Images For Business Intelligence – The information technology (IT) market is full of many words and terms that are often used interchangeably. However, in some cases there are subtle differences between terms that are important to understand and can affect the choice of tools and how they are deployed. An example is business intelligence vs. Business Analytics or BI Vs. B.A. Read on to learn how these concepts and tools differ and how they complement each other.
Although it had some earlier uses, business intelligence (BI) as it is understood today evolved from the decision support systems (DSS) used in the 1960s through the mid-1980s. Then in 1989, Howard Dresner (former Gartner analyst) proposed the term “business intelligence” to describe it as “concepts and methods for improving business decisions using fact-based support systems”.
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A more modern definition from Wikipedia describes BI as “a set of strategies and technologies used by companies for data analysis of business information”. Another definition offered by TechTarget states that “Business Intelligence (BI) is the technology-based process of analyzing data and delivering actionable information that helps leaders, managers and workers make informed business decisions.”
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The TechTarget definition describes how, 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 for presentation. Make data and analysis results available to business users for operational decisions and strategic planning.
“Business analytics” or “data analytics” is a more modern term applied to BI, corporate performance management (CPM) and a broader domain of analytical tools and applications. What I like about the word analytics is that it implies a more “proactive” approach to using information. Where BI is often seen as the process of gathering information and formatting it to deliver it to end users – analytics talks more about the process of accessing, processing, using, manipulating, dicing and drilling into information to understand trends and get analytical answers. Question
Below is the International Data Corporation (IDC) classification (see Figure 1) for big data and analytics software, which shows how all these tools and applications fit together. This classification has three primary segments for the market:
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Let’s talk about business analytics use cases, identifying the different types of business analytics tools available in the market through IDC classification. There are mainly three types of analytics that companies use to drive decision making:
Forms the majority of today’s management reports. It is the analysis of historical data using simple techniques such as data aggregation and data mining, which are used to uncover trends, signals and patterns. This information is delivered to end users through reports and management dashboards that include visual data representations such as line charts, bar charts and pie charts that provide useful insights and provide a basis for further analysis of underlying details.
A more advanced method of data analysis that uses statistical analysis techniques and machine learning on historical data to project future outcomes and the probability of those outcomes. Use cases for predictive analytics include issues such as demand or sales forecasting, fraud detection, and customer churn analysis.
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, prescriptive analysis takes the process a step further by showing decision makers the best course of action for future scenarios using a variety of statistical methods. This is achieved by collecting data from various descriptive and predictive sources and using it in the decision making process. It enables teams to see the best course of action before making decisions, saving time and money while achieving optimal results.
OneStream enables finance teams to quickly lead by integrating predictive analytics with key CPM processes: planning, budgeting and forecasting; economic integration; reporting; and financial data quality. And with our built-in predictive analytics solution (see Figure 2), OneStream transforms finance to take the budgeting, planning and forecasting process even further – allowing teams to plan, analyze and forecast with confidence.
As announced at OneStream’s Splash virtual event in 2021, OneStream’s AI services and Sensible ML solution will enable finance teams to leverage predictive ML models without the extensive work of data scientists. This solution will walk users through a step-by-step process for each part of the ML model building and deployment process. including feature development through advanced algorithm configuration, training and deployment.
Beyond Dashboards: The Future Of Analytics And Business Intelligence?
Business intelligence tools are part of a broader range of business analytics tools that include analytical data infrastructure, CPM and analytical applications, as well as advanced predictive analytics tools. These business analytics tools and applications are designed to help managers and decision makers in all organizations gather, organize and disseminate information and provide the “analytical intelligence” needed to make timely and informed decisions that can improve business performance.
To learn more about OneStream’s approach to predictive analytics and machine learning, download our white paper and contact OneStream if your organization is ready to transform finance by aligning advanced predictive analytics and machine learning with core CPM processes.
Please switch to the latest version of a modern browser such as Chrome, Firefox or Edge to access our website. Business Intelligence (BI) is an umbrella term that combines architectures, tools, databases, analytical tools, applications and methods. It was first coined by the Gartner Group in the 1990s. It is a content-free expression, so it means different things to different people. The main goals of BI are to enable interactive access to data (sometimes in real time), enable manipulation of data, and give business managers and analysts the ability to perform appropriate analysis. By analyzing historical and current data, conditions and performance, decision makers gain valuable insights that enable them to make more informed and better decisions. The process of BI is based on the transformation of data into information, then decisions and finally actions.
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A BI system consists of four main components: a data warehouse, with all relevant source data; business analytics, a collection of tools and algorithms for manipulating, mining and analyzing data in a data warehouse; Business Performance Management (BPM) to monitor and analyze performance; and a user interface (eg, a dashboard) that can effectively display insights generated by analytics.
Business cycle times are now extremely compressed; So making faster, more informed and better decisions is a competitive imperative. Managers need the right information at the right time and place. This is the mantra of the modern approach to BI. Organizations should work very smartly. Careful management of BI initiatives is essential to doing business today.
Analytics involves using all available data to answer key questions that a business needs to answer in order to improve profitability and other objectives. For example, the chief marketing officer needs to know which customer segments to target for cross- and up-selling, the head of risk needs to know that all checks and balances are in place to avoid financial or informational compromises. CIOs must have full control over budgets and returns on IT spending, and must ensure that the applications developed meet their potential. The CEO, on the other hand, wants to know if any new products need to be designed and developed to meet unmet customer needs. Most of these questions can be answered today by digging deep into corporate and external data and using advanced analytics to process the data to generate rich insights.
Importance Of Business Intelligence In Business
Analytics are often categorized as descriptive analytics, predictive analytics, and prescriptive analytics. Descriptive analysis is the use of historical data combined with simple statistical techniques for the purpose of visualizing and interacting with historical data. This helps to understand the current state of the company. Predictive analytics, on the other hand, uses advanced mathematical and statistical modeling to generate forecasts for any selected variable, such as quarterly sales. It is used for better planning of activities. Prescriptive analysis uses techniques like optimization to arrive at the best course of action considering the current state and the projected future state of the system. This enables almost real-time decision-making with the incorporation of data and analytics into organizational processes.
ABC Corp. is one of the global leaders in the travel industry, offering both business-to-consumer and business-to-business services. It serves travelers, travel agents, companies and travel suppliers through its four main companies. The current volatile global economic environment poses significant competitive challenges to the airline industry. To stay ahead of the competition, ABC Corp recognized that airline managers needed improved tools to manage their business decisions, and that gathering and assembling the financial and other information needed for actionable initiatives was a traditional, manual, time-consuming process.
It enables real-time decision support at airlines around the world that increases their (and in turn, ABC’s) return on information by generating insights, actionable intelligence and customer value from growing data. ABC Corp. has developed an Enterprise Travel Data Warehouse (ETDW) that holds massive reservation data. ETDW is updated in near real time
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