Gartner defines augmented analytics as data exploration and analysis on analytics and BI platforms using AI and machine learning services for preparing data and generating insights. It also automates the development, management, and deployment of data science, machine learning, and AI model to empower data scientists and business users with no technical capabilities in data science.
Augmented analytics is gaining relevance in direct proportion to the increasing volume of data businesses are generating but are unable to leverage in real-time. Large investments are needed into tools and technologies for analytics teams to be able to draw insights on the one hand. On the other, business users are dependent on the experts to generate the reports to make decisions. But this can be a bottleneck and render all the data useless due to the delays. Therefore, they need self-service capabilities with customizable report generation to suit their requirements and with sufficient controls to minimize security risks.
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Augmented analytics helps overcome these limitations by automating data preparation, delivering insights faster, and facilitating information-sharing collaboration for greater success.
Benefits of Augmented Analytics
Augmented analytics can help businesses make data-backed decisions, respond faster to trends and issues, and improve customer satisfaction. It can strengthen competitive advantage by becoming a key differentiator and breaking barriers to innovation. Some of the benefits of augmented analytics include:
- Greater Agility: With augmented analysis, the data quality improves, thereby increasing the agility and responsiveness of the business. With augmented analytics, data cleaning, blending, and transforming from multiple sources becomes easier and accelerates insight generation.
- 360-deg View of Data: Augmented analysis facilitates a holistic view of data by providing the details, statistics, and insights that enable segregating the irrelevant from the important ones. This helps decision-makers focus on vital insights that can help develop critical strategies and dynamic capacities. A better overview of data allows data scientists to assess the datasets of clients and create elaborate client profiles to identify loyal customers. It can also help with identifying trends and creating strategies to leverage the trends.
- Information-Backed Decision-Making: Rather than rely on gut feel, which has a high chance of failing in a highly dynamic business environment, augmented analytics provides decision makers with insights that improve the quality and outcome of decisions. It also helps with discovering new queries hitherto not obvious to create new opportunities for growth and efficiency.
- Accelerating Decision Making: The insights also enable businesses to respond quickly to insights and trends in real time. Self-service further empowers function heads to identify issues and improve performance quickly, without having to rely on IT team or data scientists. By automating the data management and analytics processes, business users can also improve their productivity and focus on innovation. This removes the chances of human errors, thus enhancing the quality of decisions.
- Cost-Efficiency: Introducing efficiencies, cutting down on waste, reducing errors, and faster decision-making are some of the direct benefits of augmented analysis. These and many other benefits lead to lowering costs and expenses, optimizing resource utilization, and improving revenue generation.
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Features of Augmented Analytics
Some of the key features of augmented analytics that make it the future of analytics include:
- Automated Data Identification: AI enables identifying data attributes and extracting data from a variety of sources, structured and unstructured. This improves the quality of data while expanding the source for deeper insights.
- Use of Statistical Techniques: Statistical algorithms such as forecasting and clustering help with clear insights as well reveal hitherto hidden insights, improving the quality of decision-making. And the good news is, it doesn’t need statisticians or IT experts to reveal these outliers and hidden insights, but are just a click away.
- Faster Data Preparation: Machine Language algorithms help with automated, faster, and smarter data preparation. Clustering or grouping of data is possible based on predetermined criteria, improving the indexing and searching. This also helps with cleaning the data, removing null values, and splitting data fields into different columns.
- Recommendations: Augmented analytics can help create AI-driven recommendation engines to improve data preparation for better discovery, analysis, and sharing.
- Natural Language Interactions: The use of Natural Language Processing services helps with serving queries in natural language, democratizing the query process. Here too, recommendations may pop up suggesting words to improve query quality and gain better insights from the data.
Indium–Enabling Augment Analytics
Indium Software is a cutting-edge data and analytics solution provider with AI and ML capabilities. We work closely with our customers to create AI-based self-learning algorithms whose accuracy improves as errors go down over time. We develop machine learning systems that automate the generation of quick and accurate insights by examining data and learning.
We enable creating intelligent systems that mimic human capabilities and perform repetitive tasks, freeing up resources to focus on innovation and value addition. Using AI-based predictive models, we help our customers create unique services and solutions to address their operational and customer needs better.
We empower our clients to be a step ahead of the competition by:
- Using Natural Language Processing to identify trends, threats, and opportunities.
- Forecasting future trends and prescribing actions using AI/ML-based predictive and prescriptive analytics.
To know more about Indium’s augmented analytics capabilities