Big Data Analytics is nothing new to any IT professional. The primary aim of Big Data analytics is to offer actionable insights with time-series data. Big Data technology and initiatives are rapidly increasing, and almost every IT company uses Big Data technology to gain rich information that can aid in making strategically important decisions.
Big Data analytics refers to the process of extracting business intelligence from large and complex data sets. It implies that traditional data analysis techniques are insufficient in today’s business. Data mining techniques, machine learning, neural network, artificial intelligence etc., are powerful tools for Big Data analytics. These tools extract actionable information from massive structured data sets and make it easy for business analysts to work on it.
Why Big Data Analytics Matters
Today’s modern business intelligence (BI) strategies are based on substantial structured data sets analyzed with great care to find patterns and trends. Big Data tools are explicitly designed for this very purpose. These tools extract valuable information from massive collections of structured data sets and make it very easy for business analysts to handle the analysis work. Big Data analytics makes it possible to derive new insights and facts from massive amounts of data sets previously ignored by conventional data analysis methods.
Big Data insights tools help extract actionable facts from vast amounts of structured data sets, which had earlier been ignored by traditional data analysis methods. The insights provided by these tools enable analysts to make intelligent decisions based on available information. This, in turn, leads to robust business decisions that meet the specific needs of the organization.
Today’s market scenario has seen a dramatic rise in the usage of big data analytics tools. Most companies today use them for various purposes, ranging from basic research and analysis to more complex projects such as enterprise optimization and customer relationship management. Some companies like Baidu and Yahoo! have also adopted Google’s Big Data tools for managing their large data sets.
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Big data analytics’s main advantage over traditional data analysis techniques is that unstructured data can be accessed easily by machines and can be processed faster. Big Data tools like Hadoop, Spark, and Map make this possible. It also provides unprecedented access to structured data, enabling quick decision making irrespective of the data quality.
However, big data tools are not enough to create intelligent decisions, especially when the data sets are enormous. To get additional information that can improve your decision making, you may need to enlist the help of third-party vendors. Data visualization tools like dashboard solutions and visually rich user experiences provide rich information to the user, allowing better insight into various aspects of big data sets. With new insights available every week, you will make better decisions faster and achieve better business results. To get more details, make sure to check out Data Analytics: What it is and Why it Matters.
If you are using an existing software stack, it may be beneficial to leverage several analytics tools to accelerate your decision-making process. For instance, you can integrate Hadoop’s Big Data Analytics with your application and map Reduce’s capacity planning and usage management to enable you to scale up your analytics even faster. It is important to note that integrating big data analytics tools with a legacy software stack is not a requirement; however, it is definitely recommended to use all available technologies in combination. In addition to leveraging several technologies, it is also essential to maintain your data integrity at all times. The primary goal is to maximize return on investment (ROI), so ensure that your analytics solutions are always providing you with real data, even if you are using various technology stacks.