Self-service Business Intelligence Tools For Cloud-based Analytics – This blog post follows a webinar presented by Preeti Malchandani – “Smart and Personal Analytics with SAP BusinessObjects BI”.
This blog will focus on predictive features as part of advanced analytics capabilities in SAP Analytics Cloud. For a more detailed presentation and a live demonstration, watch this webinar
Self-service Business Intelligence Tools For Cloud-based Analytics
At SAP Analytics Cloud, we provide functionality for all types of users, from IT to business analysts to information workers.
What Is Sap Bi And Sap Bi Tool?
Our focus for this blog and related websites is on Advanced Analytics in SAP Analytics Cloud.
Compared to SAP BusinessObjects BI Suite, SAP Analytics Cloud offers better business intelligence with the addition of planning and analytics – two key areas that have improved analytics by using predictive technology to enhance decision making. Read this blog and watch this webinar to learn more about the comparison between SAP BusinessObjects BI Suite and SAP Analytics Cloud.
Advanced analytics in SAP Analytics Cloud includes three key technological advancements: natural language processing (NLP), artificial intelligence and machine learning. With these enhancements, your existing dashboards can provide you with fast automated interpretation and powerful predictive insights.
Advanced analytics allow you to ask questions about your current business situation, predict future trends, and gain insight into your results by understanding the key drivers affecting your KPIs. For example, related to profitability, you can see what your profit was last year, what it will be next year, and see why you got the answer. You can also ask questions about different areas of your organization For example, in human resources, you can estimate how many employees you need in specific workplaces to optimize efficiency. You can find answers to these tough questions through our six simple lessons as part of the Advanced Analytics capabilities in SAP Analytics Cloud.
What Is Business Intelligence?
A smart device can be used depending on your needs and questions All features are applicable to your specific use case, regardless of industry or process
To learn more about how you can extend and enhance your current SAP BusinessObjects BI solution with SAP Analytics Cloud, watch our on-demand webinar here. All business functions and data – information originating from internal and external sources for your company And these data channels double for managers by providing analytical information on business and market-related activities Therefore, misunderstandings, misinformation and lack of information can lead to deterioration of market conditions and internal processes – leading to poor decisions.
Making data-driven decisions requires a 360° view of all aspects of your business, which you may not have anticipated. But how do you turn unstructured data into something useful? The answer is business intelligence.
In this article, we will discuss the right way to bring business intelligence into your existing corporate structure. You will learn how to develop a business intelligence strategy and integrate the tools into your company’s workflow What is business intelligence? Business intelligence or BA is a set of techniques for collecting, generating and analyzing raw data. BI explores processes and tools that transform unstructured data sets and compile them into easy-to-understand reports or dashboards. The primary purpose of BI is to support data-driven decision making
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Business Intelligence Process: How Does BI Work? The entire process of business education can be divided into five main stages
Business intelligence is a mechanical process that relies heavily on inputs Technologies used in BC can be used for data mining to transform unstructured or semi-structured data and traditional tools to work with big data. Business Intelligence and Predictive Analytics Analytics The definition of business intelligence is often confused when it is linked to other areas of knowledge.
. Descriptive analytics and analytics—or BI—can help businesses learn about their company’s market trends, as well as their internal processes. A review of the past can help identify pain points and development paths
Based on data processing of past and present events Rather than providing a view of past events, predictive analytics predicts future business conditions It can also be compared and contrasted To achieve this, a complex data architecture involving advanced ML techniques must be developed by a professional data science team.
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So we can say that predictive analytics can be considered as the next level of business intelligence Currently, prescriptive analysis is the fourth, most advanced type that suggests solutions to business problems and actions to solve them. Business Intelligence Architecture: ETL, Data Warehouse, OLAP, and Data Mart
A general concept that can include management elements (information management, policies, standards, etc.), but in this article, we will focus on technical structures. Most of the time, it’s in
Now we’ll go over all the infrastructure aspects, but if you want to expand your knowledge of data technology, check out our article or watch the video below.
To begin with, the core of any BI architecture is a data warehouse A database is a database that stores your information in a predetermined format, usually structured, organized, and error-free.
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However, if your data is not pre-processed, neither your BI tools nor your IT department can query it Because of this, you cannot connect your data warehouse and data source directly Instead, you need to use ETL tools ETL ETL (Extract, Transform, Load) and data integration tools process raw data from primary sources and send it to the warehouse in three steps.
Typically, ETL tools and BI tools are offered from vendors (we’ll cover the most popular ones). Data Warehousing Once you have configured data delivery from selected sources, you need to set up the warehouse In business intelligence, data warehouses are a type of database that stores historical information in a tabular format. Warehouses are connected to data sources and ETL systems on one side and reporting tools and dashboard connections on the other. It allows data from different systems to be displayed through a single interface
But there is a lot of information in the warehouse (100GB+), and the response to queries is slow In some cases, the data can be stored in an unstructured or semi-structured manner, which can lead to many errors when sorting the data to generate a report. Analytics requires some type of data that is grouped in a storage location for easy access That’s why businesses use other technologies to access small data quickly
Recommendation: If you don’t have a lot of data, a simple SQL database should be used Other structural features such as data marketing can cost you a lot for nothing Data in a database + OLAP cube warehouse takes two forms, usually represented in a tabular format (tables and rows). How the warehouse stores the data is also considered incorrect
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. A database can contain thousands of different types of data, so it takes a long time to query a database To meet the needs of analysts to access data quickly, analyze it from different dimensions and group it whenever they want, OLAP blocks are used.
OLAP or Online Analytical Processing is a technology that analyzes and presents data from multiple sources simultaneously. Building your data in OLAP blocks helps overcome the limitations of data warehousing
An OLAP cube is a data structure for quick analysis of data from an SQL database (warehouse). Cube source data from a data warehouse is somewhat smaller However, the data structure requires more than 2 dimensions (the row and column layout of the table). Departments are an important part of creating a report, for example, for a sales department
Blockchain creates a versatile database of information that can be adapted to groups in different ways and generate reports quickly. A warehouse and OLAP are used together, because only small blocks of data are stored for efficient processing.
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Recommendation: The data warehouse + OLAP cube architecture can be used by companies of all sizes that need to analyze large amounts of data. If you don’t want to flood your warehouse with queries, consider an OLAP architecture approach. Data Warehouse + Data Marketing Technology The warehouse is first and foremost a business intelligence architecture. A minor form of data warehouse is a database that collects information dedicated to a particular subject area With the help of Data Marketer, specific departments can access the required data
Recommendation: Data warehouse + Data warehouse is the second most popular architecture It can set up instant reports or easily access information without requiring end user permission Hybrid Architecture Enterprise businesses may require multiple options for data management Data mart and blockchain are different technologies, but they are used to represent small data from a repository.
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