Big Data Analytics: What It Is, How It Works
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ach day, your customers generate an abundance of data. Every time they open your email, use your mobile app, tag you on social media, walk into your store, make an online purchase, talk to a customer service representative, or ask a virtual assistant about you, those technologies collect and process that data for your organization. And that’s just your customers. Each day, employees, supply chains, marketing efforts, finance teams, and more generate an abundance of data, too. Big data is an extremely large volume of data and datasets that come in diverse forms and from multiple sources.
Many organizations have recognized the advantages of collecting as much data as possible. But it’s not enough just to collect and store big data—you also have to put it to use.
Thanks to rapidly growing technology, organizations can use big data analytics to transform terabytes of data into actionable insights.
What is big data analytics?
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.
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.
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. 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
Big data analytics refers to collecting, processing, cleaning, and analyzing large datasets to help organizations operationalize their big data.
1. 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.
2. 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.
3. 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.
4. 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.
