Most enterprises nowadays collect large and varied datasets – Big Data – from diverse sources. But no enterprise can leverage big data fully without analyzing the structured and unstructured data accurately and draw conclusions about the data timely. The processing and analysis of big data is essential for making informed business decisions at the right time.
As a form of advanced analytics, big data analytics help businesses to analyze a large amount of data proactively and uncover hidden patterns and correlations immediately. Unlike conventional business intelligence (BI) solutions, big data analytics simplify and accelerate analysis of large volumes of structured and unstructured data using statistical algorithms, predictive models, and what-if analysis.
The businesses even have the option to choose from a wide range of tools for big data analysis. These big data analytics solutions come with built-in features to uncover useful information, facilitate decision making, and suggest conclusions by organizing, cleaning, inspecting, transforming and modeling big data. That is why; it becomes essential for enterprises to understand important aspects of big data analytics.
Understanding Important Aspects of Big Data Analytics
An Array of Technologies
Big data analytics accelerate examination of large datasets and facilitate decision making by combining a number of technologies. In addition to using data mining and data management technologies, big data analytics also leverage in-memory data analytics to examine and analyze data from system memory. Also, it determines future outcomes based on historical data by leveraging predictive analytics technologies using data, machine learning techniques, and statistical algorithms.
At the same time, big data analytics also use text mining techniques to analyze the data collected text-based sources like websites, emails, social media feeds, and books. The text mining technologies further analyze a large amount of text data by leveraging machine learning and natural language processing techniques. Big data analytics even uses open source software applications like Hadoop to simplify storage and processing of a large amount of data.
A Wide Range of Tools
The enterprises have the option to accelerate big data analysis by choosing from a wide range of tools. Some of these big data analytics tools are available as open source technologies, whereas others are licensed and commercial software applications. But each of these big data analytics tools comes with built-in features to accelerate analysis of large and complex datasets and help managers to make informed decisions. These tools even make it easier for enterprises to get insights from structured and unstructured data without investing additional time and resources.
Used by Enterprises from Varied Industries
At present, big data analytics are being used by enterprises from various sectors and regions. Hence, the use cases of big data analytics differ across businesses. The retail businesses leverage big data analytics to understand customer preferences, predict product demand, launch innovative products, and boost customer loyalty. Likewise, enterprises providing healthcare service use custom big data analytics solutions to provide large volumes of patient data quickly and accurately. At the same time, the businesses from travel and hospitality sector take advantage of big data analytics tools to improve customer experience by detecting and resolving potential problems early.
Most enterprises nowadays invest in big data analytics tools to drive the sale, improve customer experience, and beat the competition. Also, big data analytics still help businesses to stay profitable and relevant in the long run by leveraging big data fully. In addition to accelerating analysis of big data in full context, some of these tools also support real-time data analysis. Hence, enterprises leverage big data analytics tools to reap benefits like transforming business processes, generating revenue by developing the right products, reduce overall operational expenses, and improve customer experience.
Current and Emerging Trends
Like other technologies, big data analytics has been evolving consistently. Likewise, the trends in big data analytics tools also keep changing from time to time. For instance, many analysts believe that big data analytics tools can be transformed by emerging trends like cloud-based big data analytics, big data lakes, next-generation NoSQL, deep learning and in-memory data analytics. While comparing big data analytics tools, decision makers must keep in mind the current as well as emerging trends. They also need to upgrade the big data analytics tools regularly to stay relevant and adopt emerging trends.
On the whole, big data analytics refers to the process of analyzing a large amount of structured and unstructured data collected from varied sources to facilitate decision making. Enterprises from varied industries leverage big data tools and solutions for data analysis to boost customer experience and drive sales by taking the right decision at the right time.
Also read: Top 5 Challenges in Big Data & Analytics