Data analysis is a process of converting raw data into useful information. The raw data can be in a number of different formats such as figures, images, or sounds etc. Data analysis can be done both manually and with the help of computers depending on the nature of the data. In this post we will learn about various tools used for data analysis.

Data Analysis is at the core of businesses, academia and public sector organisations. It enables decision-making, build strategies, inform operational management, identify trends and define strategies. Businesses are data-driven with data being the new currency powering the business world. Businesses build strategies based on predictions about future events informed by data analysis. Academic research is similarly riddled with data that needs to be analyzed to answer specific questions. The public sector relies on data analysis to inform policy decisions. 

A majority of both big and small businesses collect a substantial amount of data. This data can prove to be highly valuable in improving their operations and efficiency. For instance, a business owner who is able to gain a more in-depth insight into the buying habits of his/her target market will be able to deliver better services. In addition, these insights can help give an indication into when that business owner should lower his/her prices or increase the use of marketing campaigns that have been effective in the past.

Google Data Studio

Google Data Studio is one of the popular free Data analytics tools for dashboarding and data visualization that automatically integrates with most other Google applications, such as Google Analytics, Google Ads, and Google BigQuery. Data Studio is perfect for those who need to evaluate their Google data due to its convergence with other Google services. For example, marketers could create dashboards to help analyze consumer conversion and retention for their Google Advertising and Analytics results. Data Studio can run with Data from several other sources as long as the Data is replicated first to BigQuery using a Data pipeline such as Stitch.

Grafana

Grafana is another free, open-source Data analytics software for monitoring and observing metrics across diverse databases and applications. It offers a real-time view into external processes and warns users when such incidents occur. Grafana is widely used for tracking their applications by tech and DevOps engineers.

Redash

Redash is a lightweight and cost-effective Data analytics software for querying data sources and building visualizations. The code is open source, and for organizations that want to begin quickly, an inexpensive host version is available. Redash’s heart is a query editor, which offers a quick interface for requests, schemes and integration management. Search results are cached in Redash, and users can automatically schedule updates.

Jupyter Notebook

Jupyter Notebook is one of the robust free, open-source online Data analytics tools that can be administered in a browser after installation using the Anaconda platform or Python’s package manager, pip. It enables developers to generate reports with Live Code Data and views. This Data analytics software supports more than 40 programming languages. Formerly known as IPython Notebook, Jupyter Notebook was initially developed using Python. It enables developers to make use of Python’s wide variety of analytics and visualization packages. The tool has a large group of users who also use other languages.

QlikView

QlikView is a Self-Service Business Intelligence, Data Visualization, and Data Analytics tool. Being named a leader in Gartner Magic Quadrant 2020 for Analytics and BI platforms, it aims to accelerate business value through data by providing features such as Data Integration, Data Literacy, and Data Analytics.

Products:

QlikView comes with a variety of products and services for Data Integration, Data Analytics, and Developer platforms, out of which few are available for a free trial period of 30 days.

Companies using:

Trusted by more than 50,000 customers worldwide few of the top customers of QlikView are CISCO, NHS, KitchenAid, SAMSUNG.

Recent Advancements/ Features:

Recently QlikView has launched an intelligent alerting platform Qlik Alerting for Qlik Sense® which helps the organizations handle the exceptions, notify users of potential issues, help users analyze further, and also prompt actions based on the derived insights.

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Apache Spark

Apache Spark is one of the most successful projects in the Apache Software Foundation and is a  cluster computing framework that is open-source and is used for real-time processing. Being the most active Apache project at the moment, it comes with a fantastic open-source community and an interface for programming. This interface makes sure of fault tolerance and implicit data parallelism.

Products:

Apache Spark keeps on releasing new releases with new features. You can also choose the various package types for Spark. The recent version is 2.4.5 and 3.0.0 is in preview.

Companies using: 

Companies such as  Oracle, Hortonworks, Verizon, Visa use Apache Spark for real-time computation of data with ease of use and speed.

Recent Advancements/ Features:

• In today’s world Spark runs on Kubernetes, Apache Mesos, standalone, Hadoop, or in the cloud.
• It provides high-level APIs in Java, Scala, Python, and R, and Spark code can be written in any of these four languages.
• Spark’s MLlib –  the Machine Learning component is handy when it comes to Big Data processing. 

IBM Cognos

IBM Cognos is a Data analytics software for business intelligence with built-in AI tools to show and clarify information concealed in plain English. It has automated Data preparation software to automatically clean and aggregate Data sources, enabling the fast integration and analysis of Data sources.

R

Free and open-source programming languages are a great option if you’re building your own data analysis tools. R is widely used for exploratory data analysis, statistical computing, and data visualization. At first, it was mainly used by researchers and academics but has now branched out into the business world. Learning R is relatively easy, even if you don’t have a programming background.

Python

Python is one of the most in-demand programming languages today and it’s considered the preferred language for machine learning. It stands out for being very flexible, allowing you to build solutions for various use cases. Plus, it’s fairly straightforward to learn and write.

SAS

SAS at a glance:

• Type of tool: Statistical software suite.
• Availability: Commercial.
• Mostly used for: Business intelligence, multivariate, and predictive analysis.
• Pros: Easily accessible, business-focused, good user support.
• Cons: High cost, poor graphical representation.

SAS (which stands for Statistical Analysis System) is a popular commercial suite of business intelligence and data analytics tools. It was developed by the SAS Institute in the 1960s and has evolved ever since. Its main use today is for profiling customers, reporting, data mining, and predictive modeling. Created for an enterprise market, the software is generally more robust, versatile, and easier for large organizations to use. This is because they tend to have varying levels of in-house programming expertise. But as a commercial product, it comes with a hefty price tag. Nevertheless, with cost comes benefits; it regularly has new modules added, based on customer demand. Although it has fewer of these than say, Python libraries, they are highly focused. For instance, it offers modules for specific uses such as anti-money laundering and analytics for the Internet of Things.

Microsoft Power BI

Power BI at a glance:

• Type of tool: Business analytics suite.
• Availability: Commercial software (with a free version available).
• Mostly used for: Everything from data visualization to predictive analytics. 
• Pros: Great data connectivity, regular updates, good visualizations.
• Cons: Clunky user interface, rigid formulas, data limits (in the free version).

At less than a decade old, Power BI is a relative newcomer to the market of data analytics tools. It began life as an Excel plug-in but was redeveloped in the early 2010s as a standalone suite of business data analysis tools. Power BI allows users to create interactive visual reports and dashboards, with a minimal learning curve. Its main selling point is its great data connectivity—it operates seamlessly with Excel (as you’d expect, being a Microsoft product) but also text files, SQL servers, and cloud sources, like Google and Facebook analytics. It also offers strong data visualization but has room for improvement in other areas. For example, it has quite a bulky user interface, rigid formulas, and the proprietary language (Data Analytics Expressions, or ‘DAX’) is not that user-friendly. It does offer several subscriptions though, including a free one. This is great if you want to get to grips with the tool, although the free version does have drawbacks—the main limitation being the low data limit (around 2GB).

Conclusion: When analyzing data, there are numerous ways to approach it. Many of the techniques used are the same regardless of the analytical technique being applied. It’s also interesting to note that software used for data analysis is becoming increasingly advanced. Thanks to this increasing complexity, many people don’t know where to begin when it comes to choosing what software they should use. To help with this problem, this post lists some of the most popular open-source data analysis programs on the market.