An introduction to Azure Analysis Services

| March 20, 2017

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Azure Analysis Services is a proven analytics engine that connects and analyzes disparate data sources whether on-premises or in the cloud without having to manage your own infrastructure. Josh Caplan & Jeremy Chapman show you how you can gain insights from your data using analysis services semantic model, the speed of in-memory processing and querying of your enterprise data sets and migrating your on-premises data models to the cloud.

Spotlight

Zeta Interactive

Zeta is a Big Data & Analytics company that uses a People-based Marketing Cloud to help leading brands acquire, grow and retain customers. Founded by David A. Steinberg and John Sculley (former CEO of Apple Computer and Pepsi-Cola) in 2007, Zeta uses big data, advanced analytics and machine learning to power marketing programs for hundreds of Fortune 1000 and Middle Market brands. With over 1,000 employees worldwide, the company is headquartered in New York City, with Centers of Excellence in Silicon Valley, Boston, London, and Hyderabad, India, and 12 additional offices around the globe.

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Is Augmented Analytics the Future of Big Data Analytics?

Article | August 3, 2021

We currently live in the age of data. It’s not just any kind of data, but big data. The current data sets have become huge, complicated, and quick, making it difficult for traditional business intelligence (BI) solutions to handle. These dated BI solutions are either unable to get the data, deal with the data, or understand the data. It is vital to handle the data aptly since data is everywhere and is being produced constantly. Your organization needs to discover any hidden insights in your datasets. Going through all the data will be doable with the right tools like machine learning (ML) and augmented analytics. According to Gartner, augmented analytics is the future of data analytics and defines it as: “Augmented analytics uses machine learning/artificial intelligence (ML/AI) techniques to automate data preparation, insight discovery, and sharing. It also automates data science and ML model development, management, and deployment.” Augmented analytics is different from BI tools because ML technologies work behind the scenes continuously to learn and enhance results. Augmented analytics facilitates this process faster to derive insights from large amounts of structured and unstructured data to gain ML-based recommendations. In addition, it helps to find patterns in the data that usually go unnoticed, removes human bias, and allows predictive capabilities to inform an organization of what to do next. Artificial intelligence has brought about an augmented analytics trend, and there has been a significant increase in the demand for augmented analytics. Benefits of Augmented Analytics Organizations now understand the benefits of augmented analytics which has led them to adopt it to deal with the increasing volume of structured and unstructured data. Oracle identified top four reasons organizations are opting for augmented analytics: Data Democratization Augmented data science availability to everyone has become a possibility thanks to augmented analytics. Augmented analytics solutions come prebuilt with models and algorithms, so data scientists are not needed to do this work. In addition, these augmented analytics models have user-friendly interfaces, making it easier for business users and executives to use them. Quicker Decision-making You will receive suggestions and recommendations through augmented analytics about which datasets to incorporate in analyses, alert users with dataset upgrades, and recommend new datasets when the results are not what the users expect. With just one click, augmented analytics provides precise forecasts and predictions on historical data. Programmed Recommendations Natural language processing (NLP) is featured on the augmented analytics platforms enabling non-technical users to question the source data easily. Interpreting the complex data into text with intelligent recommendations is automated by natural language generation (NLG), thus speeding up the analytic insights. Anyone using the tools can find out hidden patterns and predict trends to optimize the time it takes to go from data to insights to decisions using automated recommendations for data improvement and visualization. Non-expert users can use NLP technology to make sense of large amounts of data. Users can ask doubts about data using typical business terms. The software will find and question the correct data, making the results easy to digest using visualization tools or natural language output. Grow into a Data-driven Company It is more significant to understand data and business while organizations are rapidly adjusting to changes. Analytics has become more critical to doing everything from understanding sales trends, to segment customers, based on their online behaviors, and predicting how much inventory to hold to strategizing marketing campaigns. Analytics is what makes data a valuable asset. Essential Capabilities of Augmented Analytics Augmented analytics reduces the repetitive processes data analysts need to do every time they work with new datasets. It helps to decrease the time it takes to clean data through the ETL process. Augmented analytics allows more time to think about the data implications, discover patterns, auto-generated code, create visualizations, and propose recommendations from the insights it derives. Augmented analytics considers intents and behaviors and turns them into contextual insights. It presents new directions to look at data and identify patterns and insights companies would have otherwise missed out on completely- thus altering the way analytics is used. The ability to highlight the most relevant hidden insights is a powerful capability. Augmented analytics, for example, can help users manage the context at the explanatory process stage. It understands the values of data that are associated with or unrelated to that context, which results in powerful and relevant suggestions that are context-aware. Modern self-service BI tools have a friendly user interface that enables business users with low to no technical skills to derive insights from data in real-time. In addition, these tools can easily handle large datasets from various sources in a quickly and competently. The insights from augmented analytics tools can tell you what, why, and how something happened. In addition, it can reveal important insights, recommendations, and relationships between data points in real-time and present it to the user in the form of reports in conversational language. Users can have data queries to get insights through the augmented analytics tools. For example, business users can ask, “How was the company’s performance last year?” or “What was the most profitable quarter of the year?” The systems provide in-depth explanations and recommendations around data insights, clearly understanding the “what” and the “why” of the data. It enhances efficiency, decision-making, and collaboration between users and encourages data literacy and data democracy throughout an organization. Augmented Analytics: What’s Next? Augmented analytics is going to change the way people understand and examine data. It has become a necessity for businesses to survive. It will simplify and speed up the augmented data preparation, cleansing, and standardization of data, thus assist businesses to focus all their efforts on data analysis. BI and analytics will become an immersive environment with integrations allowing users to interact with their data. New insights and data will be easier to access through various devices and interfaces like mobile phones, virtual assistants, or chatbots. In addition, it will help decision-making by notifying the users of alerts that need immediate attention. This will help businesses to stay updated about any changes happening in real-time. Frequently Asked Questions What are the benefits of augmented analytics? Augmented analytics helps companies become more agile, gain access to analytics, helps users make better, faster, and data-driven decisions, and reduces costs. How important is augmented analytics? Augmented analytics build efficiency into the data analysis process, equips businesses and people with tools that can answer data-based questions within seconds, and assist companies in getting ahead of their competitors. What are the examples of augmented analytics? Augmented analytics can help retain existing customers, capitalize on customer needs, drive revenue through optimized pricing, and optimize operations in the healthcare sector for better patient outcomes. These are some of the examples of the use of augmented analytics. { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "What are the benefits of augmented analytics?", "acceptedAnswer": { "@type": "Answer", "text": "Augmented analytics helps companies become more agile, gain access to analytics, helps users make better, faster, and data-driven decisions, and reduces costs." } },{ "@type": "Question", "name": "How important is augmented analytics?", "acceptedAnswer": { "@type": "Answer", "text": "Augmented analytics build efficiency into the data analysis process, equips businesses and people with tools that can answer data-based questions within seconds, and assist companies in getting ahead of their competitors." } },{ "@type": "Question", "name": "What are the examples of augmented analytics?", "acceptedAnswer": { "@type": "Answer", "text": "Augmented analytics can help retain existing customers, capitalize on customer needs, drive revenue through optimized pricing, and optimize operations in the healthcare sector for better patient outcomes. 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Automotive DevOps Rules of the Road Ahead

Article | March 13, 2020

DevOps will provide over-the-air (OTA), seamless software updates which would allow important and immediate updates without affecting the car’s capabilities through Liquid Software liquid software. OTA updates will enable automakers to fix engine and automotive malfunctions, as well as implement safety standards directly into the program. Tesla is one of the pioneers of over-the-air updates but while its’ cars are off. In total, Tesla’s updates are usually about 30 minutes. Since 2012, hundreds of OTA updates have been sent out by the company to adjust things like speed limit settings, acceleration, battery issues, and even braking distance. Most car manufacturers are behind when it comes to over-the-air software updates.

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How can we democratize machine learning on IoT devices

Article | February 12, 2020

TinyML, as a concept, concerns the running of ML inference on Ultra Low-Power (ULP 1mW) microcontrollers found on IoT devices. Yet today, various challenges still limit the effective execution of TinyML in the embedded IoT world. As both a concept and community, it is still under development.Here at Ericsson, the focus of our TinyML as-a-Service (TinyMLaaS) activity is to democratize TinyML, enabling manufacturers to start their AI businesses using TinyML, which runs on 8, 16 and 32 bit microcontrollers.Our goal is to make the execution of ML tasks possible and easy in a specific class of devices. These devices are characterized by very constrained hardware and software resources such as sensor and actuator nodes based on these microcontrollers.Below, we present how we can bind the as-a-service model to TinyML. We will provide a high-level technical overview of our concept and introduce the design requirements and building blocks which characterize this emerging paradigm.

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How Data Analytics in The Hospitality Industry Can be Helpful?

Article | June 9, 2021

In recent years, we have seen more industries adopt data analytics as they realize how important it is. Even the hotel industry is not left behind in this. This is because the hospitality industry is data-rich. And the key to maintaining a competitive advantage has come down to ‘how hotels manage and analyze this data’. With the changes taking place in the hospitality industry, data analysis can help you gain meaningful insights that can redefine the way hotels conduct business.

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Spotlight

Zeta Interactive

Zeta is a Big Data & Analytics company that uses a People-based Marketing Cloud to help leading brands acquire, grow and retain customers. Founded by David A. Steinberg and John Sculley (former CEO of Apple Computer and Pepsi-Cola) in 2007, Zeta uses big data, advanced analytics and machine learning to power marketing programs for hundreds of Fortune 1000 and Middle Market brands. With over 1,000 employees worldwide, the company is headquartered in New York City, with Centers of Excellence in Silicon Valley, Boston, London, and Hyderabad, India, and 12 additional offices around the globe.

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