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The Top Five Best Augmented Analytics Tools of 2024!

Discover the top five best-augmented analytics tools of 2024! Enhance your data insights with advanced AI-driven solutions designed for smarter decision-making.

Table of content
Introduction
1. Yellowfin
2. Sisense
3. QlikView
4. Kyligence
5. Tableau
Winding Up

Introduction

In this digital age, data is the new oil, especially with the emergence of augmented analytics as a game-changing tool that has the potential to transform how businesses harness this vast technological resource for strategic advantages. Earlier, the whole data analysis process was tedious and manual, as each project would have taken weeks or months to get executed. At the same time, other teams had to eagerly wait to get the correct information and further make decisions and actions that would benefit the business’s future. 

Therefore, to pace up the business process, the data science team required a better solution to make faster decisions with deeper insights. That’s where an organization needs to depend on tools such as augmented analytics. Augmented analytics combines artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) to enhance the data analytics processes, making them more accessible, faster, and less prone to human error. Furthermore, augmented analytics automates data preparation, insight generation, and visualization, enabling users to gain valuable insights from data without extensive technical expertise. 

In today’s exclusive AITech Park article, we will take a quick look at the top five augmented analytics tools that data scientist teams can depend on to democratize advanced-level analytics with augmented data ingestion, data preparation, analytics content, and DSML model development. 

1. Yellowfin

Yellowfin specializes in dashboards and data visualization that have inbuilt ML algorithms that provide automated answers in the form of an easy guide for all the best practices in visualizations and narratives. It has a broad spectrum of data sources, including cloud and on-premises databases such as spreadsheets, which enables easy data integration for analysis. The platform comes pre-built with a variety of dashboards for data scientists that can embed interactive content into third-party platforms, such as a web page or company website, allowing users of all expertise levels to streamline their business processes and report creation and sharing. However, when compared to other augmented analytics tools, Yellowfin had issues updating the data in their dashboard on every single update, which poses a challenge for SMEs and SMBs while managing costs and eventually impacts overall business performance. 

2. Sisense

Sisense is one of the most user-friendly augmented analytics tools available for businesses that are dealing with complex data in any size or format. The software allows data scientists to integrate data and discover insights through a single interface without scripting or coding, allowing them to prepare and model data. Eventually allows chief data officers (CDOs) to make an AI-driven analytics decision-making process. However, the software is extremely difficult to use, with complicated data models and an average support response time. In terms of pricing, Sisense functions on a subscription pricing model and offers a one-month trial period for interested buyers; however, the exact pricing details are not disclosed. 

3. QlikView

QlikView is well-known for its data visualization, analytics, and BI solution that aids IT organizations in making data-based strategic decisions with the help of sophisticated analytics and insights drawn from multiple data sources. The platform allows data scientists to develop, extend, and embed visual analytics in existing applications and portals while adhering to governance and security frameworks. However, some users have reported that the software may slow down when assembling large datasets. Additionally, the software sometimes lacks the desired feature and depends mostly on plugins from the older QlikView, which lacks compatibility with the updated Qlik Sense. The QlikView comes in three pricing plans: Standard Plan: $20/mo for 10 full users only, with up to 50GB/year data for analysis, Premium Plan: starts at $2,700/mo and 50GB/yr data for analysis and more advanced features and  Enterprise Plan: Custom pricing, starting at 500GB/yr data for analysis.

4. Kyligence

The fourth augmented analytics tool that data scientist teams use is Kyligence, as it stands out for its automated insights and NLP technology for businesses to generate deep insights within seconds. The technology also promises a centralized, low-code platform that emphasizes a metrics-driven approach to business decision-making and further identifies the ups and downs of the given metrics, along with discovering root causes and generating reports within seconds. However, the tools are considered to be quite complex and expensive when compared to other augmented analytics tools on the market. Kyligence comes in three pricing plans. Standard plan: $59/user/month, Premium plan: $49/user/month (minimum 5 users), and Enterprise+ plan: Flexible pricing and deployment options.

5. Tableau

Last but not least, we have the famous Tableau, an integrated BI and analytics solution that will help in acquiring, producing, and analyzing the company’s data and provide insightful information. Data scientists can use Tableau to collect information from a variety of sources, such as spreadsheets, SQL databases, Salesforce, and cloud applications. Talking about the interface, it is quite easy regardless of your technical skills, allowing you to explore and visualize data effortlessly, but professionals at an executive level might have issues adapting to this technology. However, the most concerning part of this software is its high pricing and lack of customization in terms of visualization options. In terms of pricing, Tableau comes with two exclusive plans: for an individual user, it is $75/month, and for two users, it is $150/month.

Winding Up

With the importance of data, data analytics, and augmented analytics tools, data scientists are paving the way for effortless and informed decision-making. The five tools listed above are designed to automate the complex data analysis process.

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