Business Intelligence Designer Income In Our Team – Business intelligence (BI) is a technology-driven process for analyzing data and providing actionable information that helps executives, managers, and employees make informed business decisions. As part of the BI process, organizations collect data from internal IT systems and external sources, prepare it for analysis, run queries against the data, and create data visualizations, BI dashboards, and reports to make the results of analytics available to business users for operational decision-making and strategic planning.
The ultimate goal of BI initiatives is to make better business decisions that enable organizations to increase revenue, improve operational efficiency, and gain competitive advantages over business rivals. To achieve this goal, BI incorporates a combination of analytics, data management and reporting tools, as well as various methodologies for data management and analysis.
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A business intelligence architecture includes more than BI software. Business intelligence data is typically stored in a data warehouse built for an entire organization or in smaller data brands that contain subsets of business information for individual departments and business units, often with links to an enterprise data warehouse. In addition, data lakes based on Hadoop clusters or other big data systems are increasingly used as repositories or landing pads for BI and analytics data, especially for logs, sensor data, text, and other types of unstructured or semi-structured data.
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BI data can include historical information and real-time data collected from source systems as they are generated, enabling BI tools to support strategic and tactical decision-making processes. Before being used in BI applications, raw data from different source systems generally needs to be integrated, consolidated and cleaned using data integration and data quality management tools to ensure that BI teams and business users are analyzing accurate and consistent information.
Originally, BI tools were primarily used by BI and IT professionals who asked queries and produced dashboards and reports for business users. Increasingly, however, business analysts, executives and employees are using business intelligence platforms themselves, thanks to the development of self-service BI and data discovery tools. Self-service business intelligence environments allow business users to search BI data, create data visualizations, and design dashboards on their own.
BI programs often incorporate forms of advanced analytics such as data mining, predictive analytics, text mining, statistical analysis, and big data analysis. A common example is predictive modeling that allows what-if analysis of different business scenarios. In most cases, however, advanced analytics projects are conducted by separate teams of data scientists, statisticians, predictive modelers, and other skilled analytics professionals, while BI teams oversee simpler questions and analysis of business data.
Overall, the role of business intelligence is to improve the business operations of an organization through the use of relevant data. Companies that use effective BI tools and techniques can translate the data they collect into valuable insights about their business processes and strategies. Such information can then be used to make better business decisions that increase productivity and revenue, leading to accelerated business growth and higher profits.
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Without BI, organizations cannot easily benefit from data-driven decision making. Instead, executives and employees are largely left to base important business decisions on other factors, such as accumulated knowledge, past experiences, intuition, and gut feelings. While these methods can lead to good decisions, they are also fraught with the potential for errors and mistakes due to the lack of data to support them.
A successful BI program produces a variety of business benefits to an organization. For example, BI enables C-suite executives and department managers to monitor business performance on an ongoing basis so they can act quickly when problems or opportunities arise. Analyzing customer data helps improve the effectiveness of marketing, sales and customer service efforts. Bottlenecks in supply chain, manufacturing and distribution can be identified before they cause financial damage. HR managers can better track employee productivity, labor costs and other workforce data.
BI initiatives also provide broader business benefits — among them, making it easier for project managers to track the status of business projects and for organizations to gather competitive intelligence about their rivals. In addition, BI, data management, and IT teams themselves benefit from business intelligence, using it to analyze various aspects of technology and analytics functions.
Business intelligence combines a broad set of data analysis applications designed to address different information needs. Most are supported by both self-service BI software and traditional BI platforms. The list of BI technologies available to organizations includes the following:
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Ad hoc analysis. Also known as ad hoc querying, this is one of the fundamentals of modern BI applications and a key feature of self-service BI tools. It is the process of writing and running queries to analyze specific business issues. While ad hoc queries are typically created on the fly, they often end up being run regularly, with the results of the analytics incorporated into dashboards and reports.
Online Analytical Processing (OLAP). One of the first BI technologies, OLAP tools allow users to analyze data in multiple dimensions, which is particularly suitable for complex queries and calculations. In the past, data had to be extracted from a data warehouse and stored in multidimensional OLAP cubes, but it is increasingly possible to perform OLAP analyzes directly on columnar databases.
Mobile BI. Mobile business intelligence makes BI apps and dashboards available on smartphones and tablets. Often used more for viewing data than analyzing it, mobile BI tools are typically designed with ease of use in mind. For example, mobile dashboards can only display two or three data visualizations and KPIs so they can be easily viewed on a device screen.
BI in real time. In real-time BI applications, data is analyzed as it is generated, collected, and processed to provide users with an up-to-date view of business operations, customer behavior, financial markets, and other areas of interest. The real-time analytics process often involves streaming data and supports decision analytics uses such as credit scoring, stock trading, and targeted promotional offers.
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Operational intelligence (OI). Also called operational BI, it is a form of real-time analysis that provides information to managers and front-line workers in business operations. OI applications are designed to help drive business decisions and enable faster action on issues — for example, helping call center agents solve problems for customers and logistics managers to ease distribution bottlenecks.
Software-as-a-service BI. SaaS BI tools use vendor-hosted cloud computing systems to provide data analysis capabilities to users in the form of a service typically priced on a subscription basis. Also known as cloud BI, the SaaS option increasingly offers multi-cloud support, which allows organizations to deploy BI applications on different cloud platforms to meet user needs and avoid vendor lock-in.
Open Source BI (OSBI). Business intelligence software that is open source typically includes two versions: a community version that can be used for free and a subscription-based commercial version with technical support from the vendor. BI teams can also access the source code for development uses. In addition, some vendors of proprietary BI tools offer free versions, mostly for individual users.
Embedded BI. Embedded business intelligence tools put BI and data visualization functionality directly into business applications. This allows business users to analyze data within the applications they use to do their jobs. Embedded analytics features are typically built in by application software vendors, but enterprise software developers can also include them in native applications.
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Collaborative BI. This is more of a process than a specific technology. It involves the combination of BI applications and collaboration tools to enable different users to collaborate on data analysis and share information with each other. For example, users can annotate BI data and analytics results with comments, questions, and highlights through the use of online chat and discussion tools.
Location Intelligence (LI). This is a specialized BI format that allows users to analyze location data and geospatial data, with built-in map-based data visualization functionality. Location intelligence brings geographic intelligence to business data and operations. Potential uses include site selection for retail and corporate facilities, location-based marketing and logistics management.
Self-service BI and data visualization tools have become the standard for modern BI software. Tableau, Qlik, and Spotfire, now part of Tibco Software, pioneered the development of self-service technology early on and became prominent competitors in the BI market by 2010. Most vendors of traditional BI search and reporting tools have followed suit since then. Now, almost every major BI tool incorporates self-service features such as visual data discovery and ad hoc search.
BI tools are available from dozens of vendors in total. Major IT vendors offering BI software include IBM, Microsoft, Oracle, SAP, SAS and Salesforce, which bought Tableau in 2019 and also sells its own tools developed before the acquisition. Google is also in the BI market through its Looker unit, acquired in 2020. Other notable BI vendors include Alteryx, Domo, GoodData, Infor Birst, Information Builders, Logi Analytics, MicroStrategy, Pyramid Analytics, Sisense, ThoughtSpot, and Yellowfin.
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While full-featured BI platforms are the most widely used business intelligence technology, the BI market also includes other product categories. Some vendors offer tools specifically for embedded BI uses. examples include GoodData and Logi Analytics. Companies like Looker, Sisense and ThoughtSpot are targeting complex and curated data analytics applications. Various
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