Business intelligence (BI) is the process and methods that enable organizations to generate more meaningful results and make data-driven decisions using tools such as data mining, data visualization, business analytics on existing data so that they can make better decisions.
Business intelligence parses and reports all the data generated by a business in an easy to understand way. In this way, the organization can reveal management decisions that will guide performance metrics and trends. Business intelligence addresses the needs of standard users, including senior managers, managers, front-line workers, customers, and suppliers. Provides reports and tables that are tailored to each user's task and include metrics for strategic goals and objectives. Business intelligence is powered by a data warehouse infrastructure that consolidates end-to-end enterprise data and reinforces information consistency by transforming shared data into a common data model (schema).
In terms of comparison, Gartner defines business intelligence, also known as BI, as “... an umbrella term that encompasses applications, infrastructure, and tools that enable information access and analysis to improve and optimize decisions and performance.” Similarly, CIO.com makes a statement like this: “Companies use BI to improve decision-making, reduce costs and identify new business opportunities. BI is more than just a set of tools used to extract data from enterprise reporting and enterprise systems. CIOs use BI to identify inefficient business processes that are ready to restructure.”
The growing volume of data is complicating companies' existing business processes, while also challenging decision-making processes. Business intelligence (BI) enables organizations to leverage existing data and make better decisions, making the organization more efficient.
With a properly selected business intelligence (BI) technology for your organization, you can gain a variety of benefits.
For years, business intelligence (BI) professionals have tried to fit a wide variety of business users, workloads, and data into the same architecture, and have often been disappointed. The problem is that business intelligence (BI) is a broad field. Strategically, it is about using information to make smarter decisions, while tactically, it is about setting up applications for reporting and analysis. To succeed in the new world of data, BI professionals need to adopt new thinking and approaches. They need to move away from the one-size-fits-all approach of the past.
Better decisions require better information, which is the main goal of business intelligence. Business intelligence works to improve data accuracy, timing, and volume by analyzing existing data presented on a structured metric to make better decisions for this purpose. For maximum return, BI must work to increase the accuracy, timeliness, and quantity of data. These requirements mean finding more ways to capture unsaved information by checking information for errors and structuring information to enable comprehensive analysis.
In the real world, many companies have unstructured or multi-format data, which complicates the collection and analysis processes. This requires enterprise software developers to provide business intelligence solutions that optimize information extracted from existing data. These enterprise software applications are designed to integrate a company's data and analytics ecosystem.
Software solutions are growing and developing at a steady pace, but data scientists are faced with the need to balance the contention between the speed and depth of reporting. The Internet of Things requires large volume data analysts, of which a business is a confidant, to extract findings to select data points that represent the power of an entire business area or a particular process at a point-by-point. This reduces the time to capture and format data for analysis, thereby saving time and increasing the speed of reporting.
In order for a business to compete effectively using analytics, it needs to provide its staff with BI and analytics applications that are successful not only for selected, well-understood requirements, but also for all the needs of modern users — so that all users can recognize the value in data and develop insights and analytics. Designers and developers should look beyond traditional collection methods and collaborate with users to understand their needs. A business must also give the same level of intensity it devotes to creating exciting and satisfying internal user experiences as it devotes to enhancing external customer experiences.
At Komtaş, we design end-to-end solutions in the field of Business Intelligence to help organizations make the right strategic, operational and tactical decisions, reduce IT dependencies, recognize market trends or new revenue opportunities faster.
As a partner in Turkey of Looker and Qlik technologies, which have a leading position in the field of business intelligence in the world, we offer solutions specifically designed for your needs. You can contact us to learn more about our business intelligence solutions.
Predictive analysis is the analysis of big data to make predictions and determine the likelihood of future outcomes, trends, or events occurring.
Predictive analysis, a type or extension of predictive analysis, is used to recommend or predict certain actions when certain information states are reached or conditions are met.
Data Modelling means data analysis in its simplest form. It is one of the best ways to understand, store and manage data. It is made by data scientists and modelers.
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