Top Data Analytics Tools to Unleash the Potential of Big Data

The Potential of Big Data

There is a data revolution going on around the world, and data analytics is the shiny new field that has been drawing professionals, leading them to learn about the top data analytics tools and more. If you think the hype about data analytics and Big Data is overstated, check out these statistics:

1. According to the International Data Corporation, the big data and analytics market revenues will increase to $203 billion worldwide in 2020, with a compound annual growth rate of 11.7 percent.
2. The U.S. Bureau of Labor Statistics reports that the job market for various data analyst disciplines is growing annually at the rate of 13 percent faster than most other occupations.
3. Big Data adoption in organizations increased from 17 percent in 2015 to 59 percent in 2018.
4. According to the International Institute for Analytics, businesses using data will save $430 billion compared with competitors in 2020, due to the enhancement of productivity.
5. According to a report by IBM, there will be 2,720,000 jobs for data professionals by 2020.

So, data analytics is currently the most lucrative path to ride the Big Data wave and if you want to enter this field, you need to know how to select the right data analysis tools once you’re certified. But data analytics tools have proliferated recently, and selecting the right ones to work with can be challenging.

Here we list the 10 most efficient data analytics tools to unleash the potential of Big Data and drive businesses toward more informed processes.

1. Cloudera

Cloudera is the perfect enterprise solution to help businesses manage their Hadoop ecosystem. The Service Monitor and Host Monitor roles in the Cloudera Management Service stockpile time-series data and health data, as well as Impala query and Yet Another Resource Negotiator (YARN) application metadata. They also deliver intricate data security, which is essential for storing any sensitive or personal data.

2. MongoDB

MongoDB is an efficient data analytics tool responsible for preserving data for mobile apps, product catalogs, real-time personalization, and content management, providing a single view across multiple systems. Here are some of the benefits of MongoDB:

1. It reduces operational overhead by up to 95 percent.
2. Its new flexible storage architecture includes the WiredTiger storage engine.
3. It has a global distribution with up to 50 replica-set members.
4. It takes up to 80 percent less storage space due to compression.

3. Vidyard

Vidyard is a business video hosting and analytics provider. Companies like Ernst & Young have entrusted their video analytics to Vidyard. With a splendid custom video player and real-time analytics, Vidyard is a boon for anyone looking for a custom solution. Some key metrics monitored by Vidyard include views, average attention span, minutes watched and most popular region.

4. KnowledgeREADER

KnowledgeREADER, from Angoss, is a portion of a wide suite of data analytics tools; it explicitly addresses text analytics related to customer-oriented and marketing applications. It specializes in visual representation, including dashboards for sentiment and text analysis, and also provides a map of the results of association mining to show words that tend to occur together.

Many of its cutting-edge features make use of the embedded Lexalytics text analytics engine, which is widely recognized for its potential. Entity, theme, and topic extraction are sustained, along with decision and strategy trees for profiling, segmenting and predictive modeling.

5. Meltwater Social (Sysomos)

Meltwater Social, formerly Sysomos, is a powerful social media and data analytics tool to provide deep insight into enterprise marketing and user analytics. The ability to use social listening, audience insight, and brand engagement is an important part of any marketing professional’s toolkit. However, Meltwater Social takes the idea of a social media management platform to an entirely new level with a robust and user-friendly analytics powerhouse on the backend. Powered by a learning type of artificial intelligence technology, the analytics are drawn from bits of social user data to provide complete insights that translate to more than marketing.

6. OpenRefine

OpenRefine is a software that cleans data to prepare it for analysis. What do we mean by that? Well, OpenRefine lets you cluster into cells any values that might be differentiated by either spelling or capitalization of letters but mean the exact same thing. This tool might appear basic, but it helps fight redundancy. A simple data analysis activity such as clustering customer info into one cell based on geographical location might otherwise be difficult, as each customer might spell or write the locality information a different way. OpenRefine can detect similarities to make clustering easy. It contains a number of clustering algorithms and makes quick work of an otherwise messy problem.

7. Qubole

Qubole simplifies, speeds up and scales big data analytics workloads against data stored in the cloud on AWS, Google or Azure. This tool takes the stress out of infrastructure wrangling. Once the IT policies are in place, any number of data analysts can collaborate and click to query with the power of Hive, Spark, Presto, and others. Qubole is an enterprise-level data processing engine, and its flexibility and accessibility set it apart from the rest.

8. Tableau

Some of Tableau’s crucial benefits come from its advanced language and storage analytics database. It can help you easily translate data into meaningful business metrics. The online and server versions enable an entire team to build and work collectively with the data visualization tool. Tableau can connect to local or remote data in many different formats. Additionally, the Tableau engine can access live data for up-to-date visualizations or warehoused data for much more smoothly moving visualizations. Tableau Public’s million-row limit provides a thriving platform for personal use, and the free trial is more than enough to explore the tool effectively.

Tableau 10 also has innovative technology for database connections called Query Fusion, which greatly simplifies queries by looking at all of the queries in the user’s dashboard.

9. Chartio

Chartio lets you chain data sources and executes queries in your browser. You can generate potent dashboards in just a few clicks. Chartio’s visual query language lets you collect data from any source without having to know SQL or any other complicated model languages. It also lets you schedule PDF reports to be exported and emailed. The other significant feature about this tool is that in most formats it doesn’t require a data warehouse. This means that you can get up and running at a faster pace and that the cost of implementation is going to be lower and more predictable when compared to other options mentioned above.

10. Blockspring

Blockspring is a distinctive tool due to the way it harnesses all of the capabilities of services such as If This Then That (IFTTT) and Zapier on popular platforms such as Excel and Google Sheets. You can connect to a wide array of third-party programs merely by writing a Google Sheet formula. You can post Tweets from a spreadsheet, track your followers and connect to AWS, Import.io, Tableau and more. Blockspring lets you create and share private functions, implement custom tags for enhanced search and discovery, and set API tokens for your whole organization at once.

Kickstart Your Career in Data Analytics

If you’re interested in starting a career in data analytics, there are several steps you can take to get started:

1. Develop your skills: Data analytics requires a strong foundation in statistics, mathematics, and computer programming. Consider taking courses or obtaining certifications in these areas to build your skills.
2. Gain practical experience: Look for opportunities to gain hands-on experience working with data, such as internships, freelance projects, or personal projects.
3. Build a portfolio: As you gain experience, build a portfolio of your work to showcase your skills and expertise to potential employers.
4. Network with professionals: Attend industry events, join online communities, and connect with professionals in the field to learn more about the industry and potential job opportunities.
5. Apply for jobs: Once you feel confident in your skills and experience, start applying for entry-level positions in data analytics. Be sure to tailor your resume and cover letter to each job application and highlight your relevant skills and experience.

Remember, building a career in data analytics requires dedication, hard work, and continuous learning. Stay up-to-date with the latest trends and technologies in the field and be prepared to adapt to new challenges and opportunities as they arise.

Conclusion

There are a variety of powerful data analytics tools available today that can help businesses unlock the potential of big data. From open-source platforms like Apache Hadoop and Spark to commercial solutions like Tableau and SAS, there are options to suit every budget and use case. Some tools excel at data preparation and cleaning, while others are designed for advanced analytics and machine learning. When choosing a data analytics tool, it’s important to consider factors such as scalability, ease of use, and compatibility with your existing infrastructure. By selecting the right tool and investing in the necessary skills and resources, organizations can gain valuable insights from their data and make data-driven decisions that drive business success.