1. Big data analytics describes the process of uncovering trends, patterns, and correlations in large amounts of raw data to help make data-informed decisions. These processes use familiar statistical analysis techniques—like clustering and regression—and apply them to more extensive datasets with the help of newer tools.
2. Big data has been a buzz word since the early 2000s, when software and hardware capabilities made it possible for organizations to handle large amounts of unstructured data. Since then, new technologies—from Amazon to smartphones—have contributed even more to the substantial amounts of data available to organizations.
3. With the explosion of data, early innovation projects like Hadoop, Spark, and NoSQL databases were created for the storage and processing of big data. This field continues to evolve as data engineers look for ways to integrate the vast amounts of complex information created by sensors, networks, transactions, smart devices, web usage, and more.
4. Even now, big data analytics methods are being used with emerging technologies, like machine learning, to discover and scale more complex insights.
HOW BIG DATA ANALYTICS WORKS
1. Big data analytics refers to collecting, processing, cleaning, and analyzing large datasets to help organizations operationalize their big data.
2. Collect Data - Data collection looks different for every organization. With today’s technology, organizations can gather both structured and unstructured data from a variety of sources — from cloud storage to mobile applications to in-store IoT sensors and beyond. Some data will be stored in data warehouses where business intelligence tools and solutions can access it easily. Raw or unstructured data that is too diverse or complex for a warehouse may be assigned metadata and stored in a data lake.
3. Process Data - Once data is collected and stored, it must be organized properly to get accurate results on analytical queries, especially when it’s large and unstructured. Available data is growing exponentially, making data processing a challenge for organizations. One processing option is batch processing, which looks at large data blocks over time. Batch processing is useful when there is a longer turnaround time between collecting and analyzing data. Stream processing looks at small batches of data at once, shortening the delay time between collection and analysis for quicker decision-making. Stream processing is more complex and often more expensive.
4. Clean Data - Data big or small requires scrubbing to improve data quality and get stronger results; all data must be formatted correctly, and any duplicative or irrelevant data must be eliminated or accounted for. Dirty data can obscure and mislead, creating flawed insights.
5. Analyze Data - Getting big data into a usable state takes time. Once it’s ready, advanced analytics processes can turn big data into big insights. Some of these big data analysis methods include:
- Data mining sorts through large datasets to identify patterns and relationships by identifying anomalies and creating data clusters.
- Predictive analytics uses an organization’s historical data to make predictions about the future, identifying upcoming risks and opportunities.
- Deep learning imitates human learning patterns by using artificial intelligence and machine learning to layer algorithms and find patterns in the most complex and abstract data.
BIG DATA ANALYTICS TOOLS AND TECHNOLOGY
1. Big data analytics cannot be narrowed down to a single tool or technology. Instead, several types of tools work together to help you collect, process, cleanse, and analyze big data. Some of the major players in big data ecosystems are listed below.
2. Hadoop is an open-source framework that efficiently stores and processes big datasets on clusters of commodity hardware. This framework is free and can handle large amounts of structured and unstructured data, making it a valuable mainstay for any big data operation.
3. NoSQL databases are non-relational data management systems that do not require a fixed scheme, making them a great option for big, raw, unstructured data. NoSQL stands for “not only SQL,” and these databases can handle a variety of data models.
4. MapReduce is an essential component to the Hadoop framework serving two functions. The first is mapping, which filters data to various nodes within the cluster. The second is reducing, which organizes and reduces the results from each node to answer a query.
5. YARN stands for “Yet Another Resource Negotiator.” It is another component of second-generation Hadoop. The cluster management technology helps with job scheduling and resource management in the cluster.
6. Spark is an open source cluster computing framework that uses implicit data parallelism and fault tolerance to provide an interface for programming entire clusters. Spark can handle both batch and stream processing for fast computation.
7. Tableau is an end-to-end data analytics platform that allows you to prep, analyze, collaborate, and share your big data insights. Tableau excels in self-service visual analysis, allowing people to ask new questions of governed big data and easily share those insights across the organization.
BIG DATA ANALYTICS BENEFITS
1. The ability to analyze more data at a faster rate can provide big benefits to an organization, allowing it to more efficiently use data to answer important questions.
2. Big data analytics is important because it lets organizations use colossal amounts of data in multiple formats from multiple sources to identify opportunities and risks, helping organizations move quickly and improve their bottom lines. Some benefits of big data analytics include:
- Cost savings. Helping organizations identify ways to do business more efficiently
- Product development. Providing a better understanding of customer needs
- Market insights. Tracking purchase behavior and market trends
BIG DATA CHALLENGES
1. Big data brings big benefits, but it also brings big challenges such new privacy and security concerns, accessibility for business users, and choosing the right solutions for your business needs. To capitalize on incoming data, organizations will have to address the following:
2. Making big data accessible. Collecting and processing data becomes more difficult as the amount of data grows. Organizations must make data easy and convenient for data owners of all skill levels to use.
3. Maintaining quality data. With so much data to maintain, organizations are spending more time than ever before scrubbing for duplicates, errors, absences, conflicts, and inconsistencies.
4. Keeping data secure. As the amount of data grows, so do privacy and security concerns. Organizations will need to strive for compliance and put tight data processes in place before they take advantage of big data.
5. Finding the right tools and platforms. New technologies for processing and analyzing big data are developed all the time. Organizations must find the right technology to work within their established ecosystems and address their particular needs. Often, the right solution is also a flexible solution that can accommodate future infrastructure changes.
Source
https://www.tableau.com/learn/articles/big-data-analytics
