Big Data Analytics

 

Big data analytics is a complex process of examining big data to uncover information, such as - hidden patterns, correlations, market trends and customer preferences. This can help organizations make informed business decisions. Data Analytics technologies and techniques give organizations a way to analyze data sets and gather new information. Big Data Analytics enables enterprises to analyze their data in full context quickly and some also offer real-time analysis.

Importance of Big Data Analytics:

Organizations use big data analytics systems and software to make data-driven decisions that can improve business-related outcomes. The benefits include more effective marketing, new revenue opportunities, customer personalization and improved operational efficiency. With an effective strategy, these benefits can provide competitive advantages over rivals.

Big Data Analytics tools also help businesses save time and money and aid in gaining insights to inform data-driven decisions. Big Data Analytics enables enterprises to narrow their Big Data to the most relevant information and analyze it to inform critical business decisions

 

Challenges of conventional systems

• Big data is the storage and analysis of large data sets.
• These are complex data sets that can be both structured or unstructured.
• They are so large that it is not possible to work on them with traditional analytical tools.
• One of the major challenges of conventional systems was the uncertainty of the Data Management Landscape.
• Big data is continuously expanding, there are new companies and technologies that are being developed every day.
• A big challenge for companies is to find out which technology works bests for them without the introduction of new risks and problems.
• These days, organizations are realising the value they get out of big data analytics and hence they are deploying big data tools and processes to bring more efficiency in their work environment.

 

Intelligent data analysis, nature of data

Intelligent Data Analysis (IDA) is one of the most important approaches in the field of data mining. Based on the basic principles of IDA and the features of datasets that IDA handles, the development of IDA is briefly summarized from three aspects:

• Algorithm principle
• The scale
• Type of the dataset

 

Intelligent Data Analysis (IDA) is one of the major issues in artificial intelligence and information. Intelligent data analysis discloses hidden facts that are not known previously and provide potentially important information or facts from large quantities of data. It also helps in making a decision.

Based on machine learning, artificial intelligence, recognition of pattern, and records and visualization technology, IDA helps to obtain useful information, necessary data and interesting models from a lot of data available online in order to make the right choices.

 

IDA includes three stages:

(1) Preparation of data

(2) Data mining

(3) Data validation and Explanation

 

Analytic processes and tools

Big Data Analytics is the process of collecting large chunks of structured/unstructured data, segregating and analyzing it and discovering the patterns and other useful business insights from it.

These days, organizations are realizing the value they get out of big data analytics and hence they are deploying big data tools and processes to bring more efficiency in their work environment. 

Many big data tools and processes are being utilized by companies these days in the processes of discovering insights and supporting decision making.

Big data processing is a set of techniques or programming models to access large- scale data to extract useful information for supporting and providing decisions.

Below is the list of some of the data analytics tools used most in the industry:

• R Programming (Leading Analytics Tool in the industry)
• Python
• Excel
• SAS
• Apache Spark
• Splunk
• RapidMiner
• Tableau Public
• KNime

 

 Analysis vs reporting

Reporting:

• Once data is collected, it will be organized using tools such as graphs and tables.
• The process of organizing this data is called reporting.
• Reporting translates raw data into information.
• Reporting helps companies to monitor their online business and be alerted when data falls outside of expected ranges.
• Good reporting should raise questions about the business from its end users. 

Analysis:

• Analytics is the process of taking the organized data and analyzing it.
• This helps users to gain valuable insights on how businesses can improve their performance.
• Analysis transforms data and information into insights.
• The goal of the analysis is to answer questions by interpreting the data at a deeper level and providing actionable recommendations.

Conclusion:

• Reporting shows us “what is happening”.
• The analysis focuses on explaining “why it is happening”  and “what we can do about it”.

 

Modern data analytic tools

• These days, organizations are realising the value they get out of big data analytics and hence they are deploying big data tools and processes to bring more efficiency to their work environment. 
• Many big data tools and processes are being utilised by companies these days in the processes of discovering insights and supporting decision making.
• Data Analytics tools are types of application software that retrieve data from one or more systems and combine it in a repository, such as a data warehouse, to be reviewed and analysed.
• Most organizations use more than one analytics tool including spreadsheets with statistical functions, statistical software packages, data mining tools, and predictive modelling tools.
• Together, these Data Analytics Tools give the organization a complete overview of the company to provide key insights and understanding of the market/business so smarter decisions may be made.
• Data analytics tools not only report the results of the data but also explain why the results occurred to help identify weaknesses, fix potential problem areas, alert decision-makers to unforeseen events and even forecast future results based on decisions the company might make.

Below is the list some of data analytics tools :

• R Programming (Leading Analytics Tool in the industry)
• Python
• Excel
• SAS
• Apache Spark
• Splunk
• RapidMiner
• Tableau Public
• KNime