11: This slide shows need for a data warehouse based on business users, storage for historical data, etc.
Slide 12: This slide depicts the data warehouse benefits for organizations such as time-saving, improved business intelligence, etc.
Slide 13: This slide highlights title for topics that are to be covered next in the template.
Slide 14: This slide shows characteristics of data warehouses such as subject-oriented, integrated, time-variant, and non-volatile.
Slide 15: This slide represents the subject-oriented feature of data warehouse and various operational applications.
Slide 16: This slide depicts the integrated feature of the data warehouse and how different subjects are stored.
Slide 17: This slide shows time-variant feature of data warehouses and how they can store years-old information.
Slide 18: This slide illustrates the non-volatile feature of the data warehouse.
Slide 19: This slide highlights title for topics that are to be covered next in the template.
Slide 20: This slide displays the basic architecture of a data warehouse and how information is processed and stored in this architecture.
Slide 21: This slide depicts the three-tier data warehouse architecture, including functions performed.
Slide 22: This slide describes a data warehouse architecture with a staging area.
Slide 23: This slide presents a data warehouse architecture with a staging area and data marts.
Slide 24: This slide shows data warehouse bus architecture and how it decides the flow of the data in the data warehouse.
Slide 25: This slide displays different views of data warehouses, such as top-down view, data source view, data warehouse view, etc.
Slide 26: This slide highlights title for topics that are to be covered next in the template.
Slide 27: This slide depicts the various types of data warehouses, such as enterprise data warehouses, operational data stores, etc.
Slide 28: This slide presents the enterprise data warehouse (EDW) and its architecture, including the data source layer, staging area, etc.
Slide 29: This slide represents the types of enterprise data warehouses such as on-premises data warehouses, cloud-hosted data warehouses, etc.
Slide 30: This slide illustrates the operational data store and its architecture, including data sources such as unstructured and structured.
Slide 31: This slide depicts the data mart type of data warehouse, its architecture, and how a single department manages it.
Slide 32: This slide depicts the dependent data mart and how it can be established in two ways.
Slide 33: This slide presents the independent data mart and has no connection with the central data warehouse.
Slide 34: This slide depicts the hybrid data mart and how data is integrated into this type of data mart other than data warehouse.
Slide 35: This slide highlights title for topics that are to be covered next in the template.
Slide 36: This slide depicts what a cloud data warehouse is and how it can store data from many data sources.
Slide 37: This slide shows the benefits of cloud data warehouses, such as cost reduction, data security, etc.
Slide 38: This slide represents what a modern data warehouse is and how it supports SQL, machine learning, etc.
Slide 39: This slide highlights title for topics that are to be covered next in the template.
Slide 40: This slide displays the critical components of a data warehouse, such as load manager, warehouse manager, etc.
Slide 41: This slide represents the stages of data warehouse such as operational database, offline data warehouse, etc.
Slide 42: This slide represents the most prominent data warehouse solutions such as MarkLogic, Amazon RedShift, and Oracle.
Slide 43: This slide highlights title for topics that are to be covered next in the template.
Slide 44: This slide depicts how the data warehouse works, including how operations such as extraction, transformation, etc.
Slide 45: This slide represents how data warehouses, databases, and data lakes work together.
Slide 46: This slide highlights title for topics that are to be covered next in the template.
Slide 47: This slide represents the guidelines for data warehouse design, such as describing the business requirements, development of conceptual design, etc.
Slide 48: This slide presents the top-down design approach of the data warehouse, including its features such as time-variant, non-volatile, subject-oriented, etc.
Slide 49: This slide depicts the bottom-up design approach of the data warehouse and how data mart is built firstly in this approach.
Slide 50: This slide highlights title for topics that are to be covered next in the template.
Slide 51: This slide depicts the business best practices to implement a data warehouse.
Slide 52: This slide describes the IT best practices for implementing a data warehouse, including tracking performance & security, maintaining data quality standards, etc.
Slide 53: This slide shows Checklist to Implement Data Warehouse in Company.
Slide 54: This slide represents the steps to implement a data warehouse in the organization, including enterprise strategies, phased delivery, etc.
Slide 55: This slide depicts the data warehouse implementation trends such as cloud data warehouse, data warehouse as a service, etc.
Slide 56: This slide represents the autonomous data warehouse with zero complexity deployment and how it will automate the routine.
Slide 57: This slide describes the budget for data warehouse implementation, including storage on the cloud, storage on-premise, etc.
Slide 58: This slide highlights title for topics that are to be covered next in the template.
Slide 59: This slide depicts a comparison between database and data warehouse based on the design, type of information, etc.
Slide 60: This slide displays the comparison between data warehouse and operational database systems based on design, purpose, etc.
Slide 61: This slide depicts the comparison between data warehouse and data lake and how data is stored in the data warehouse.
Slide 62: This slide represents a comparison between data warehouse and data mart and how data marts can be designed for sole operational reasons.
Slide 63: This slide presents the comparison between data warehousing and business intelligence and how business intelligence helps to generate useful output from raw data.
Slide 64: This slide highlights title for topics that are to be covered next in the template.
Slide 65: This slide represents the impacts of data warehouse implementation on the company.
Slide 66: This slide highlights title for topics that are to be covered next in the template.
Slide 67: This slide represents the 30-60-90 days plan to implement a data warehouse in the company.
Slide 68: This slide highlights title for topics that are to be covered next in the template.
Slide 69: This slide depicts the roadmap for data warehouse implementation in the company.
Slide 70: This slide highlights title for topics that are to be covered next in the template.
Slide 71: This slide shows dashboard for data warehouse implementation in the organization.
Slide 72: This slide is titled as Additional Slides for moving forward.
Slide 73: This slide highlights title for topics that are to be covered next in the template.
Slide 74: This slide represents what a data warehouse is, including its different data sources and the operations performed.
Slide 75: This slide displays the OLAP and OLTP in data warehousing and how OLAP tools are used for multifaceted data analysis.
Slide 76: This slide represents the extract transform and load tools of the data warehouse and how they perform their jobs.
Slide 77: This slide depicts the schemas in data warehouses such as star schema and snowflake schema.
Slide 78: This slide represents the massively parallel processing analytical database and how parallel processing is done.
Slide 79: This slide describes the applications of data warehouses in different industries such as banking, healthcare, government, etc.
Slide 80: This slide contains all the icons used in this presentation.
Slide 81: This is a Comparison slide to state comparison between commodities, entities etc.
Slide 82: This is About Us slide to show company specifications etc.
Slide 83: This slide presents Post It Notes. Post your important notes here.
Slide 84: This slide shows Circular Diagram with additional textboxes.
Slide 85: This slide displays Puzzle with related icons and text.
Slide 86: This slide shows SWOT describing- Strength, Weakness, Opportunity, and Threat.
Slide 87: This slide shows Bar Graph with three products comparison.
Slide 88: This slide presents Venn diagram with text boxes.
Slide 89: This is a Thank You slide with address, contact numbers and email address.
Business Intelligence Solution Powerpoint Presentation Slides with all 94 slides:
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FAQs
Some best practices for implementing a data warehouse include identifying business requirements, following a top-down or bottom-up design approach, tracking performance and security, and maintaining data quality standards.
The three-tier data warehouse architecture includes the bottom tier (data source layer), middle tier (ETL layer), and top tier (data presentation layer). The functions performed in each tier include data extraction, transformation, and loading in the bottom tier, data integration and cleansing in the middle tier, and data analysis and reporting in the top tier.
A data warehouse provides a single point of access to an organization's data and ensures data quality. It also allows for the storage of historical data and improves business intelligence.
Data warehouses have four main characteristics: they are subject-oriented, integrated, time-variant, and non-volatile. This means that they focus on specific subject areas, combine data from different sources, store historical data, and do not allow data to be modified or deleted.
An enterprise data warehouse is a large-scale centralized repository that stores data from across an entire organization, while a data mart is a smaller repository that is focused on a specific department or business function. Data marts are often used to provide quick access to specific data sets, while an enterprise data warehouse is used for broader data analysis and reporting.
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