If you are reading this, I am assuming that you want to learn about best software to learn for data analytics or about data analytics tools free. Whatever it is, there are many ways in which you could make use of data analytics and tools. Data collection, management and analysis are very important aspects of business now-a-days. If you have heard the term ‘Data Analytics’ then the first thing that comes to our mind is the use of information and technology to extract meaningful insights from complex data sets such as customer purchase habits, demographic details, media behavior and much more. Data analytics is being adopted by businesses in almost every industry with each claiming that it will transform their operations for real and deliver significant growth in the future.
You’re probably wondering, “where do I even start if I want to learn software for data analytics?” There are a bunch of tools out there that you need to consider when trying to find the best software for data analytics. But what are they? What do they have to offer? Where are they useful? Nobody likes that question. Because it’s hard to pinpoint exactly what someone needs when they don’t even know themselves. And that’s just what makes it difficult sometimes: knowing yourself and what you want in a certain scenario. All you want is easy answers, but sometimes things aren’t so simple.
You can set up MonkeyLearn to automatically analyze large collections of data right away using pre-trained models, or you can create your own customized text analysis models in a simple point-and-click interface.
MonkeyLearn’s suite of data analysis tools allow you to classify data by topic, sentiment, intent, and more, or extract relevant information, like names, locations, and keywords. Native integration and a robust API make it easy to connect the tools and apps you already use to MonkeyLearn’s machine learning tools.
To make it really simple to discover insights in your text data, MonkeyLearn Studio provides an in-app data visualization tool, so you can go from data analysis to data visualization all in one place.
RapidMiner is a data science platform that helps companies build predictive machine learning models from data. It’s aimed at data analytics teams that want to tackle challenging tasks and handle large amounts of data, so you’ll need a technical background.
Depending on your needs, you can opt for different solutions, including TurboPrep, which allows you to clean and prepare your data; AutoModel, which provides different algorithms to build machine learning models; and DataStudio, to create a visual workflow and explore your data.
There’s a free trial available for some of these products.
KEY INSIGHT: Even among market leaders, Tableau is a top vendor in the data analytics software tools market. The company was acquired by Salesforce in 2019.
Tableau has built a large and enthusiastic user base due to the depth and quality of its data visualizations. The company’s data analytics platform is known for collecting multiple data inputs, allowing users to combine them, then offering a dashboard display that enhances visual data mining.
Furthermore, data can then be arranged and rearranged to create hierarchical and bin structures with relative ease. All of this advanced data manipulation can be accomplished by staff without an extensive background in data science, yet the Tableau platform is robust enough to reward a user with data science education.
- Tableau has been a data analytics market leader due to its data visualizations. With its acquisition by Salesforce, it’s expected that enhanced capabilities in AI and ML will continue to grow rapidly.
- A good fit for companies of almost all sizes, from large enterprises to SMBs.
- The Tableau Online solution offers a wide array of deployment options for a multi-cloud environment.
- Some users would like to see expanded admin and governance functionality.
KEY INSIGHT: Driven by its Azure Cloud, Microsoft is the leader in hybrid cloud. The company’s Power BI platform benefits from this strength.
In classic Microsoft fashion, the company’s related software products help promote its Power BI analytics tool. For instance, reminders in Excel and Office 365 urge users to adopt it. Consequently, between this built-in advertising and the software giant’s already-sprawling user base, Power BI can justifiably be called the most popular analytics program on the market. This is important because a large user base prompts constant product upgrades, which Power BI certainly benefits from.
Most important: with its deep pockets, Microsoft has built an impressive array of AL and ML functionality, powering the augmented analytics that has become the key differentiator in the data analytics sector. For example, image analytics – clearly a step ahead – are driven by Power BI’s AI feature set.
Significantly, these ML and AI features are driven by the Azure functions built into the Azure Cloud, which are industry-leading.
- Top AI and ML tools offer augmented data analytics
- Very well respected among its large user base
- No company has a more extensive software product portfolio than Microsoft, and Power BI benefits from interoperability with this exhaustive toolset
- The on-premise-only version of Power BI does not offer the depth of functionality offered by the cloud version
- Users must run the product in the Microsoft Azure cloud, as opposed to the other competing clouds that many companies also use
TIBCO Spotfire provides interactive dashboards, visualizations, and predictive and event-driven analytics, to develop unexpected insights immediately on any device. Spotfire is an enterprise class analytics platform that helps both business and technical users quickly explore data to develop actionable insights, without requiring IT intervention. Spotfire meets the analytic needs of users across the enterprise with data discovery and ad-hoc analysis, interactive reporting and dashboards, domain-specific applications, event-driven real-time analysis, and powerful predictive analytics. All these capabilities are delivered from a single product architecture. Spotfire Analyst makes comprehensive analytics fast and easy for a variety of users, allowing them to gain…
• Data Discovery
• Data Wrangling
• Predictive Analytics
• Big Data Analytics
• Location Analytics
• Enterprise Scale Analytics
What is best?
• Data Discovery
• Data Wrangling
• Predictive Analytics
What are the benefits?
• Smart visual data discovery
• Immersive data wrangling
• Predictive analytics your way
TIBCO’s Predictive Analytics, Spotfire Platform includes Spotfire Statistics Services, Enterprise Runtime for R and Predictive Modeling Tool.
Talend offers a suite of cloud apps for data integration. It’s designed to help businesses collect all their data in a single platform so that teams can access the right data when they need it.
The platform has a series of in-built machine learning components, which allow users to analyze data without the need to code. It uses classification, clustering, recommendation, and regression algorithms.
ClicData is an end-to-end business intelligence platform with extensive data connectivity, data transformation, automation and visualization features. ClicData is 100% cloud-based and works on all operating systems and devices.
Within a day, you can easily connect, blend data from various sources and build dashboards with their drag-and-drop interface. They offer self-service BI with online resources as well as full-service BI with in-app support and expert services.
ClicData offers a free trial and four plans that will suit mid-sized and enterprise companies.
Looker integrates with existing tools to introduce new, highly-focused data that can show previously unseen data relationships to help teams make more informed decisions.
Customizable programs and applications ensure that models are designed specifically for individual clients. And many of their “embedded analytics solutions” come pre-designed for industries like retail, healthcare, and more.
KEY INSIGHT: If your organization seeks to use ML and AI to enhance the quality of data mining, the Qlik Sense is a top choice.
With two decades under its belt, Qlik’s combination of strengths offers a compelling vision in the data analytics sector. Chief among them: the company has advanced versions of artificial intelligence and machine learning built into its Qlik Sense platform. And it offers this functionality without requiring deep data science skills, so sales reps and mid-level staffers can leverage AI for data mining.
Also important: Qlik Sense is cloud-agnostic, so companies can deploy the data analytics tools to any cloud in their multi-cloud infrastructure. They can also deploy on-prem, and then hook the application into the cloud for a hybrid data analytics approach.
- The company’s associated insights feature promises to deploy a cognitive application to dig for insights that users might miss.
- Very flexible and strong across public, private, and hybrid clouds.
- Enables upper-level self-service analytics for data scientists, or for users with minimal data science training.
- While its product offering is strong, its overall vendor profile is not as high as industry giants like Microsoft or even Tableau.
KEY INSIGHT: While not as well known as some other data analytics software vendors, ThoughtSpot offers a next-generation “search first” tool that earns it a berth as a leader in the market.
ThoughtSpot offers any number of compelling features, particularly an AI-based recommendation system that leverages crowdsourcing. Additionally, sources for its query options range from a legacy provider like Microsoft to a “new kid on the block” like Snowflake.
But most attractive of all, ThoughtSpot’s calling card in a crowded market is its search-based query interface. Users can input a complex analytics query – by typing or speaking – and the ThoughtSpot platform uses augmented analytics to offer insight. Impressively, it can handle large data queries, with many users sifting through more than a terabyte of information. All of this is accomplished – from comparative analysis to anomaly detection – with no software code required. So business staff can data mine without the help of experts.
- The search interface allows easy queries of complex questions, analyzing billions of data rows with artificial intelligence.
- Founded in 2012 as a growing company, the company has ridden the wave of enterprise analytics to a solid niche in the analytics sector.
- Well regarded for its ability to scale and handle ever-larger query loads.
- Without the large product portfolio of some vendors, users will need to bring their own related tools, like data preparation applications.
KEY INSIGHT: In a bold move, MicroStrategy envisions itself as the foundation of enterprise analytics, by connecting various competing platforms into a unified system.
In a highly competitive data analytics market, where each vendor is trying to top the others, MicroStrategy seeks to join them together. Its platform includes API connectors that join competing platforms while – of course – using MicroStrategy as the unifying layer. In a related technique, the company connects all business content from browser-based systems, like CRM and ERP (and competing analytics software), and then offers it as an easy-to-consume analytics dashboard.
As soon as a user moves their mouse over a link, the data appears – offering updated, real-time data insights through the workday.
Additionally, users who can code can leverage MicroStrategy to quickly insert or update a diverse array of data sources from mobile or across the Internet. This easy update from multiple sources plays into MicroStrategy’s “connector” strategy and is well regarded in the data analytics sector.
- MicroStrategy’s Hyperintelligence linking technology is an innovative twist that may launch it into a leading position in the years ahead.
- Well respected for the stability of its platform, with little or no problem with bugs or downtime.
- Does not have a high profile in the data analytics market.
Every business has the need for data analytics at one time or another, and those that have a need for it all the time are growing at twice the rate of companies who don’t. What does this mean to you? It means that you should consider getting up to speed on data analytics as quickly as possible if you’re reading this.