Top Data Analytics Tools to Unleash the Potential of Big Data

Top Data Analytics Tools to Unleash the Potential of Big Data
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R Bhargav

Last updated December 1, 2016


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There is a data revolution going on around the world and data analytics is the shiny new thing in the job market that has been alluring professionals. If you thought it’s just hyperbole, check out these statistics:

1. According to International Data Corporation, the big data and analytics market reached $125 billion worldwide in 2015.

2. The US Bureau of Labor Statistics reports that the job market for various data analyst disciplines is growing annually at the rate of 27%,  far exceeding the 11% national job growth average.

3. 70% of large organizations in the US have already purchased external data and 100% will do so by 2019. (Source: Forbes)

4. According to the International Institute for Analytics, by 2020 businesses using data will save $430 billion due to productivity enhancement over their competitors.

5. In the US, 1.5 million data-savvy managers and analysts will be needed in the next 5 years, lead businesses, and drive informed decisions. (Source: Forbes)

So, data analytics is currently the most lucrative path to ride the big data wave. There is a proliferation of data analytics tools in the market, and thus selecting appropriate tools to work with is a challenging choice. Here we are listing 10 most efficient data analytics tool to unleash the big potential of data and drive businesses towards 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, health data, as well as Impala query and YARN application metadata. They also deliver intricate data security, which is highly significant if you’re storing any sensitive or personal data.

2. MongoDB:

This is an efficient data analytics tool responsible for preserving data for mobile apps, product catalogs, real-time personalization, content management, and applications providing a single view across multiple systems. Here’s why MongoDB is exactly the tool to enable data analytics capabilities:

1. It reduces operational overhead up to 95%

2. Its new flexible storage architecture includes WiredTiger storage engine

3. It has a global distribution with up to 50 replica set members

4. It takes up to 80% less storage space due to compression

3. Vidyard:

Vidyard is an adept 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 seamless 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 analytics tools and explicitly addresses text analytics in the milieu of customer oriented and marketing applications. It specializes in visual representation including dashboards for sentiment and text analysis, and also provide exclusive 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 a widely recognized for its potential. Entity, theme, and topic extraction are sustained along with decision and strategy trees for profiling, segmentation, and predictive modelling.

5. Buzzlogix:

Buzzlogix provides cloud based natural language processing and machine learning API’s to support sentiment analysis, data mining, content discovery, business intelligence, and other tasks where natural language processing is leveraged. The various functions are called via a REST API and address the diverse data applications:

1. Sentiment Analysis

2. Twitter Sentiment Analysis

3. Subjectivity Analysis

4. Topic Classification

5. Gender Detection

6. Keyword Extraction

7. Entity Extraction

6. OpenRefine:

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

The following example shows how OpenRefine classifies clusters of information with text analytics and intelligence. (Source: Forbes)

Open Refine

7. Qubole:

Qubole works on simplifying, speeding, and scaling big data analytics workloads against data stored on AWS, Google, or Azure clouds. 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, etc. in the ever transforming list of data processing engines. Qubole is an enterprise level solution and its flexibility sets it apart from the rest as well as it is the most accessible of the platforms.

8. Tableau:

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

Tableau 9.0 also has innovative technology for database connections called Query Fusion that will greatly help simplify queries by looking at all of the queries in the user's dashboard.

9. Chartio:

Chartio lets you to chain data sources and execute queries in-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 other complicated model languages. They also let the user schedule PDF reports so that those can 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’re going to get up and running at a faster pace and that the cost of implementation is going to be lower and more predictable.

10. Blockspring:

Blockspring is a distinctive tool in the way that it harnesses all of the capabilities of services such as IFTTT (‘If This Then That’) and Zapier in popular platforms such as Excel and Google Sheets. You can connect to a wide array of 3rd party programs by merely writing a Google Sheet formula. You can post Tweets from a spreadsheet, track your followers, as well as connect to AWS, Import.io, Tableau, and more. Blockspring lets you to create and share private functions, implement custom tags for enhanced search and discovery, and set API tokens for your whole organization at once.

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About the Author

An experienced process analyst at Simplilearn, the author specializes in adapting current quality management best practices to the needs of fast-paced digital businesses. An MS in MechEng with over eight years of professional experience in various domains, Bhargav was previously associated with Paradox Interactive, The Creative Assembly, and Mott MacDonald LLC.


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