It has 4 major components. Let’s see each of them below.
1. DATA WAREHOUSE
It consists of historical and current data.
2. BUSINESS ANALYTICS
End users can work with data and information in the data warehouse by using a variety of tools and techniques.
2.1 REPORTS AND QUERIES
It consists of static and dynamic reporting, all types of queries , multidimensional views and drill down.
2.2 DATA, TEXT, WEB MINING, MATHEMATICAL AND STATISTICAL TOOLS
Data mining is a process of searching for unknown relationships and information in large databases or data warehouse using intelligent tools such as neural computing, predictive analytics techniques and advanced statistical methods.
3. BUSINESS PERFORMANCE MANAGEMENT
A set of performance management and analytics processes that enable the management of the organization’s performance.
4. USER INTERFACE: DASHBOARD
Dashboards provides a comprehensive visual view of corporate performance measures, trends and exceptions. Other than dashboards, other tools are corporate portals or other technologies such as geographical information system (GIS).
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Modern BI Architecture
Organizations starts to look into modernize their analytics platforms by starting adopting the concept of data lakes. Data lakes store information in its raw and unfiltered form, be it structured, semi structured or unstructured. As opposed to the stand-alone enterprise data warehouse (EDW), data lakes perform little automated cleansing and transformation of data, allowing data to be ingested with greater efficiency. Larger responsibility of data preparation and analysis goes to business users.
With Hadoop’s distributed file system (HDFS), data lakes offer low-cost solutions for efficiently storing and analyzing many types of data in its native form. A data lake solution with a data warehouse defines the next generation of BI and offers an optimal foundation for data analysis.
Understand Your Data within a Modern BI Environment
According to an article from Deloitte, data lake does not provide a one-size fits all solutions for every data types. The higher the complexity and veracity of the data, the greater the need to cleanse, transform and organize the data.
A BI platform without data management is a data swamp, a place where data goes in, but is not able to be retrieved or provide the desired value. Modern BI data management focuses on increasing the value.
Governance, Metadata and Security
Governance is typically defined as an internal body that helps organizations oversee the changes to analytics solutions and processes, resolve analytics or data issues and facilitate decision makings. The governance body helps prioritize data sets to be ingested into data lake, defines best practices for performing analyses and creating efficient self-service data set and set the criteria for publishing data sets for other users.
Metadata management can be best illustrated by considering a library. It is important to catalog, index and describe each book such as genre, publication date and author name. In the same way, design a metadata process from the beginning enables efficient data organization and trust throughout the pipeline, preventing data lake from degrading. An effective metadata management enables shared knowledge of how data is defined and related, expediting future analyses.
Security plays a key role in the development and proper use of the data lake solution. Comprehensive identify management and authentication systems are key to controlling access to content stored in the data lake. Role-based access and security groups offer a way to regulate which users have the ability to access and interact with the data lake, minimizing the risk of accessing potential sensitive or confidential data.
Magic Quadrant for 2020 for Analytics and Business Intelligence tools
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