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Figure 1 – We suggest implementing an enterprise data warehouse and a single semantic layer to access the data. Once all the layers shown in the above diagram are established, increasing the breadth of the warehouse to accommodate reporting requirements of wider range of users can be an iterative process.
Approach to implement BI solution
Introduction
The most common risk faced by organizations at an early stage of their Business Intelligence (BI) journey is a proliferation of numerous reporting projects each having carte blanche to implement their favored design. Our approach to implement BI will help the organizations to mitigate this risk considerably.
Implementation considerations
It is important to understand the current application system landscape in the client organization and the future plans before proposing roadmap for business intelligence (BI) implementation.
If the initiative is in its early stages, it is recommended to implement an enterprise data warehouse with a single semantic layer to access the data. Once this basic infrastructure is implemented, departmental reporting requirements can be turned around in a scalable manner.
Subsequent reporting projects can avoid the technical discovery process by simply adopting the established data infrastructure standards. Every reporting project will enrich the data warehouse making it easier to cater to information requirements of wider range of users.
Leverage your data for business advantage
Accessibility to high quality data improves decision making process radically. We have built the right combination of people and process to help our clients to successfully implement their BI strategies.
Implementation steps
Our approach, Exility, enables us to manage requirements better and develop software that’s easier to change. Key aspects of how Exility is a 'game changer':
- Business data dictionary – It is important to uniquely define and name the data elements with business terms. Business data dictionary will list all the business terms frequently used in business conversations within an enterprise. These will be documented in metadata repository and used while building the warehouse.
- Identify the “systems of records” or data source applications – Identify the operational data source systems. This may include ERPs, core systems used for manufacturing, sales, distribution, retail and warehousing.
- Extract, Transform and Load the data – The data needs to be extracted from identified systems of records, cleansed and profiled. Data transformation logic will be defined based on business requirements. The transformed data will then be loaded through workflow based loading programs.
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- Data warehousing – It involves developing enterprise data architecture, developing data model and then establishing database for the core data layer. Extracted and consolidated data has to be stored in a manner that provides better performance and scalability.
- Semantic layer – This is a business representation of organization data that helps end user’s access data autonomously using common business terms. It maps complex data present at enterprise data warehouse into familiar business terms to offer a unified, consolidated view of data across the organization. Access and security can be defined for each basic block of data.
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- Data Presentation layer – Data can be presented according to the requirement of
each user. Typically, operational user requirements are met by:
- Canned reports (standard operational reports ready for use by an authorized user)
- Ad-hoc reports (“on the fly” reports and real time queries)
- Portal reporting and alerts – Reports at web portal can be personalized as it allows user defined views that can be saved
- OLAP reports – This allows slicing and dicing data based on various dimensions
- Executives’ need summarized meaningful information in the form of Scorecards and Dashboards. These provide management level quick view of information. We pay attention to understand executive visualization requirements and accommodate the same in the presentation solution.
- Predictive analytic requirements will be facilitated through data mining and modeling tools. Visualization for predictive analytics will be developed once the data warehouse matures and holds significant amount of data to analyze.
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This case study is centered on how EXILANT helped the retail client to establish and improve reporting solution for their retail stores operation. The solution enabled the client to track the KPI’s of stores and also analyze the shopping trends.
Summary
Business Intelligence projects are iterative in nature. The initial development and deployment of BI platform will be based on the organization’s crucial information requirements. Once this base infrastructure is established, usage within the organization increases significantly, resulting in more reporting requirements. These requirements can be accommodated in subsequent iterations faster and at lower cost.
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