Data analysis is a process of gathering relevant information from different sources and organizing it in a meaningful way. Data plays a crucial role in this process as it helps us to better understand who our consumer is and what he likes to do. In today’s age, consumers have a choice to speak their mind out loud or by writing a review on different websites. Using those data to analyze consumers’ opinion toward your business would allow you to take the correct business decisions, especially with regard to modifying your products or strategizing marketing campaigns that can lure consumers into buying from you instead of your competitors.
1. R
R is now one of the most popular analytics tools in the industry. It has surpassed SAS in usage and is now the Data analytics tool of choice, even for companies that can easily afford SAS. Over the years, R has become a lot more robust. It handles large data sets much better than it used to, say even a decade earlier. It has also become a lot more versatile.
1800 new packages were introduced in R between April 2015 and April 2016. The total number of R packages is now over 8000. There are some concerns about the sheer number of packages, but this has certainly added a lot to R’s capabilities. R also integrates very well with many Big Data platforms, which have contributed to its success.
2. Python
Python has been one of the favorite languages of programmers since its inception. The main reason for its fame is the fact that it’s an easy-to-learn language that is also quite fast. However, it developed into one of the powerful Data analytics tools with the development of analytical and statistical libraries like NumPy, SciPy etc. Today, it offers comprehensive coverage of statistical and mathematical functions.
Increasingly, we are seeing programmers and other tech folks moving into analytics. Most of these guys are already familiar with Python, and therefore, it has become a Data analytics tool of choice for many data
3. Splunk
Splunk is a platform used to search, analyze, and visualize the machine-generated data gathered from the applications, websites, etc. Being named by Gartner as a Visionary in the 2020 Magic Quadrant for APM, Splunk has evolved products in various fields such as IT, Security, DevOps, Analytics.
Products:
- Splunk Free
- Splunk Enterprise
- Splunk Cloud
All these 3 products differ by the bandwidth of the features they offer and are available for free download and trial versions. The pricing options for Splunk products are based on predictive pricing, Infrastructure-based pricing, and also rapid adoption packages.
Companies using:
Trusted by 92 out of the Fortune 100, companies such as Dominos, Otto Group, Intel, Lenovo are using Splunk in their day to day practices to discover the processes and correlate data in real-time.
Recent Advancements/ Features:
Since almost all the organizations need to deal with data across various divisions, according to Splunk official website Splunk aims to bring data to every part of your organization, by helping teams use Splunk to prevent and predict problems with monitoring experience, detect and diagnose issues with clear visibility, explore and visualize business processes and streamline the entire security stack.
4. Talend
Talend is one of the most powerful data integration ETL tools available in the market and is developed in the Eclipse graphical development environment. Being named as a Leader in Gartner’s Magic Quadrant for Data Integration Tools and Data Quality tools 2019, this tool lets you easily manage all the steps involved in the ETL process and aims to deliver compliant, accessible and clean data for everyone.
Products:
Talend comes with the following five products:
- Talend Open Source
- Stitch Data Loader
- Talend Pipeline Designer
- Talend Cloud Data Integration
- Talend Data Fabric
Out of these, few are completely free, few are free for 14 days and few are licensed. All these products differ in their functionalities and pricing options.
Companies using:
Small startups to multinational companies such as ALDO, ABInBev, EuroNext, AstraZeneca are using Talend to make critical decisions.
Recent Advancements/ Features:
Talend is the only platform that delivers complete and clean data at the moment you need it by maintaining data quality, providing Big Data integration, cloud API services, Preparing Data, and providing Data Catalog and Stitch Data Loader.
Recently Talend has also accelerated the journey to the lakehouse paradigm and the path to reveal intelligence in data. Not only this but the Talend Cloud is now available in Microsoft Azure Marketplace.
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5. Apache Spark
Spark is another open-source processing engine that is built with a focus on analytics, especially on unstructured data or huge volumes of data. Spark has become one of the tremendously popular Data analytics tools in the last couple of years. This is because of various reasons – easy integration with the Hadoop ecosystem being one of them. Spark has its own machine learning library, which makes it ideal for analytics as well.
6. Apache Storm
Storm is the Big Data tool of choice for moving data or when the data comes in as a continuous stream. Spark works on static data. Storm is ideal for real-time analytics or stream processing.
7. PIG and HIVE
Pig and Hive are integral Data analytics tools in the Hadoop ecosystem that reduce the complexity of writing MapReduce queries. Both these languages are like SQL (Hive more so than Pig). Most companies that work with Big Data and leverage the Hadoop platform use Pig and/or Hive.
8. Tableau
Tableau at a glance:
- Type of tool: Data visualization tool.
- Availability: Commercial.
- Mostly used for: Creating data dashboards and worksheets.
- Pros: Great visualizations, speed, interactivity, mobile support.
- Cons: Poor version control, no data pre-processing.
If you’re looking to create interactive visualizations and dashboards without extensive coding expertise, Tableau is one of the best commercial data analytics tools available. The suite handles large amounts of data better than many other BI tools, and it is very simple to use. It has a visual drag and drop interface (another definite advantage over many other data analysis tools). However, because it has no scripting layer, there’s a limit to what Tableau can do. For instance, it’s not great for pre-processing data or building more complex calculations. While it does contain functions for manipulating data, these aren’t great. As a rule, you’ll need to carry out scripting functions using Python or R before importing your data into Tableau. But its visualization is pretty top-notch, making it very popular despite its drawbacks. Furthermore, it’s mobile-ready. As a data analyst, mobility might not be your priority, but it’s nice to have if you want to dabble on the move! You can learn more about Tableau in this post.
9. KNIME
KNIME at a glance:
- Type of tool: Data integration platform.
- Availability: Open-source.
- Mostly used for: Data mining and machine learning.
- Pros: Open-source platform that is great for visually-driven programming.
- Cons: Lacks scalability, and technical expertise is needed for some functions.
Last on our list is KNIME (Konstanz Information Miner), an open-source, cloud-based, data integration platform. It was developed in 2004 by software engineers at Konstanz University in Germany. Although first created for the pharmaceutical industry, KNIME’s strength in accruing data from numerous sources into a single system has driven its application in other areas. These include customer analysis, business intelligence, and machine learning. Its main draw (besides being free) is its usability. A drag-and-drop graphical user interface (GUI) makes it ideal for visual programming. This means users don’t need a lot of technical expertise to create data workflows. While it claims to support the full range of data analytics tasks, in reality, its strength lies in data mining. Though it offers in-depth statistical analysis too, users will benefit from some knowledge of Python and R. Being open-source, KNIME is very flexible and customizable to an organization’s needs—without heavy costs. This makes it popular with smaller businesses, who have limited budgets.
10. Qlik
With both cloud and on-premises deployment, Qlik offers helpful tools for those with expansive technical backgrounds or users that are not even fully computer literate. QlikView offers in-memory data processing for super fast results and the visualization of color-coded data relationships makes the results and insights easy to understand.
11. SAS Business Intelligence
The SAS Business Intelligence platform focuses on visualizations that can be easily understood and shared simply across an organization for insights with a clear path to implementing change, in order to streamline processes and improve customer satisfaction.
SAS BI aims to help clients answer specific questions, like “Where do my customers come from?” and “Where are most accidents occurring?”
12. Looker
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.
Conclusion
While big companies usually have in-house data analysis departments that analyze huge volumes of data to find business trends, smaller companies may not have the human resources required for the job. That’s why they turn to automated data-analysis tools to help get the job done.
Software for data analysis is one of the most used tools in every corporate sector. Many companies want to know whether their sales has increased or not. That is why they need to use software for the Data Analysis. But what all types of software are available and which is best among them? So make a decision in this regard and start using any of the software in your company and its benefits.