Big data is a well-known term in the business industry for more than a decade now and big data analytics have also been around for quite
some time. All businesses want to utilize big data in a positive way and
perform big data analytics to help data analysts get the most out of the data wealth of the company. Management can make informed decisions with more confidence on the basis of big data analytics.
Big data analytics can be applied for all and any business to boost their revenue and conversions and identify common mistakes or faulty practices that might be affecting their growth in a negative way as compared to their competitors. The data insights from big data analytics can help businesses overcome this gap and make structural and strategic changes to their business operations and procedures for a positive impact and business growth.
“Without big data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway.” – Geoffrey Moore, Author and Consultant.
Big data is a term used to refer to business data that is spread across the organization and is streaming in from multiple channels in a business. These data may be coming from different data sources and are managed by different database systems set up across the business organization. Data is often collated and stored in a compatible format before big data analytics could be performed on the business data.
Big data analytics help businesses aggregate, manage, and process their data and use it to answer important questions and identify opportunities. It can help them to manage their operations more efficiently and lead to more productivity, higher performance, greater profits, and better customer management and relations.
Big data applications are being devised for government agencies to manage their large databases that hold sensitive data for different types of government services like identification documents, citizens’ data, and data from different government agencies. All this data is interlinked and comes from completely different sections or offices of government organizations.
Healthcare is another area of application that encompasses big bulks of data and information coming from different sources. Areas like patients and hospital data, medicine and pharmaceutical data, medical research and scientific data, and health insurance data all come under this umbrella.
Education has expanded a lot over time and falls in the application of big data. Information about students, parents, teachers, educationalists can be a focus of education-related big data. Also included in this area are the SEN, EAL, and gifted and talented children data that is dealt with separately in a school environment.
Media and entertainment are a big industry that is a main application of big data analytics. Scores of social media platforms and music and video websites are available that are generating a huge amount of data that
is completely out of the scope of traditional database management systems and data reporting and analysis tools.
Banking is another enormous industry that utilizes big data analytics to manage its large wealth of big data generated by different banking
sectors and expert areas. The online banking systems that offer online services contribute their share to the big data in the banking industry. New banking services and products are coming into the market every day and they bring with them their own fair share of big data and the volume keeps increasing. This big data is managed and studied using big data analytic tools and techniques.
Gaining insight into the types of industries involved in the big data scenario and the bulk of data that is being produced by these industries give us an idea about how important it is to convert this bulk of big data to meaningful insights and actionable visualizations that can provide data analysts means to understand and coerce the data trends and use them to impact the business positively.
Some advantages of big data analytics are cost control and able to increase productivity and performance by analyzing the business data and reaching conclusions about key performance metrics. Big data analytics also allows data analysts to convince management into making informed decisions quicker and with more confidence. It also has a positive impact on customer relationships and helps to retain customers by acting and improving services based on customer feedback.
In this section, we will discuss some tools and techniques that form the basis of big data analytics and helps data analysts perform their tasks quickly and with ease. These big data analytics tools can help aggregate, assemble, and organize data before applying algorithms to understand the data trends.
Machine learning is a set of algorithms designed to run on big data and identify the data patterns for performing analytics. Machine learning is a subset of data science, which lets data analysts study big data and forecast data metric that helps the management take short-term and long-term decisions regarding their operations and services.
Predictive analysis of big data allows business analysts to perform detailed data analytics for the big data of the business to predict future outcomes based on historical data trends. These predictions are very useful for customer engagements and suggesting customized adverts according to their interests. A range of predictions can be made for different business products and services and action plans can be developed to improve services that may in turn increase revenue.
Data mining tools implement algorithms to identify data patterns and create visualizations that help data analysts to answer business-related questions and make predictions about business performance. Data mining can be useful when a business is dealing with bulks of data that need refinement and organization for detailed study.
Data querying is another tool to apply big data analytics for converting data into information. Data analysts perform analytics to answer queries that target increasing performance metrics and specific questions related to business data. Data querying is important when management wants answers to specific questions and data analysts study data to specifically answer these performance-related questions.
The importance of data visualization can never be negated in big data analytics as visuals are a powerful tool to understand data and derive
information and statistics that can lead to changes in course of action and
business procedures to increase productivity and improve services. Data
reporting and analytics softwares can help business analysts achieve this goal by offering features to create customized visualizations and data reports.
Lots of big data analytics tools and softwares are making their way into the software market every day. They are packed with features and options to perform big data analytics. BigML and DataRobot are more inclined
towards using artificial intelligence and machine learning techniques for big data analytics. Many business intelligence softwares and data reporting tools like Tableau, Sisense, and dotnet report builder provide enterprise-level businesses with features to perform big data analytics through data visualization, data reporting, and analytics.
Before a business makes a decision to purchase a software solution for big data analytics, the data analysts’ must perform a requirement
analysis for their business to identify the data analytics needs for their
business. Then, they can search the market for big data analytic solution that best matches their needs and blends well with their existing business workflow.