As smart technologies continue to evolve, industries such as healthcare, manufacturing, finance, construction, education, retail, etc., are taking advantage of modern solutions.

Augmented Analytics is one such emerging technology that has completely changed the way people perceive data analytics, data manipulation, monitoring, and, data literacy. The popularity of augmented analytics is because it adds value to the analytics lifecycle, insight delivery, and the entire process of data preparation, and analysis.

Considering that it is an emerging technology, it is natural for people to have a number of questions about it. In this article, we have answered some frequently asked questions (FAQs) about augmented analytics so you can understand it better.

Here are the FAQs –

1. What is Augmented Analytics?

Augmented Analytics is an automated system that gathers and analyses data. For this, it uses  Machine learning and natural language processing. Augmented Analytics uses a combination of NLP (natural language processing) and ML (machine learning) to automate the generation of meaningful insights.

Augmented Analytics reduced the dependency on data scientists to scan through and analyze data. Thus, organizations can use natural language searches to unlock the power of data themselves.

2. Why is Augmented Analytics important?

Using Augmented Analytics, it is possible for people to access data insights and to better comprehend and act on data. The data insights can further be used to come up with actionable practices to solve complex business challenges. These actionable insights are beneficial since they serve as a compass for what should be the priority of the company.

3. Is there a minimum amount of data that an organization must have in order to effectively use augmented analytics?

People think of AI and machine learning when it comes to handling huge data. However, these technologies scan through the already existing data and detect its outlines, and this doesn’t require a lot of information. Some predictive analytics and AI use cases for spend analysis will require additional data, but there is no particular threshold. Rather, the more data you have and the more precise it is, the better off you will be.

4. What is the objective of Augmented Analytics?

Augmented Analytics makes it easy to gain deeper, technical insights from data; therefore, data analytics experts and business executives consider it a boon. Using augmented analytics, businesses are able to track data, create questions, and gather valuable insights. Businesses can also use Augmented Analytics to draft business plans and implement them faster. 

5. How can businesses benefit from augmented analytics?

With more efficient and reliable data, augmented analytics has the potential to help business analysts work better. It also helps domain experts overcome business-associated technical constraints, especially if it is intended to run the finest of Machine Learning and NLP technology.

The following are some of the primary advantages of employing Augmented Analytics for business:

Accelerated insights

Augmented analytics supports getting the appropriate data to the right location at the right time. Its ability to track user behavior helps professionals in customizing the action plan and fine-tuning it based on user actions in the past.

Precision at its best

Augmented Analytics solves complex calculations using AI and ML; additionally, it runs over repetitive tasks and offers the most precise data required for efficient decision making.

Efficiency from Automation    

Through the use of AI and ML, Augmented Analytics brings progressive algorithms together to work on specialized tasks. Thus, the technology brings down the number of resources and the amount of time needed to collect and process data. This entails a considerably efficient approach to data gathering, preparation, analysis, and reporting.

Better approach to context analysis

Everything that helps people simplify things is referred to as augmented technology. Augmented Analytics employs technologies like machine learning and artificial intelligence to mimic human intuition in order to make better decisions. Because it is impossible for the human mind to consider all of the conceivable permutations and combinations while analyzing data, Augmented Analytics might assist with useful conclusions.

6. What are the features unique to Augmented Analytics?

Considering the fact that Augmented Analytics is more of a futuristic business approach, businesses need to sharpen their focus on software testing and system development to yield maximum benefits from their data. When done with precision, Augmented analytics introduces businesses to a range of business-centric features –  

• Automated Data Detection: Augmented Analytics, through the use of Business Intelligence (BI) platforms, could help businesses with automated data detection. It does so using specific data attributes.  
• Statistical Operations: Using statistical operational algorithms, Augmented Analytics systems can predict on data clusters. This is something that some systems can do automatically, while others can be explicitly directed to highlight specific groups of important data. In other words, it does not require users to perform complex computations or have any coding knowledge.
• Intelligent Data processing: Augmented Analytics allows users to index data without manual intervention. Thus, the task of updating values and fields becomes simple.
• Advanced Recommendations: Augmented Analytics also offers AI-driven recommendations that can be extremely helpful for businesses with data preparation for analysis and reporting. Additionally, AI and ML can be used to segment analytical content to be used by different user groups. 
• NLP-based query processing: Augmented Analytics also provides users with Natural Language Processing (NLP) capabilities, allowing plain language to be processed for data interpretation. 

7. What are some Augmented Analytics implementation challenges?

The Myths Surrounding AI & ML 

It is well known that AI and ML are complicated technologies. With a similar notion, most business executives perceive Augmented Analytics systems to be difficult to interact. Additionally, there are beliefs that AI is taking over human jobs. This makes it difficult for people to trust advanced technology. 

The Limitations of Augmented Analytics 

While there are high expectations and significant results associated with Augmented Analytics, it comes with a significant drawback – the inability to assess costs. As a result, users must first comprehend their purpose before implementing the technology, which should be done after thorough consideration of Quality Assurance and testing.

System Literacy Requirements 

This is yet another important challenge linked to the implementation of Augmented Analytics. This means that the person using Augmented Analytics needs to know the type of data to be fed into the system. For this, people need to think critically to gain better analytic proficiency. 

Governance & Management 

This challenge is primarily based on data reliability for improved traction during deployment. 

The Ethics 

With AI technology becoming more advanced than ever before, it is vital that any data user needs to be ethical. This entails using non-biased models and only processing data that provides value and can be trusted for major choices.

8. What are the things to consider before investing in Augmented Analytics?

Implementation

As discussed previously, there are certain implementation challenges associated with Augmented Analytics – and the Augmented Analytics solutions work only when they are implemented right. So it is important that businesses consider all the implementation challenges before venturing into it.

Before  investing in Augmented Analytics, businesses need to ask

• Where is the data located? In the cloud or on-prem?
• Is there a need for a hybrid solution?
• How ready are the employees to adopt Augmented Analytics?
• What kind of training do they need?

Future-Proof Architecture

It’s also crucial to think about a solution’s adaptability in an increasingly digital environment, where data is a sensitive and dynamic topic and new technologies emerge at a rapid pace.

Businesses may now collect more data than ever before thanks to the increasing importance and traction of internet platforms. This influx of data gives an opportunity for firms to gain greater insights into their customers, but it also poses a difficulty in terms of determining how to maintain and manage this data.

Furthermore, with changes such as the GDPR, businesses that rely on e-commerce and online communication channels must consider changing legislation when deciding how to collect data online, necessitating the use of an agile solution.

As a result, augmented analytics must account for the increasingly complex data structures seen in multichannel sales and marketing.

Core Capabilities

Data is definitely an invaluable asset.

While augmented analytics has the potential to help businesses make the most of their data, not every augmented analytics solution will be able to meet these demands.

Though augmented analytics can be used by a variety of technologies to answer queries, keep in mind the differentiators highlighted, such as:

• Whether the system, in addition to NLG, can handle natural language processing.
• Can the tool answer “why”, “how”, and “what” questions.
• The degree to which your data visualizations will be adaptable and interactive.
• What types of insights are generated, and how detailed and actionable are they?
• How will the system be designed to handle complex data?

Conclusion

In this blog, we have answered the FAQs about Augmented Analytics. Knowing the answers to these questions provides a better understanding of this evolving technology, and why it is so important. 

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