The data analytics software market is booming. There are several hundred tools available for you to use. If you’re trying to pick the best data analytics tools for your company, it can be challenging. That’s why I decided to create this list to help you.
Here are my favorite data analytics tools that will greatly assist you in your data analysis endeavors.
Apache Spark
Features:
- Used for big data processing
- Can run on Hadoop, Apache Mesos, standalone or in the cloud
- High-performance
- Companies that use Apache Spark include Uber, Slack and Shopify
Spark Is an integrated analytics engine for Big Data processing designed for developers, researchers, and data scientists. It is free, open-source and a wide range of developers contribute to its development
Spark is a high-performance tool and works well for batch and streaming data. It can also be used interactively from the Scala, Python, R, and SQL shells as well.
Spark includes libraries such as SparkSQL for SQL and structured data, MLlib for machine learning, SparkStreaming for live data stream processing, and GraphX for graph analytics.
Pros:
- High-performance
- Can access a diverse set of data sources
- Can be used interactively from the Scala, Python, R, and SQL shells
Cons:
- Software isn’t user-friendly
- High memory usage
- Doesn’t have much documentation
Learn more about Apache Spark here.
Excel
Features:
- Widely popular piece of software available on most office systems
- Easy to pick up and use for basic analysis
- Good for performing statistical analysis
- Used by more than 750 million users across the world
Excel is a spreadsheet and a simple yet powerful tool for data collection and analysis. Excel is not free, as it comes as a part of the Microsoft Office “suite” of programs. It is also readily available, widely used and easy to learn and start data analysis with.
The Data Analysis Toolpak in Excel offers a variety of options to perform statistical analysis of your data. The charts and graphs in Excel give a clear interpretation and visualization of data. The Analysis Toolpak feature needs to be enabled and configured in Excel, as seen here:
Once the Toolpak has been set up, you will see the list of tools. You can choose the tool based on your goals and the information that you want to analyze.
Pros:
- Easy to organize data
- Built-in formulae and calculation makes it easy to get started right away
Cons:
- Human error is very possible with the way that excel works
- Not good for large-scale analysis as a business scales.
SAS
Features:
- Used in business intelligence
- Widely used in the pharmaceutical industry, BI, and weather forecasting
- Google, Facebook, Netflix, Twitter use SAS
- SAS is used for clinical research reporting in Novartis and Covance, Citibank, Apple and Deloitte for predictive analysis
SAS is a statistical software suite widely used for BI (Business Intelligence), data management, and predictive analysis. As a proprietary software, companies need to pay to use it. A free university edition has been introduced for students to learn and use SAS.
SAS has a simple GUI which is easy to learn; however, a good knowledge of the SAS programming knowledge is required to make the most of the tool. SAS’s DATA step (The data step is where data is created, imported, modified, merged, or calculated) helps inefficient data handling and manipulation.
SAS’s Visual Analytics software is a powerful tool for interactive dashboards, reports, BI, self-service analytics, Text analytics, and smart visualizations. SAS is widely used in the pharmaceutical industry, BI, and weather forecasting.
SAS’s data analytics process is as shown:
Pros:
- Simple user interface and easy to learn
- Free university edition for students does exist
- 24×7 customer support
Cons:
- Proprietary software that requires payment
You can learn more about SAS here.
Tableau
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.
Pros:
- 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.
Cons:
- Some users would like to see expanded admin and governance functionality.
Microsoft
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.
Pros:
- 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
Cons:
- 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
Qlik
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.
Pros:
- 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.
Cons:
- While its product offering is strong, its overall vendor profile is not as high as industry giants like Microsoft or even Tableau.
ThoughtSpot
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.
Pros:
- 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.
Cons:
- Without the large product portfolio of some vendors, users will need to bring their own related tools, like data preparation applications.
MicroStrategy
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.
Pros:
- 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.
Cons:
- Does not have a high profile in the data analytics market.
Conclusion
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