Data Engineering

Top 10 Data Analytics Tools of 2023 – Best for Data Management

5 minutes read

Modified on July 29, 2022

eCommerce

Data analysis has become the main focus of all businesses. Determining what data analytics tool is to be used is challenging. Let us review the most popular data analytics tools on the market.

You need to consider a few things before choosing the right data analysis tool, which will be most suitable for your business. It would be best if you first understood the types of data your enterprise wants to analyze. Before you start analyzing data, you need to select data sources and replicate them to a data warehouse to create a unified database for analytics. You need to assess data security and data governance to protect sensitive data, especially when data is shared between departments.

How to Choose the Right Data Analytics Tool

After data consolidation, begin analyzing it using different data analytics tools. But how will you select the perfect fit for your organization Consider the following major factors before choosing a data analytics tool:

Start by recognizing your business needs and the users of the tool. Data analytics tools can be used by sophisticated data analysts, data scientists, or non-technical users. Few data analytics platforms provide an interactive experience for iterating on code development while others offer intuitive point-and-click analysis for non-technical users. The tool should also offer support visualizations appropriate for your business.

Data modeling capabilities of the platform also matter. Some perform data modeling directly while others support a semantic layer.

Take into account the price and licensing. There are free tools. There are tools that require licensing or subscription charges. You should not overlook free tools as the pricey ones might not be robust or feature-complete.

Now, let us review some of the popular data analytics tools available in the market.

Top 10 Data Analytics Tools Ideal for Data Analysts

The following Data Analytics tools and platforms are considered to be the most effective and popular among Data Analysts for perfect data management.

Google Data Studio

Google Data Studio is a free data visualization tool that supports Google Analytics, Google Ads, and Google BigQuery. The integration with other Google apps makes Data Studio ideal for those who need to analyze their Google data. Marketers can create dashboards for their Google Ad campaigns and analyze data for better customer conversion and retention. Data Studio can also work with data from other sources, given that the data is replicated to Google BigQuery using a data pipeline.

Microsoft Power BI

Microsoft Power BI is the most popular business intelligence platform supporting multitudes of data sources. It enables users to build and share reports, visualizations, and dashboards. You can also consolidate a group of dashboards or reports into a Power BI app for simple distribution. You can build automated machine learning models in Power BI with the Azure Machine Learning integration.

Tableau

Tableau is an analytics platform that allows data visualization for users across desktop and mobile platforms. You can create and share reports on the cloud or on-premises. Tableau platform runs on VizQL language. The platform offers a drag-and-drop dashboard and visualization features helping end-user in performance optimizations. Tableau does not support advanced SQL queries.

Looker

Looker is a cloud-based data analytics platform. You can build an automatic data model that examines data schemas and understands links between tables and data sources. A built-in code editor is also available, which helps data engineers to alter the generated models.

Chartio

Chartio is an automated business intelligence platform that enables easy import of files and integrates with different data warehouses. It has a unique visual representation of SQL that offers point-and-click construction of queries. Hence, business analysts who are not accustomed to SQL can also modify and analyze easily.

IBM Cognos

IBM Cognos is a business intelligence platform that offers built-in AI tools for better data insights. Cognos also has automated data consolidation tools for cleaning and unifying data sources. It allows for easy integration of various data sources for analysis.

Oracle Analytics Cloud

Oracle offers a suite of analytics and business intelligence tools. Large companies can swap their legacy systems for a modern cloud platform. The wide range of analytics features enables users to create simple visualizations using machine learning algorithms for deeper data insights.

Qlik Sense

Qlik Sense is a visual analytics and business intelligence platform that is built on Qlik’s unique Associative Engine. It offers interactive dashboards, conversational analytics, self-service analytics, mobile analytics, reporting, custom and embedded analytics. Qlik Sense can be deployed both in the cloud and on-premises. It comes in three different editions – Qlik Sense Enterprise, Business, and Team.

Google Analytics

Google Analytics is the most popular web analytics tool that can help you allocate your budget in a more effective way. This allows you to analyze how different investments are affecting your marketing budgets. Since its launch in 2005, Google Analytics has become a premium ROI measurement tool.

MS Excel & Google Sheets

Microsoft Excel and Google Sheets are the most used tools for handling spreadsheets and examining data. Excel and Sheets both support all kinds of standard analytics workflow. These are ideal for simple analysis, but lacks the capability of big data and historical analysis. Companies need cloud-based data analytics tools for extensive analyses and data management.

Use Daton with Your Data Analytics Tool

Data analysis tools and platforms operate best with relevant data consolidated in a data warehouse. Daton is a simple data pipeline that can populate popular data warehouses like Snowflake, Bigquery, and Amazon Redshift for fast and easy analytics using 100+ data sources. The best part is that you can use Daton without the need for any coding experience and it is the cheapest data pipeline available in the market. Sign up for a free trial of Daton today!!

Data analysis helps companies enhance their goods and services to increase customer happiness. Large-scale data collection and organization are done to extract information that may be used to guide important business decisions. Data analytics generally analyzes data and makes predictions to derive helpful information. Data analytics is the statistical analysis and processing of existing datasets. Data analysts may concentrate on creating methods for gathering, analyzing, and organizing data to find insightful solutions for current business issues and use cases. This is done as we decide how to provide the information in the most understandable way possible. This post will teach you more about the widely used data analysis tools.

Data analysis tools are software programs that assist organizations and individuals in processing and analyzing vast amounts of data to draw conclusions, spot trends, and make fact-based choices. To aid users in understanding their data better, these tools employ a variety of methodologies, including statistical analysis, machine learning, and data visualization. Data analysis tools may be connected to various data sources, including databases, warehouses, and lakes. They can be used for various data-related tasks, including cleansing, transformation, and modeling. Several well-liked tools for data analysis are Tableau, Power BI, QlikView, SAS, Python, and R.

Companies across all industries want experts who can gather data, analyze it, derive actionable data-driven insights from it, and then use those insights to help them address pressing business issues. As a result, there are several reasons you could think about pursuing a career in data analysis: a. The need for positions: Data analysts are in high demand, and there are no indicators that this trend will soon slow down. According to US Bureau of Labor Statistics data, there will be a 23% increase in analyst positions between 2021 and 2031. b. The wage: The average salary for data analyst positions in the US is $63,632. c. The satisfaction at work: PayScale research shows that, on average, 3.9 out of 5 stars, data analysts are satisfied with their work.

With the ability to analyze, display, and report on their data, organizations may better manage their data thanks to IBM Cognos Analytics, a business intelligence platform. The following are a few ways that IBM Cognos Analytics may support data management: a. Data modeling: With the help of IBM Cognos Analytics, companies can build data models that depict the connections between various data sources, allowing them to aggregate and analyze data from many sources. b. Report creation: It is made possible by a variety of tools offered by IBM Cognos Analytics, allowing organizations to produce visualizations and reports emphasizing essential insights rapidly.

Big data workloads are processed using Apache Spark, an open-source distributed processing engine. It uses optimized query execution and in-memory caching for quick analytic queries against any quantity of data. Batch processing, interactive queries, real-time analytics, machine learning, and graph processing are among the tasks it offers. It also provides code reuse across these workloads. Development APIs are available in Java, Scala, Python, and R. Businesses across all industries, including FINRA, Yelp, Zillow, DataXu, Urban Institute, and CrowdStrike, use it. Apache Spark, one of the most popular large-scale distributed processing technologies, had 365,000 meetup members in 2017.