10 Top Analytics Tools to Boost Your Brand! – CMSWire

According to a 2023 Statista report, by 2025, the amount of data that is created, consumed and stored is projected to grow to over 180 zettabytes. The use of data analytics is essential for brands looking to gain actionable insights that inform strategic decisions and optimize performance based on the vast amounts of data they produce. To make the most of big data, choosing the right analytics platform is key. The top data analytics solutions provide powerful, accessible analysis of customer, marketing, operational and IoT data. This article will examine the top all-in-one and specialized data analytics platforms that are empowering brands today.

Features of a Best of Breed Data Analytics Platforms

Business data analytics is a rapidly evolving technology, and according to an August 2023 Statista report, by 2029, the big data analytics market is expected to reach over $655 billion U.S. dollars. In fact, although 57% of leading businesses indicated that they are using data to drive innovation, only 27% feel that they have successfully created a “data-driven” brand. This is indicative that there is still much work to be done using best of breed data analytics platforms.

Edlyn Collanto, a B2B marketing research specialist at UpCity, a B2B service provider, told CMSWire that data analytics trends include the rising adoption of AI and machine learning for predictive analytics, real-time data processing, and the growing emphasis on data privacy and security in compliance with evolving regulations like GDPR. “Additionally, data democratization and the move toward cloud-based analytics solutions are shaping the way brands harness data to make informed decisions and enhance their competitiveness in the market,” said Collanto.

A best of breed data analytics platform embodies a myriad of robust features tailored to empower brands with the ability to extract valuable insights from their data, facilitate informed decision-making, and drive business strategies forward. Among these features, the following stand out:

• Ease of Use: A platform that boasts intuitive interfaces and straightforward functionality enables users to navigate and use analytics tools with minimal learning curve, regardless of their technical knowledge. 
• Data Integration and Management: Seamless integration with various data sources and robust data management capabilities are crucial. 
• Scalability: Scalability ensures that the analytics platform can handle growing data volumes and user numbers without sacrificing performance. 
• Advanced Analytics and Machine Learning: Advanced analytics features, including predictive analytics and machine learning, enable brands to unearth deeper insights. 
• Real-time Analytics: The ability to perform real-time analytics allows brands to react promptly to emerging trends and issues. 
• Customization and Extensibility: Offers a high degree of customization and extensibility enables businesses to tailor the analytics environment to their unique needs. 
• Security and Compliance: Robust security frameworks and compliance with industry regulations are vital to protect sensitive data and ensure legal compliance. 
• Collaboration Features: Features that promote collaboration among users encourage a shared understanding and enriched insights. 
• Visualizations and Reporting: Effective visualizations and reporting tools translate complex data into comprehensible insights. 
• Support and Training: Comprehensive support and training resources ensure employees can effectively use the platform and troubleshoot issues. 

Each of these attributes contributes to a robust data analytics environment that not only meets the immediate analytical needs of a business but also scales to accommodate future demands. 

Related Article: How AI and Data Analytics Drive Personalization Strategies

Best of Breed Data Analytics Platforms

We have discussed the standout features of best of breed data analytics platforms, and now we will examine each of the top ten data solutions and their specific features, including the pros and the cons of each, along with one additional platform that is unique among the others.

IBM Business Analytics Enterprise: IBM’s Business Analytics suite is robust and caters to a wide range of analytics needs including business intelligence, predictive analytics, and data management. Features a natural language AI assistant, and has the ability to create detailed data visualizations.

• Strengths: Strong integration capabilities. Wide range of solutions. Support for AI-driven insights through its Watson platform.
• Weaknesses: Cost could become a significant concern, especially for larger projects. A lack of new features, uses older technologies.

Tableau: Tableau is recognized for its intuitive user interface and robust visualization capabilities which allow users to create interactive and shareable dashboards. It offers a wide array of attractive visualizations to represent datasets, seamlessly connecting to various data sources.

• Strengths: Strong visualization capabilities. Intuitive user interface. Wide range of data connectivity options.
• Weaknesses: May come with a steep learning curve for more complex functionalities. Pricing can be high for small to medium businesses.

Microsoft Power BI: This platform includes a wide range of features like data connection, relationships, Power Query, Power Pivot, custom visualizations, and report sharing. It features a strong focus on data security with features like sensitivity labeling, encryption, and access monitoring. Additionally, it supports stream analytics and various data sources such as Excel, CSV, SQL Server, and Web files.

• Strengths: Integrates well with other Microsoft products. Cost-effective solution with a robust set of features. Customizable dashboards and reports.
• Weaknesses: Some users find the interface less intuitive compared to other platforms. Could have limitations in advanced analytical capabilities.

Amazon Redshift: Redshift is known for its scalability, ease of data ingestion, data sharing and collaboration features. It offers secure and reliable analytics, with machine learning capabilities to enhance throughput and processing speed.

• Strengths: High scalability and fast query performance. Strong integration with other AWS services. Supports machine learning for optimized query processing.
• Weaknesses: Can be expensive. Management and optimization may require specialized skills.

Adobe Analytics: Adobe Analytics is an enterprise-level web analytics platform known for its powerful segmentation and customization capabilities. It provides granular, real-time data analysis for large companies’ sophisticated needs, and is considered a more advanced alternative to Google Analytics, geared toward large enterprises.

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• Strengths: Offers a high degree of customization, in-depth segmentation, and real-time analytics. Highly integrated into Adobe Experience Cloud, simplifying the tech stack for those brands using it.
• Weaknesses: Steep learning curve due to its complexity. Somewhat prohibitive cost barrier. Time-consuming and complex initial setup. Typically requires specialized training.

SAS Viya: SAS provides a cloud-native AI and analytics platform known as SAS Viya. It offers features such as forecasting, multivariate, descriptive, and statistical analysis. SAS platforms also provide advanced analytics, dashboards, predictive analytics, and real-time analytics to help improve business practices and provide actionable insights.

 

• Strengths: Comprehensive analytics and statistical analysis capabilities. Strong support for advanced analytics and machine learning. Wide range of industry solutions.
• Weaknesses: Can be expensive, especially for smaller businesses. May have a steep learning curve for non-technical users.

Looker: Looker is part of Google Cloud, and serves as a business intelligence and big data analytics platform that provides a wide range of connector options for common data sources. It features real-time business analytics, generative AI, data modeling capabilities, and collaboration features, making it a user-friendly and highly customizable platform.

• Strengths: Strong data modeling and exploration capabilities. Supports a wide range of data sources. Good collaboration features.
• Weaknesses: A high learning curve for LookML (Looker’s modeling language). Some users may find the visualization capabilities less advanced compared to other platforms.

Google Analytics: Google Analytics, now called GA4, is a user-centric, event-based analytics platform built for a cookieless world, and offers a comprehensive look at user interactions across web and mobile applications. It leverages machine learning to uncover actionable insights to inform marketing and CX strategies.

• Strengths: User-centric analytics that provide a 360-degree view of user behaviors, cross-platform unity, engineered to respect user privacy, and uses machine learning to predict the future actions of users.
• Weaknesses: Learning curve and data migration issues when transitioning from Universal Analytics (GA3), limited third-party integrations, and a lack of specialized reporting features that were included in GA3.

Zoho Analytics: This self-service business intelligence platform features data visualization, reporting, and dashboard creation for small to medium enterprises (SMEs). It provides analytics capabilities without extensive IT or data science expertise. The cloud-based solution is accessible for smaller organizations or departments within large enterprises.

 

• Strengths: User-friendly interface, drag-and-drop functionality, a wide range of affordable subscription options, and connects to a variety of data sources.
• Weaknesses: Lacks some of the advanced features and customizations of other analytics platforms, data visualization options are limited, performance constraints for large scale big data or real-time analytics, and limited educational resources and customer support.

Splunk: Designed to manage big data and analyze machine data either on-premises or in the cloud, Splunk’s features include data visualization, performance metrics, data collection, real-time search, indexing, KPI tracking, reporting, and monitoring. Splunk provides machine learning and AI capabilities to improve security and business outcomes.

• Strengths: Powerful for searching, monitoring, and analyzing machine-generated data. Real-time analytics and machine learning capabilities, along with robust security features.
• Weaknesses: Can get expensive as data volume increases. Query language may come with a learning curve.

There are many valuable data analytics tools available today, but we would be remiss if we didn’t include one more: Exasol. It is unique in that it is an in-memory, MPP (Massively Parallel Processing) database that is specifically designed for analytics and business intelligence. It was engineered to manage vast amounts of data and perform complex queries at breakneck speeds. 

Cesar Picco, senior software engineer for T-Mobile USA, told CMSWire that he was brought to T-Mobile to help automate the data processes across various functions, including day-to-day workflows. “One of the first tasks I was responsible for was replacing an outdated SQL server database, and that’s when it struck me that we had too many slow data processes in place — we needed something faster which led me to Exasol.”

Picco said that one of the immediate benefits that became apparent was Exasol’s speed and high-performance. “My team was impressed by the sheer volume of rows of data that we were able to process in record-time. It also helped us innovate internal processes.” Picco explained that Exasol helped his team on-premises and in the cloud, meeting them where they needed to be — without ever sacrificing performance or increasing costs.

 

• Strengths: One of the fastest databases available, due to its in-memory architecture and MPP capabilities. Scales linearly. Support for various data connectors and programming languages. Ability to handle numerous users simultaneously without sacrificing performance.
• Weaknesses: Initial investment and ongoing costs can be a barrier. Can be complex to set up and manage. It’s not a full-stack analytics solution, and may not be the optimum solution for brands with modest or general-purpose database needs.

The selection of a specific platform would depend on the brand’s specific needs, budget, and the technical expertise of its team. Some platforms may offer superior visualization while others excel in real-time analytics or machine learning capabilities, making a thorough evaluation critical to ensure alignment with business goals and objectives.

Related Article: 5 AI Analytics Trends for CX Personalization

Final Thoughts

Selecting the right platform is crucial for brands aiming to leverage the power of data for strategic decision-making and operational optimization. Whether focusing on visualization, real-time analytics, machine learning, or ease of use, there’s a solution tailored to meet the demands of different business goals and strategies.