5 Costly Dashboard Mistakes That Are Slowing Your Decisions | #powerbi #datanalytics #anaytics
Bad dashboards aren’t just annoying — they’re expensive. In mining and operations, every delayed decision carries a cost. source

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5 Costly Dashboard Mistakes That Are Slowing Your Decisions | #powerbi #datanalytics #anaytics
Bad dashboards aren’t just annoying — they’re expensive. In mining and operations, every delayed decision carries a cost. source

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SEO Analytics for Accountants: Measuring Success and ROI
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Kubient (Nasdaq: KBNT, a cloud-based software platform for digital advertising, today released Kubient Artificial Intelligence (KAI) 2.0, the latest update to its proprietary ad fraud identification and prevention technology. KAI 2.0 comes with the following additions: RelatedPosts Ishtehari co-founder collaborates with Foxymoron co-founder for peace podcast 5 Best tools to organize and optimize your work […]
The KAI 2.0 update comes just two months after the United States Patent and Trademark Office (“USPTO”) issued its patent for KAI in December 2022, which established Kubient as the intellectual property owner of KAI from now until 2040.
The sea of data within telcos, if correctly interpreted and utilized, can lead to greater monetization opportunities and understanding of customers.
Analytics with Druid
Modern applications produce large numbers of events. These events can be users clicking, IoT sensors accumulating data, network events or log messages. There is a lot of requirement to analyze the data as it comes and compare it with historical data to figure out anomalies and patterns.
Stream-processing vs Analytics
Why would you need Druid?
Stream processing technologies help to look…
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How to implement the ETL steps for your data warehouse?
The world of data has been growing exponentially, and the data management industry is totally changed from what it was a few years ago. Around 90% of the current data has been generated in the last couple of years only. According to a report by Domo, our continuous data output is nearly 2.5 quintillion bytes in a day, which means there’s massive data generated every minute. With technological transformation, data has become a critical factor in business success. Above all, processing data in the right way has become a pivotal solution for many businesses around the globe.
If we look back, terms like a data lake, ETL (Extract, Transform, Load), or warehousing would have been baffling for most people, or merely some buzzwords.
Today, data management technology is growing at a fast pace and providing ample opportunities to organizations. Organizations these days are full of raw data that needs filtering. Systematically arranging the data to get actionable insights for decision-makers is a real challenge. Thus, meaningful data accelerates decision-making, and using ETL tools for data management can be helpful.
The evolution of Extract Transform Load (ETL)
Data warehouses and ETL tools were created to get actionable insights from all your business data. There are currently several ETL tools in the market that have expanded functionality for data cleansing, data profiling, big data processing, master data management, data governance, and Enterprise Application Integration (EAI). With the availability of data in the warehouse or Online analytical process (OLAP) cube, a Business Intelligence (BI) software is used to visualize and analyze them. This software helps with reporting, data discovery, mining, and boarding.
The complete process
The process of extracting and organizing raw data, transforming to make it understandable, and loading it into a database or a data warehouse to easily access and analyze it, is known as an ETL process. In short, it’s an essential component in the data ecosystem of any contemporary business.
Since data coming from various sources has a distinct structure, every dataset needs to be transformed differently before using it for business intelligence and analytics. For example, if you organize data from source systems like Google Analytics and Amazon Redshift, these two sources should be treated separately with the whole ETL process.
Implementing the ETL process in the data warehouse
The ETL process includes three steps:
1. Extract
This step comprises data extraction from the source system into the staging area. Any transformations can be done in the staging area without degrading the performance of the source system. Also, if you copy any corrupted data directly from the source into the database of the data warehouse, restoring could be a challenge. Users can validate extracted data in the staging area before moving it into the data warehouse.
The data warehouses should merge systems with hardware, DBMS, OS, and communication protocols. Sources include legacy apps like custom applications, mainframes, POC devices like call switches, ATM, text files, ERP, spreadsheets, data from partners, and vendors. As a result, you need a logical data map before extracting data and loading it physically. The data map represents the connection between sources and target data.
Conclusion
Every business around the world, whether small, mid-sized, or large, has an extensive amount of data. However, this data is nothing without using a robust process to gather it. Implementing ETL in data warehousing provides a full context of your business for the decision-makers. The process is flexible and agile that allows you to swiftly load data, transform it into meaningful information, and use it to conduct business analysis.
What you need to know about advanced data warehousing
An advanced data warehouse, also known as an enterprise data warehouse, serves as a data hub for business intelligence. It is a support system that stores data across the organization processes it, and enables it to be utilized for various business purposes, including reporting, business analysis, and dashboards. A data warehouse system stores structured data from multiple sources such as Online Transaction Processing (OLTP), Customer Relationship Management (CRM), and Enterprise Resource Planning (ERP).
Data warehousing has been assisting organizations with their data storage and analysis requirements for years now, but the introduction of cloud and its integration with data warehousing has changed the dimensions of data governance, storage, and data management. Now, vendors like Azure Synapse are offering robust data warehousing solutions that feature enhanced data quality, data security, and business intelligence analytics to streamline a company’s decision-making process.
The most successful of the eCommerce stores make decisions backed by data. They always know the current scenario of their online store’s performance at all times which enables them to grow and take…