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Top 5 Big Data Analytics Tools That is Your Next Key to Success

It’s no surprise that more and more companies across various industries are recognizing the importance of big data analytics. In fact, according to 2017 Big Data Analytics Market Study by Dresner Advisory Services, 53% of companies today are using big data analytics.

Howard Dresner, Dresner Advisory Services’ founder and chief research officer, says that there has been an “uptake in usage and a large drop of those with no plans to adopt” among companies. Organizations are looking into making smarter business decisions, better customer service, higher profits, and efficient operations. This is where big data analytics comes in.

Although several organizations are making use of big data analytics, Dresner says that the Information Technology (IT) sector has emerged as its top adopter. Other industries, such as financial services, health care, telecommunications, and retail organizations are also considering its use.

 

Top 5 Big Data Analytics Tools

The 2017 Big Data Analytics Market Study pointed out the top five technologies under big data analytics that are highly beneficial to business intelligence. This includes the following:

 

Reporting

One of the most used strategies by businesses is data reporting. This involves the process of organizing data into more understandable summaries to monitor how different areas of business are performing. The core metrics are measured, and the data is interpreted into insightful charts and graphs. It is then presented in dashboards, slide decks, or emails.

 

Data Dashboards

Another tool in big data analytics that companies are widely adopting is the data dashboard. Dashboards are information management tools that visually track and display metrics to monitor the performance of an organization. What makes data dashboards so beneficial to companies is that it consolidates data from various sources and displays it in readable, intuitive forms (graphs, tables, charts).

 

Advanced Data Visualization

Data visualization is used to understand the significance of data generated by companies. Dr. Angela Hausman says that great data insights are useless if the people controlling it don’t understand it. Advanced data visualization (ADV) examines data to gain deeper insights, make predictions, and give recommendations. Additionally, ADV uses tools such as animation, interactive visualization, autofocus, and multiple dimension views to display data better.

 

End-user Self-service Portals

Businesses are all about consumers. This is why companies are rapidly adopting end-user self-service portals. This is a website that has self-service functions. These allow consumers to find information, book different services, and resolve issues. Self-service portals can include functions like downloading software, resetting passwords, and reporting issues.

 

Data Warehousing

Big companies need a repository for all the data that they handle, and this is why they adopted data warehousing. Data warehousing involves the collection, storage, and management of data from various sources. They store the database in mainframe servers. However, they are using cloud storage solutions today.

Top 5 Big Data Analytics Tools
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Industries Adopting Big Data Analytics

According to the aforementioned market study by Dresner Advisory Services, several industries are presently adopting big data analytics.

Among these industries, the Telecommunications industry is seen as the most active one, followed by Financial Services, Healthcare, and Technology. The Education sector has the lowest adoption, with the majority of the results indicating that they “might use big data in the future.”

Dresner Advisory Services reports that there are 55% of big data users that are from North America. EMEA has 53% of big data users, while the Asia-Pacific has 44%.

Industries Adopting Big Data Analytics
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Top 5 Big Data Analytics Use Cases

The following are the top big data analytics use cases by the industries mentioned above.

 

Data Warehouse Optimization

70% of the respondents said that data warehouse optimization is considered extremely important to them. As the volume of data increases and new types of data are emerging, optimizing data warehouses can increase its performance and improve its scalability.

 

Customer/Social analysis

Technology-based industries use customer/social analysis more than others. Analytics can be sourced through website data, sales data, surveys, and focus groups. However, social media serves as a source of customer insights.

 

Predictive Maintenance

Companies under the financial services industry are the top users of predictive maintenance. This entails techniques that can predict whatever failures a machine might have in the future. This is especially important for banking institutions. The reason is, they have to minimize equipment downtime as much as possible.

 

Clickstream Analysis

Financial services are also big users of clickstream analysis. This process involves the analysis of data about the web pages that visitors often visit. Indeed, clickstream analysis is extremely useful in marketing and employee productivity.

 

Fraud Detection

In terms of identifying trends, patterns, and anomalies within the data to detect fraudulent activities, the Telecommunications and Financial services industries are its top users. Indeed, several frauds are damaging both of these industries every year.

Top 5 Big Data Analytics Use Cases
Photo by Sandeep Raut on Simplified Analytics

 

Top 5 Big Data Analytics Access Methods

All of the respondents have varying preferences when it comes to accessing their big data analytics. However, five of these stood out at the top. Majority of these respondents consider Spark SQL as critical in their business.

Hive/Hive HQL and HDFS come head to head as the next preferred methods. Amazon S3 comes in fourth, while HBase ranks as fifth.

Meanwhile, machine learning remains as a key technology by receiving more industry support and investment plans. Spark Machine Learning Library (MLib) is projected to dominate machine learning in the coming months.

 

The Future Of Big Data Analytics

As data volume increases and data analysis techniques evolve, it’s evident that the use of big data analytics will become more widespread across industries. In addition to this, the ability to harness data and use it to stay agile will prove to be extremely valuable to companies.

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