Service segment is anticipated to grow at the fastest CAGR of more than 30% through 2025 owing to aggrandized demand for planning, maintenance, training and support services and personnel associated with augmented analytics market.
Analytics uses natural language processing and machine learning to automate data analysis and representing the insights. This has facilitated non-technical users to automate key aspects of their data analytics by amalgamating artificial intelligence (AI) with business intelligence (BI). These platforms have benefited end-users on two fronts. First, data scientists and technical analysts are freed from basic reports and running routine. This has empowered them handle complex data science projects and queries. Second, similar to interacting with Siri and google, non-technical users have augmented analytics solutions by asking questions and getting answers instantly. Thereby, drastic reduction in reporting time and accelerating performance and strategy.
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The technology has major benefits for marketers and non-technical users as all their daily operations are transformed with help of augmented analytics. Brand managers, chief marketing officers and other employees rely on an analytics team for data analysis and reporting. Beginning with one-off questions to profound research and planning, all these users depend on third parties and outsourcing which is costly and inefficient. Augmented solutions have transferred the power back to the marketing users. As explained in “CPG Analytics: The Definitive Guide,” “From opportunity analysis to churn analytics to attribution studies and even customer journey mapping, new advancements in business intelligence make it so that you can make quick and effective decisions.”
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“How Augmented Analytics Is Re-shaping The Businesses And Analytic Innovation
The amount of data generated now-a-days is endless, data analysis has become an imperative component of business advancement. To be on the side of competitive advantage, businesses requires to remain updated with the up gradation of business analytics. In 2017, the expression “smart data discovery” emerged and at present it is dominant as differentiator crosswise over various businesses. To get a clear overview and understanding over the topic, most organizations are currently focusing on creation of models and bring together comprehensive information for streamlining it and tasks automation. This has opened gates of immense opportunity for the skilled data scientists, widely in the market.”
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Business enterprises have adopted AA to drive technological disruption. Chevron Corp. is an early adopter of Google’s AutoML technology. The technology was designed to aid users who lack of machine learning expertise to create and analytical models. Chevron’s seismic imaging and processing team implemented alpha version of AutoML Vision image analysis tool to see through internal documents to evolve new opportunities for oil drilling. To segregate documents that have spatial information related to prospective oil locations in the Gulf of Mexico, the Chevron team started a search on geologic map to emphasize documents that contains embedded map images. The next step is to run an analytic model built on AutoML Vision which was created in a way that identify 60 plus geologic labels on the map images.
The application of augmented analytics has surged with the increasing market player’s participation. The leading market players include Tableau Insights, IBM Corporation, Qlik, Tibco Software, SAP SE, SAS Institute, Microsoft Corporation, Salesforce.com, Inc., Sisense Inc. and ThoughtSpot, Inc. These companies have uplifted the competition among themselves by implementing different strategies including partnerships, joint ventures, expansions, mergers & acquisitions, collaborations to gain a stronghold in the market.
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Case Study: Leveraging Google’s Cloud AutoML Vision For Image Recognition
Google Cloud has heavily invested in security and augmented intelligence. The main goal behind creating Google Cloud to reduce the barrier of entry and make artificial intelligence tools accessible to the largest community of researchers, businesses and developers. Cloud AutoML will aid artificial intelligence experts to be more productive, discover new fields and assist less-skilled engineers to create powerful artificial intelligence systems. Basically, AutoML stands for automated machine learning, which helps to build a similar or new model automatically and business can have it in customized form by imparting its own data-set. This study focuses this technology’s effective adopt
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