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Abhishek Shrivastava
Abhishek Shrivastava

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What is big data analytics?

What is big data analytics?
Big data analytics refers to the methods, tools, and applications used to collect, process, and derive insights from varied, high-volume, high-velocity data sets. These data sets may come from a variety of sources, such as web, mobile, email, social media, and networked smart devices. They often feature data that is generated at a high speed and varied in form, ranging from structured (database tables, Excel sheets) to semi-structured (XML files, webpages) to unstructured (images, audio files).

Traditional forms of data analysis software aren't equipped to support this level of complexity and scale, which is where the systems, tools, and applications designed specifically for big data analysis come into play.

Why is big data analytics important?
Now you know what big data analytics is. But why does it matter? And most importantly, how can the understanding and use of big data assist us?

Data is woven into the everyday fabric of our lives. With the rise of mobile, social media, and smart technologies associated with the Internet of Things (IoT), we now transmit more data than ever before—and at a dizzying speed. Thanks to big data analytics, organizations can now use that information to rapidly improve the way they work, think, and provide value to their customers. With the assistance of tools and applications, big data can help you gain insights, optimize operations, and predict future outcomes.

This ability to derive insights to inform better decision making is why big data is important. It's how a retailer might hone their targeted ad campaigns, or how a wholesaler might resolve bottlenecks in the supply chain. It's also how a health care provider might discover new options for clinical care based on patient data trends. Big data analytics enables a more holistic, data-driven approach to decision-making, in turn promoting growth, efficiency, and innovation.

Now that you know the importance of big data, as well as the importance of data analytics, let's dive into how big data analytics works.

How does big data analytics work?
Analytics solutions glean insights and predict outcomes by analyzing data sets. However, in order for the data to be successfully analyzed, it must first be stored, organized, and cleaned by a series of applications in an integrated, step-by-step preparation process:

Collect. The data, which comes in structured, semi-structured, and unstructured forms, is collected from multiple sources across web, mobile, and the cloud. It is then stored in a repository—a data lake or data warehouse—in preparation to be processed.
Process. During the processing phase, the stored data is verified, sorted, and filtered, which prepares it for further use and improves the performance of queries.
Scrub. After processing, the data is then scrubbed. Conflicts, redundancies, invalid or incomplete fields, and formatting errors within the data set are corrected and cleaned.
Analyze. The data is now ready to be analyzed. Analyzing big data is accomplished through tools and technologies such as data mining, AI, predictive analytics, machine learning, and statistical analysis, which help define and predict patterns and behaviors in the data.

Key big data analytics technologies and tools
Though it is often referred to as a single system or solution, big data analytics is actually composed of many individual technologies and tools working together to store, move, scale, and analyze data. They may vary depending on your infrastructure, but here are some of the most common big data analytics tools you'll find:

Collection and storage
Hadoop. One of the first frameworks to address the requirements of big data analytics, Apache Hadoop is an open-source ecosystem that stores and processes large data sets through a distributed computing environment. Hadoop can scale up or down, depending on your needs, which makes it a highly flexible and cost-efficient framework for managing big data.
NoSQL databases. Unlike traditional databases, which are relational, NoSQL databases do not require that their data types adhere to a fixed schema or structure. This allows them to support all types of data models, which is useful when working with large quantities of semi-structured and raw data. Due to their flexibility, NoSQL databases have also proven to be faster and more scalable than relational databases. Some popular examples of NoSQL include MongoDB, Apache CouchDB, and Azure Cosmos DB.
Data lakes and warehouses. Once data is collected from its sources, it must be stored in a central silo for further processing. A data lake holds raw and unstructured data, which is then ready to be used across applications, while a data warehouse is a system that pulls structured, pre-defined data from a variety of sources and processes that data for operational use. Both options have different functions, but they often work together to make up a well-organized system for data storage.

Data integration software. Data integration tools connect and consolidate data from different platforms into one unified hub, such as a data warehouse, so that users have centralized access to all the information they need for data mining, business intelligence reporting, and operational purposes.
In-memory data processing. While traditional data processing is disk-based, in-memory data processing uses RAM, or memory, to process data. This substantially increases processing and transfer speeds, making it possible for organizations to glean insights in real time. Processing frameworks like Apache Spark perform batch processing and real-time data stream processing in memory.

Data preprocessing and scrubbing tools. To ensure that your data is of the highest quality, data cleansing tools resolve errors, fix syntax mistakes, remove missing values, and scrub duplicates. These tools then standardize and validate your data so that it's ready for analysis.
Data mining. Big data analytics gain insight from data through knowledge discovery processes like data mining, which extracts underlying patterns from large data sets. Through algorithms designed to identify notable relationships between the data, data mining can automatically define current trends in data, both structured and unstructured.
Predictive analytics. Predictive analytics helps build analytic models that predict patterns and behavior. This is accomplished through machine learning and other types of statistical algorithms, which allow you to identify future outcomes, improve operations, and meet the needs of your users.
Real-time analytics. By connecting a series of scalable, end-to-end streaming pipelines, real-time streaming solutions like Azure Data Explorer store, process, and analyze your cross-platform data in real time, allowing you to gain insights instantly.

Big data analytics uses and examples
Today, many major industries use different types of data analysis to make more informed decisions around product strategy, operations, sales, marketing, and customer care. Big data analytics makes it possible for any organization that works with large amounts of data to derive meaningful insights from that data. Here are just a few real-life applications out of many:

Product development. Big data analytics helps organizations define what their customers want by unearthing their needs through large volumes of business analytics data, steering feature development and roadmap strategy.
Personalization. Streaming platforms and online retailers analyze user engagement to create a more personalized experience in the form of recommendations, targeted ads, upsells, and loyalty programs.
Supply chain management. Predictive analytics define and forecast all aspects of the supply chain, including inventory, procurement, delivery, and returns.
Healthcare. Big data analytics can be used to glean key insights from patient data, which helps providers discover new diagnoses and treatment options.
Pricing. Sales and transaction data can be analyzed to create optimized pricing models, which helps companies make pricing decisions that maximize revenue.
Fraud prevention. Financial institutions use data mining and machine learning to mitigate risk by detecting and predicting patterns of fraudulent activity.
Operations. Analyzing financial data helps organizations detect and reduce hidden operational costs, in turn saving money and increasing productivity.
Customer acquisition and retention. Online retailers use order history, search data, online reviews, and other data sources to predict customer behavior, which they may use to build better retention.
Learn more about big data analytics at an enterprise scale

Big data analytics benefits and challenges
As illustrated by its many use cases, big data benefits organizations across a wide set of industries and a diverse range of contexts. However, given the complex nature of its infrastructure, big data also presents some concerns to consider. Here are a few big data challenges to watch out for:

Keeping your data organized and accessible. The biggest challenge associated with big data is figuring out how to manage the sheer volume of information that's coming in so that it's flowing properly throughout your applications. It's essential that you avoid silos, keep your data integrated, and plan your infrastructure around an effective management strategy.
Quality control. Maintaining the accuracy and quality of your data can be difficult and time-consuming, especially when that data is coming in rapidly at a very high volume. Before you perform any analysis, you'll want to make sure your data collection, processing, and cleaning processes are integrated, standardized, and optimized.
Keeping your data secure. With data breaches on the rise, protecting your data is more important than ever. As your analytics system grows, so does the opportunity for security challenges in the form of fake data, leaks, issues with compliance, and software vulnerabilities. Encrypting your data, keeping up with security audits, and doing your due diligence helps mitigate some of these concerns.
Choosing the right tools. The glut of available tools and technologies can be overwhelming to choose from. This is why it's important to educate yourself, stay informed, and if possible, to hire or consult a specialist when needed.
Despite how much work it can take to set up and manage systems efficiently, the advantages of using big data analytics are well worth the effort. For anyone seeking a more informed, data-driven approach to how they run an organization, big data's long-term benefits are invaluable. Here are just a few:

Faster time to insight. With unparalleled speed and efficiency, big data analytics helps organizations turn information into insight at a faster rate. These insights are then used to make informed decisions around product, operations, marketing, and other business initiatives.
Cost efficiency. Massive amounts of data require storage, which can be expensive to maintain. But with the advent of more scalable systems of storage, organizations can now maximize operational efficiency while reducing costs. This means higher profit margins, as well as more productive systems.
User satisfaction. Big data's advanced business intelligence features not only analyze customer trends, but they also forecast behavior through predictive analytics. By learning more about what their users want, organizations can create personalized products that meet their needs.

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