Business Intelligence Tasks Germany – For more than 10 years, Gartner has published its famous Magic Quadrant for analytics and business intelligence platforms, and 2023 was no exception. Over the years, both Salesforce’s Tableau and Microsoft’s Power BI have consistently demonstrated solid performance and powerful capabilities in the Leaders Quadrant. Tableau has always scored high, and Microsoft has done a great job of positioning itself ahead of Tableau. This is not only the result of fast development cycles and the regular addition of new features, but also a redefining of the criteria behind these annual Magic Quadrants.
Below, I recall Gartner’s market definition and overview, critical platform capabilities, evaluation criteria, and summarize the strengths and caveats of the following vendors:
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Get a full copy of the Gartner Reprint at Tableau: https://qbq.li/GMQ23Tab. Registration is required to access the report.
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Analytics and Business Intelligence (ABI) platforms enable less technical users, including business people, to model, analyze, explore, share and manage data, and collaborate and share insights with the help of IT and augmented by artificial intelligence (AI).
The availability of major ERP and CRM cloud providers also influences ABI’s choice of platform. On the one hand, cloud search creates inevitable concerns about the blocking and unpredictable costs of data and analytics portfolios. On the other hand, cloud providers are recognizing the importance of openness in their software stacks and the growing importance of “multi-cloud” approaches where organizations run applications across and across multiple cloud offerings, such as Databricks and Snowflake.
According to Gartner, the functionality of the ABI platform includes the following 12 critical capabilities that have been updated to reflect changes in the market, differentiation and customer demand:
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Gartner excluded three critical capabilities from its evaluation criteria: security, natural language generation (NLG; combined into data storytelling), and cloud analytics (which will continue to be considered a go-to-market strategy). And one of the authorization granularity security sub-criteria (such as string-based security) has been moved to the enterprise reporting capability.
Tableau’s products are primarily focused on visual exploration that enables business users to access, prepare, analyze, and present insights into their data. CRM Analytics, formerly Tableau CRM, provides advanced analytics capabilities for citizen data analysts and researchers. In 2022, Tableau strengthened its advanced consumer vision to deliver contextual insights through deeper integration with Salesforce Data Cloud.
Microsoft’s core ABI platform, Power BI, has tremendous market reach and momentum thanks to Microsoft 365, Azure, and Teams integration, flexible pricing, above-average functionality, and an ambitious product development plan.
Google Cloud’s Looker is a cloud-based ABI platform that offers tightly managed analytics, including self-service visualizations and dashboards, as well as a code-based semantic modeling layer based on LookML. Looker’s developer-focused analytics platform provides a version-controlled collaborative platform for both internal business analytics and customer application development.
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SAP Analytics Cloud is a multi-tenant cloud platform with a wide range of capabilities for data visualization, reporting and advanced analytics. It combines analytics and enterprise planning to build end-to-end processes from insight to business action. The low code and advanced coding capabilities of analytics programs offer component analytics programs for lines of business. It has improved data integration and modeling capabilities, especially in the full context of SAP data and application ecosystems.
Qlik’s core product is Qlik Sense Enterprise SaaS, which includes Qlik Sense, Qlik AutoML and Qlik Application Automation. Qlik is a cloud provider and has the highest level of partnerships with each of the three major cloud providers (AWS, Microsoft, and Google), as well as partnerships with Databricks and Snowflake. Effective decision-making processes in business depend on high-quality information. This is a fact of life in today’s competitive business environment, which requires flexible access to data storage, organized in a way that improves business productivity and provides fast, accurate and relevant information. To meet these requirements, a business intelligence architecture has emerged with the data warehouse as the foundation of these processes.
In this post, we’ll explain the definitions, relationships, and differences between data warehousing and business intelligence, and provide a BI architecture diagram that visually explains the relationship between these terms and the framework on which they work. But first, let’s start with the basic definitions.
What is BI architecture? Business intelligence architecture is a term used to describe the standards and policies for organizing data through computer-based methods and technologies that create business intelligence systems used for online data visualization, reporting, and analysis. One of the components of the BI architecture is the data warehouse. Organizing, storing, cleaning and extracting data should be done by a central repository system, namely a data warehouse, which is considered a fundamental component of business intelligence. But how exactly are they related? Before answering this question, let’s first define in more detail what data warehouse models are. What are data warehouses? A data warehouse is a central enterprise repository where you can store and analyze massive amounts of data from multiple sources. The data warehouse is considered a key element of the business intelligence process, providing organizations with the tools to make informed decisions. In other words, a DWH is a data management system where organizations store current and historical information about sales, marketing, finance, customer service, and more. It facilitates BI processes by providing organizations with the means to query and answer the most pressing analytical questions. Thanks to this, companies can optimize their efficiency and build strategies based on accurate understanding, not on pure intuition. In trying to understand DWH and its value in a business environment, it is important to distinguish it from a database. Although they are similar and can be considered valuable for data storage and management, they are different. Below, we’ll discuss some of the obvious differences to help you evaluate the value of storage in perspective. Database vs Data Warehouse The first and most important difference between the two is that databases record data and transactions, usually in a tabular format that users can access, manipulate, and retrieve at will. The ultimate goal of a database is to provide users with a secure and organized way to store and access their information. Repositories, on the other hand, store vast amounts of data from many disparate sources and store it for analytical purposes. Giving companies the environment they need to make inquiries and shape their most important strategies. The second difference, which is also one of the most important, is how they handle data. On the one hand, databases use online transaction processing (OLTP) to perform a series of simple transactions such as insert, replace, and update, etc. In addition, OLTP responds instantly to user queries, enabling real-time data processing. Data warehousing, on the other hand, uses online analytical processing (OLAP) to quickly analyze vast amounts of big data. The main difference between the two is that while OLTP can collect data that happened only a few seconds ago, OLAP can process and analyze data a thousand times faster. The third and final difference between the two is that databases are usually limited to a single use case, such as storing real-time data about each item sold on your website. It can handle a huge number of simple and detailed queries in a short time. In contrast, DWH is “subject-oriented” and can produce aggregated data for complex queries that are later used for analysis and reporting. These are just three of the many differences between them. We won’t dive deeper into them because that would detract from the real purpose of this blog. However, you can familiarize yourself with them in more detail in this article. Types of Data Warehouses Now that you understand the basic concepts of data warehouses, let’s take a look at some of the key types you need to know. Types: Enterprise Data Warehouse (EDW): As its name suggests, an EDW provides businesses with a centralized system for storing and managing information from a large number of sources. It helps to make decisions from a tactical and strategic point of view. Operational Data Warehouse (ODS): ODS complements the EDW we just described above. It is a central database that is updated in real-time and is used for operational reporting when the EDW does not meet the company’s reporting requirements. Data Mart: This is a subset of DWH designed specifically for a specific business area or team. , such as sales, HR or marketing. It is thematically oriented, that is, users can find the information they need very quickly. Without further ado, let’s see how BI and DWH are related. What is data warehousing and business intelligence? Data warehouses and business
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