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The right .Net reporting tools for big data analytics

Businesses often have a wealth of data that can be put to good use by applying .Net reporting tools to perform big data analytics. People are asking questions regarding big data analytics, they are curious to find out if big data analytics has an impact on the business function and operations, and growth of the business.

In this article, we will focus our attention on exploring what is big data and what are the .net report tools and techniques used for performing big data analytics in a data-driven business model and derive inferences about the importance of big data analytics in today’s business operations and revenue generation.

What is Big data?

Businesses have always had data that was usually managed by a relational database management system like Oracle and used for some basic reporting using the Crystal reports for understanding the growth patterns for products. With time, businesses realized that data can be generated and stored from different functional areas of business like customer data, product data, sales data, and performing analysis of these sets of data can help in predicting business behavior in the growing market using data science techniques. The bulk of data becomes bigger and bigger until it cannot be managed by one single management system or maintained at one single site.

Nowadays, what is referred to as big data are the data sets of big organizations in which data is streaming in from multiple channels and is maintained by many separate database management systems. Big data generally refers to the volume of data, which can be huge sometimes in Zettabytes. The data can be from different departments across the organization collected through different business processes. This data may be unstructured, semi-structured, or structured according to the level it was recorded. 

What is big data analytics?

Big data analytics is needed by businesses to tap into the wealth of data that is stored and managed for the business. This data can help data analysts to understand and identify data patterns by studying the previous years’ data to gain insight into important information about the business processes and operations. The analytics they perform allow top leaders and management to take short-term and long-term decisions based on the understanding of business operations through big data analytics and reach important conclusions about the impact of their decisions on business growth. According to a survey conducted by the IBM’s Institute of Business Value and the University of Oxford in 2013, 71% of the financial service firms had already adopted analytics and big data.

Why is big data analytics important?

Observe a scenario where a business is set up and running with lots of big data but they are not utilizing it for big data analytics. They might have some basic reports to follow the business growth but they cannot answer important questions like which products performing well than the others or identify the demand of a specific product in a specific geographic area. These insights can only be acquired by detailed study of business data through performing big data analytics and identifying the data trends that the business products are following over some time.

The data insights that are available through big data analytics allow management to understand and predict the effect of a business decision and the impact it will have on the company’s revenue. Also, big data analytics can help to identify common mistakes or gaps in the marketing procedures and could give insight about which products to focus on and plan marketing strategies for it. Hence, big data analytics can completely change the way the business is operating and take the business to new heights as compared to its competitors.

.Net report tools and techniques used for big data analytics.

Big data analytics may differ from traditional data analysis techniques as it involves a great volume of data that is assembled from multiple sources. The data is aggregated, refined, and stored in a compatible format that allows data analysts to perform detailed studies and analytics on the data to generate several visualizations and charts, and reports to gain insight into the data trends.

Here are some of the tools and techniques that may be a part of the big data analytic software that a business employs to study its data. .Net report designer and analytics software like Tableau, Izenda, and dotnet report builder may incorporate some of the below techniques for performing big data analytics for enterprise-level businesses.

Machine learning

Machine learning is a subset of data science that usually implements a set of algorithms on the data to identify data patterns and collect meaningful insights. This collected information is used to predict future trends in the data. Using a machine learning technique is used by asp report generator to observe and collate data would equip data analysts with understanding to make future decisions about business processes and products. This can help them improve their services.  

Data mining

Data mining is a big data analytics technique that sifts through the bulk of data to identify trends and shed light on repetitive trends in the data that would otherwise be overlooked in traditional data analysis. This piece of information then helps data analysts using the dot net reporting tools reach conclusions derive meanings about the business processes and move the business in the right direction.

Predictive analysis

Predictive analysis is usually done alongside machine learning to make useful predictions and forecasts about the business products based on meaningful insights. The algorithms of predictive analysis observe and identifies repetitive trends and link them to customers’ interests and choices. This can help predict future interactions that would engage the customer with the business to increase conversions. Many .Net reporting tools and c# developers make use of predictive analysis in the .Net framework for reporting tools.

Natural language processing

Natural language processing is a very popular Artificial intelligence technique that is used for big data analytics to extract meaningful words and conversations from the bulk of big data available for a business. This technique is very popular to manage customer feedback and extract relevant information and classify data based on the text analysis done through natural language processing. 

Observe a scenario where customer feedback and satisfaction index is managed manually and no account is taken to address and process it. This is a wealth of data that is left untapped, which can be turned into meaningful information by applying big data analytics through natural language processing techniques. The information gathered can then be used to improve products or services based on customer feedback. This could greatly impact the business product and give them a chance to better their services and increase revenue and customer retention.

Data contextualization

Data contextualization is another technique that is utilized for big data analytics. Often, big data is coming from multiple sources across the organization and sometimes unstructured, semi-structured, and structured data may be encountered together. Having contextual information for such types of data mix can greatly help reduce the challenges faced to deal with this type of big data. Contextual information adds that extra detail to the crude data that can help to classify data and categorize them.


Big data analytics is the way to go forward but when choosing the reporting tool one has to access the features. The best way is to find out if the .net reporting tool they are using has incorporated these modern useful techniques in their data analytics and reporting application.

.Net and c# developers need to exhaust a list of reporting solutions to choose the .net report tool best suited to their needs. 

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