What is BI Anyways?

BI archetureBefore we talk about our services, let’s first clear couples of important questions that might be in your mind.

Business intelligence (BI) is Information Technology- driven process to collect, organize, analyze, and transform business data into meaningful and useful business information. Mukhles Zaman defines BI as being “neither a product nor a system. It is an umbrella term that combines architectures, applications, and databases” The goal of BI is help to non-technical end-users such as corporate executives, business managers and other end-users make more sense. The BI process analyzes historical data generated through transactions or by other kinds of business activities; and helps business end-users by analyzing the past and present business situations and performances. A successful BI implementation should lead to business users making informed decision without to always relying on the IT team. It should “answer a company’s critical questions such as, why market shares are going to competitors; which products contribute the most to profit; how can business become more profitable; why some divisions are not profitable; which plants produce at the lowest cost; how can productivity improve; which parts of the world are the most profitable; who are best and worst customers; where is money being lost or made, etc.

Why do we need BI?

To consolidate various sources of data into one single source of truth.

In many organizations data is stored in various databases, in different formats and structures. Team members spend too much time discussing and trying to agree on the validity and accuracy of data. In this case, there is a need to consolidate all the various sources of data into one single source of through. BI data warehouse allows you to combine OLTP, ERP, CRM, legacy applications, and external data and store it in one single reporting database.

To reduce the dependency of the Business users on the IT team.

In many organizations, IT department controls business data. Business users heavily relay on the business reports created by super users IT team. A successful BI implementation should reduce such dependency by allowing self-service which would empower business users.

To get the maximum benefit of what you’re already doing.

As we stated above, in many organizations data is stored in various databases, as the need of data reporting rise, IT team works hard to collect data from these various database, and to create a reporting database that gets updated regularly. In some other organizations, IT team creates table views of which reports are built off. While these workaround solutions may seem to work, the lack to provide the benefits offered by a real data warehouse which is the efficiency and the capacity are of quickly query and analyses millions of records among many others . Technically speaking, the relational database applications (OLTP) were built to process one record at time, not to handle millions of records at time.

Building Data Whareouse

We implement and deploy SAP solutions to achieve defined business goals. Maintain skills in SAP applications process design and configuration; SAP application design, development, integration, testing and deployment; and SAP application technical architecture. SAP-Business Intelligence professionals design, implement and deploy SAP business intelligence solutions (applications and technologies which are used to gather, provide access to, and analyze data and information about company operations) for end-to-end data warehousing and analytics including SAP BW, HANA and Business Objects.

BusinessObjects implementation services

We Delivery Analytical strategies develop and deliver solutions that enable the collection, processing and management of information from one or more sources, and the subsequent delivery of information to audiences in support of key business processes. Analytics Platforms professionals develop deep technical skills to support client’s Analytics agenda. Analytics can be defined as the extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions.

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