How The Potential Of Augmented Analytics Will Push The Analytics Adoption By 30 Percent Get Started
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We hear it everywhere that business teams are hungry for analytics. They crave accurate forecasts and predictions to allow them to make CEO more reliable business decisions.
WHY AUGMENTED ANALYTICS ?
The entire exercise of doing business analytics today is a bit time-consuming, for everyone involved. Likewise, getting actionable insights into the hands of everyone is increasingly vital to improving business operations.
This is why you're seeing a new wave of disruption in data analytics tools with the concept of augmented analytics gaining momentum.
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What Is Augmented Analytics? Augmented analytics is the use of enabling technologies such as machine learning and AI to assist with data preparation, insight generation and insight explanation to augment how people explore and analyze data in analytics and BI platforms. It also augments the expert and citizen data scientists by automating many aspects of data science, machine learning, and AI model development, management and deployment. This technology helps in numerous business functions, from decision-making about business deals to identifying prospects. - Gartner
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Augmented Analytics Can Be Broken Down Into Three Parts:
1. Machine Learning (ML) For instance, if you are trying to decide on the best pricing strategy for any of your services, you can use Machine Learning algorithms to automatically examine your customer relationship history and competitors' offers and suggest an appropriate price for a particular customer. 2. Natural Language Processing (NLP) NLP is a conversational AI technology that authorizes human data analysts to interact and query the data using natural language -either in the form of voice or text. These attributes have given rise to self-service analytics. 3. Automated Insights Here, the technology draws together NLP and ML so that system users can get the answers to their questions much faster. For instance, your sales team could ask, "What are the growth projections for Q1 2020?" and receive a visualized answer.
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How Is It Adding Value To Business Intelligence?
Augmented analytics helps alleviate an organization's dependence on manual processes or data scientists by automating the insight generation process with the assistance of AI and advanced machine learning algorithms. The scope of augmentation is extending. Originally intended to assist analyst personas using self-service, augmentation and, increasingly, giving rise to a new user category: augmented consumers. This change has the potential to push Analytics and BI beyond the approximately 30% adoption ceiling that has been in place for many years.
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Let's Look At The Top 4 Benefits Of Augmented Analytics
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The heavy lifting of manually sifting through vast volumes of complex data (due to lack of time or skills constraints) is significantly reduced as the analysis is automated and can always be set run. If your augmented tool finds a spike or drops or change, it can also automate the delivery of that insight, ensuring that users can then act immediately.
1.
Immediate Automated Analysis
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Augmented data preparation brings data together from disparate sources swiftly. Algorithms can be utilized for integrations and repetitive transformation for enrichment recommendations and data quality, and you can even automate the tagging, profiling, and annotation of your data before you start the process of data preparation – resulting in reliable analysis in a fraction of the time.
2.
Faster Data Preparation
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With Natural Language assistance in place, providing automated analysis of results and the explanation of discoveries - can improve their data literacy. With such kind of transformations, this can help in fostering a dataled culture that benefits the organization as a whole for the long-term.
3.
Improved Data Literacy
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Data Analysts can use Artificial Intelligence and Machine Learning, along with Data Science for Conversational Analytics. That means data users of various skill levels can access the data and obtain insights without being expert data scientists.
4.
Conversational Analytics
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1. Smart Cities Smart Cities worldwide are utilizing Augmented Analytics to process huge volumes of collated data. As more Smart Cities adopt and follow this transformational tec hnology in their city administrations, the city management practices will genuinely enter the Digital Age. With these advanced technologies in place, city planners will simulate a "Smart City," predict future and better manage their existing resources.
Augmented Analytics Use Cases
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2. Other Industry
Augmented Analytics Use Cases
In numerous industries, top management uses Augmented analytics to make the data relevant through sophisticated dashboards to make fast decisions. As most of the collated data are sensor-driven, smart technologies such as AR are highly sought after to manage, sort, collect, and display that data in a capsule format so that the management can capture the most critical insights before making their business decisions.
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Conclusion We can say that the world of data analytics is no longer restricted to data scientists, IT, and analysts. If a company is going to be successful and productive today, it must allow its business users to access easy-to-use tools with sophisticated features and functionality so that the entire team can work from the same roadmap and stay on track. Polestar Solutions helps large and medium organizations to generate actionable insights from their data with our advanced and augmented analytics solutions.
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