Augmented Analytics and How It Can Help

Augmented Analytics and How It Can Help

In today’s fast-paced business environment, data is more important than ever. With the rise of big data, organizations are struggling to keep up with the sheer volume of information at their disposal. Traditional analytics methods are no longer sufficient to uncover valuable insights from all this data, which is why augmented analytics is fast becoming a game changer in the industry.

In this article, we’ll explore what augmented analytics is and how it can help organizations make better decisions, improve their operations, and stay ahead of the curve in their industry. From identifying patterns and trends in data to providing accurate predictions, augmented analytics is a powerful tool that every organization should consider.

What Is Augmented Analytics?

Augmented analytics is a technology that uses artificial intelligence (AI) and machine learning (ML) to analyze large amounts of data and produce valuable insights. It automates the process of data preparation, analysis, and visualization, making it easier for organizations to gain a better understanding of their data and identify areas for improvement.

Benefits of Augmented Analytics

There are several benefits of using augmented analytics, including the following:

More effective decision-making

Augmented analytics allows organizations to analyze large and complex data sets more quickly and efficiently than traditional methods. With the ability to automatically identify patterns and trends in data, organizations can identify valuable insights that would otherwise be difficult or impossible to uncover manually. This can lead to more accurate predictions, improved decision-making, and a greater understanding of the underlying drivers of business performance.

Faster data preparation

You can make data preparation for building, testing, and training machine learning models more efficient with augmented data preparation techniques. This approach allows users to select and merge data sets; perform cleaning, formatting, and enrichment; and even discover new data sets that can help improve the performance of their models. The process is guided by suggestions tailored to the specific needs of the project.

Data democratization

Augmented analytics can help organizations democratize data analysis by making it more accessible to a wider range of users. By providing an intuitive and user-friendly interface, these tools can enable nontechnical users to access and analyze data easily. This can help organizations foster a data-driven culture and empower employees at all levels to make more informed decisions.

Improved predictions

Augmented analytics analyzes data using AI and ML algorithms, which can precisely discover patterns and trends in the data, allowing for better data prediction. It’s used for a variety of purposes, including predictive maintenance, fraud detection, consumer segmentation, and others.

How Augmented Analytics Works and How It Can Help

To understand how augmented analytics works, it’s important to know its three key components:

1. Machine learning

ML is a subset of AI that enables machines to learn from data and provide predictive insights without human input. These models are highly dependent on large amounts of data, and as they process more information, they become increasingly efficient and precise in their interpretations.

2. Natural language technologies

Natural language technologies enable humans and computers to communicate seamlessly through the use of natural language processing (NLP) and natural language generation (NLG). NLP interprets human language for computers, while NLG translates computer code into human language, allowing business professionals to interact with machines through familiar domain and industry terms in a conversational manner.

3. Automation

By automating repetitive manual tasks throughout the data analytics process, ML-driven tools significantly reduce the time needed to build, train, and deploy ML models. This allows organizations to reduce reliance on their IT departments and free up analysts to focus on more important tasks.

Essentially, augmented analytics utilizes machine learning to make data analytics and related technologies more accessible to everyone, even those without technical expertise. This allows for the discovery of valuable insights from raw and unstructured data, resulting in faster data preparation and analytics processes.

When it comes to DevOps metrics, augmented analytics can be particularly useful in identifying patterns and trends in the performance of systems, applications, and user experience. This can help organizations identify areas of improvement, optimize their processes, and ultimately deliver better software to their customers.

Some of the features available with augmented analytics for DevOps metrics include the following:

  • Automated data collection and analysis. This can save time and reduce the need for manual data entry.

  • Self-service. Users can access the analytics tools and data without needing specialized training or assistance from IT or data scientists.

  • Real-time monitoring. This provides up-to-date information on the performance of software and infrastructure.

  • Dashboards and visualizations. These can help users easily understand and interpret the data.

  • Natural language querying. This can make it easier for users to interact with the data and ask questions in a more human-like way.

Augmented Analytics Examples and Use Cases

Augmented analytics can be applied in a wide range of industries and help organizations gain a better understanding of their data and identify areas for improvement.

Here are a few examples of industries that have embraced augmented analytics:

  • Healthcare. Patient data analysis can help identify vital sign patterns that can lead to better treatment decisions.

  • Finance. Financial data analysis can help identify fraud or other financial risks.

  • Supply chain. Augmented analytics can optimize logistics and inventory and transportation management.

  • Retail. Sales data analysis of customer behavior and preferences can help improve sales and customer satisfaction.

  • Manufacturing. Augmented analytics can help optimize production processes and identify areas for efficiency improvements.

  • Human resources. Employee data analysis can help identify trends and patterns that can lead to better recruitment, retention, and performance management.

Challenges of Augmented Analytics

The implementation of augmented analytics comes with its own set of challenges that organizations need to be prepared to address.

Data Literacy and Analytics Proficiency

Organizations face the challenge of ensuring that their employees have the necessary data literacy and analytics proficiency to effectively use augmented analytics. This includes making sure that employees understand the basics of data analysis and that they can use the tools and techniques required to effectively analyze data.

Ethical Use of AI

To maintain ethical and legal standards, organizations must implement AI analytics while adhering to all relevant regulations. This entails protecting individuals’ privacy and ensuring unbiased decision-making when utilizing the data.

Augmented Analytics Limitations

While augmented analytics provides many benefits, it also has its limitations. Organizations need to be aware of these limitations and understand that insights provided by augmented analytics are not always perfect. They may require additional validation and verification.

Misconceptions of AI and ML

Organizations may have misconceptions about what AI and ML can do or the level of accuracy they can achieve. They must understand that AI and ML are not a panacea for all problems. They require a significant investment in data and personnel to be effective.

Data governance and management

Ensuring that the data used in the analysis is accurate and reliable is crucial for achieving meaningful insights. This requires proper data governance and management practices, including regular data backups, encryption, and access controls.


Organizations need to ensure that the augmented analytics solutions they implement can handle large amounts of data and can be scaled to meet the changing needs of the organization. This can be a challenge as the organization’s data needs may change over time and as the solution adapts.

Best Practices for Augmented Analytics

For organizations to fully utilize augmented analytics, they need to implement them effectively and efficiently. Below are some of the key best practices.

Collaborate to build trust

Collaborate with different departments and stakeholders to build trust and buy-in. This includes involving key stakeholders in the planning and implementation. Make sure they understand how the solution will benefit the organization.

By building trust and buy-in, organizations can ensure that the solution is being used to its full potential and that it’s driving business value.

Create a data-driven culture

Create a data-driven culture. This includes should include training and development programs. The programs must ensure that employees have the necessary data literacy and analytics proficiency to effectively use the solution.

Furthermore, organizations must invest in solutions that guarantee the quality of their data and conform to legal and regulatory standards.

Automate your workflows

By automating workflows, organizations can improve efficiency and reduce the risk of errors. This includes automating data integration and management as well as automating the generation of insights and reports.

Start small and align KPIs

Start small and align key performance indicators (KPIs) with the organization’s goals. This includes identifying specific use cases and goals as well as outlining the data and personnel resources required. By starting small and aligning KPIs, organizations can see to it that the solution is meeting its goals and driving business value.

Plutora’s Augmented Analytics Tool and How It Can Help

Plutora’s augmented analytics tool is designed to empower agile business users such as product managers and business analysts with DevOps metrics. It’s a simple and user-friendly platform that offers a variety of features. These features help users perform ETL, create visualizations, and conduct data analysis.

With Plutora’s augmented analytics tool, your application delivery teams can address issues and track outcomes. By utilizing machine learning and artificial intelligence, it can reveal valuable data insights. These insights can help users make better decisions and improve their overall business performance.


Augmented analytics is a powerful tool that can help organizations make sense of their data and make better decisions. Furthermore, as organizations generate more data, augmented analytics will become increasingly important for staying competitive and extracting valuable insights. Investing in an augmented analytics solution can be a step in the right direction for organizations looking to improve their decision-making processes, boost efficiency and productivity, and drive business growth. If you want to learn more about how augmented analytics can help, we’re hosting a webinar on the topic.

This post was originally published on Plutora's blog