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Learn why Microsoft was named a Leader in the April 2023 Gartner® Magic Quadrant™ for Analytics and Business Intelligence Platforms.
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Swiggy, India’s leading food ordering and delivery platform, has implemented Power BI to track near real-time metrics across its rapidly growing business, enabling data-driven decision making.
Fairlife dairy reduced the time it takes to generate reports on key metrics from nearly seven hours to almost instantaneously by using a single source of truth with always-up-to-date data.
Hendrick Motorsports used Power BI to generate pit performance reports within minutes of a race, instead of their previous wait of up to two days, for faster pit stop feedback.
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Professional services firm Grant Thornton has created a portal for customers to view Power BI content using Azure Active Directory B2C. As a result, their customers now have secure, self-service access to financial information.
PwC used Power BI and Azure Synapse Analytics to create a reporting and analytics solution that was customizable to their clients’ needs, saving time and IT resources and providing deep insights.
Microsoft ranked highest for Ability to Execute and furthest for Completeness of Vision in the April 2023 Gartner® Magic Quadrant™ for Analytics and Business Intelligence Platforms.
Find out how companies drove revenue, cost savings and productivity in Microsoft PowerBI’s Total Economic Impact™ commissioned study conducted by Forrester Consulting.
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“It got our key decision makers off the weeds by giving them the metrics they need to do their job, and they don’t have to manipulate the data to get answers.” Flexibility, interactivity… get started.
“I loved unlocking stories with data from people they didn’t know existed. Power BI gave us the ability to easily visualize our data and automate a lot of the preparation and reporting.
“Power BI helps me accelerate iteration and implementation, creating robust and scalable solutions for a wide variety of industries.”
“It’s very quick for someone with little or no experience or technical knowledge to pick up the tools and start creating things, which changes the way they do their own work.”
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Build powerful end-to-end business solutions by connecting PowerAutomate on Microsoft PowerPlatform, as well as Microsoft365, Dynamics365, Azure, and hundreds of other applications, to drive innovation across your organization.
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Quickly create and launch custom low-code business websites: unify your data and connect customers to vital information and services.
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Gartner added Big Data to its 2011 hype cycle and called it one of the 10 most important strategic technologies for 2012, stating, “The size, complexity of formats, and speed of delivery exceed capabilities of traditional data management technologies; it requires the use of new or exotic technologies just to manage the volume alone”. Big Data has few key characteristics such as volume, sources, speed, variety and veracity.
Data is becoming the world’s newest resource for competitive advantage among nations, organizations, and businesses. It is estimated that every day we create 2.5 quintillion bytes of data from a variety of sources. These range from computer notes to posts on social media sites and records of purchase transactions to images.
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These data sets that are so large and complex and difficult to process using available database management tools are known as Big Data. Big Data challenges include capturing, curating, storing, searching, sharing, transferring, analyzing and visualizing data.
Big data has few key characteristics such as volume, sources, velocity, variety and veracity. The first of these is volume. Experts predict that by 2020 the volume of data in the world will reach 40 zettabytes. It affects all businesses, governments and individuals. According to a recent study, 2.8 zettabytes of data were created in 2012 and only 0.5% of that data was used for analysis. Unstructured data, such as texts, notes, journals make up a large portion of this data volume and these require text mining to analyze the data.
Business data is also growing at this same exponential rate. Along with the volume, the number of sources from which data is extracted is also increasing. Data is increasingly accelerating the rate at which it is created as the process moves from batch processing to real-time activity. The company’s requests for this data have also increased, from a response next week to a response within a minute.
Business Intelligence (BI) provides standard OLAP-based business reports, ad-hoc reports on past data. These ad hoc analyzes examine the static past of the data. This has its purpose and business uses, but does not meet the needs of a forward-looking business. Big data foresight analysis requires statistical analysis, statistical forecasting, occasional analysis, optimization, predictive modeling and text mining on the vast amount of data available. There are performance issues when this high-volume past data is used in the relational data model, for forward-looking analysis of big data, for the future in the current system landscape of many organizations.
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Big Data Analytics will help organizations provide a holistic view of the drivers of their business by bringing Big Data technology into the organization. It is the application of advanced analytical techniques to very large data sets. These cannot be achieved by standard data warehousing applications. These technologies are hadoop, mapreduce, massively parallel processing databases, in-memory database, search-based applications, data mining grids, distributed file systems, distributed databases, cloud, etc.
With big data analytics, relevant information from the data warehouse in terabytes, petabytes, and exabytes can be extracted and analyzed to transform business decisions for the future.
Part of the reason big data is a hot topic right now is because technology -MapReduce, Hadoop, in-memory database, massively parallel processing database, database grids, research, etc. is now available to process these large datasets which are mostly a combination of structured and unstructured data. Thanks to these technologies, it is now possible to bring insights from this data into the day-to-day decision-making process.
MapReduce was created by Google in 2004. It is a model inspired by mapping and reduction functions to process large datasets with a parallel and distributed algorithm on a cluster.
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Hadoop is an open source Apache implementation project. It was created by Yahoo in 2004 to implement the MapReduce function. Hadoop allows applications to work with huge amounts of data stored on different servers. Hadoop has a large-scale file system known as Hadoop Distributed File System or HDFS, which can write programs, manage program distribution, accept results, and then generate a result set of data.
Data in main memory can be accessed faster than data stored on hard drive or other flash storage device. A database management system that relies primarily on main memory for storing computer data is called an in-memory database.
Massively parallel processing is a loosely coupled database where each server or node has memory or CPUs to process data locally and the data is partitioned across multiple servers or nodes.
Search-based applications are a search engine platform used to aggregate and classify data and use natural language technologies to access data.
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Data mining grids are an environment that uses grid computing concepts, which allows integration of data from various online and remote data sources.
Distributed file system is a shared file system which is shared by being
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