Big Data Analytics – Definition

What is big data analytics?

Big data analytics is the process of looking at large amounts of data to find helpful information, such as hidden patterns, correlations, market trends, and customer preferences. This information can help businesses make better decisions.

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Data analytics technologies and techniques allow organizations to look at large data sets and learn new things about them. Business intelligence (BI) answers basic questions about how a business works and how well it is doing.

Big data analytics is a type of advanced analytics. These complex applications use analytics systems to run things like predictive models, statistical algorithms, and “what-if” analyses.

Why is big data analytics important?

Organizations can use software and systems to analyze big data to make decisions based on the data, leading to better business results. Some benefits could be better marketing, new ways to make money, better customer service, and more efficient operations. With a good plan, these advantages can give you an edge over your competitors.

How does big data analytics work?

Data analysts, data scientists, predictive modelers, statisticians, and other analytics professionals collect, process, clean, and analyze growing amounts of structured transaction data and different types of data that traditional BI and analytics programs don’t use.

Here’s a quick look at the four steps of the process for analyzing big data:

Collect data

Data professionals collect data from many different sources. It is often a mix of semistructured and unstructured data. Each company will use other data streams, but here are some common ones:

Internet clickstream data;
Web server logs;
Cloud applications;
Mobile applications;
Social media content;
Text from emails and survey responses from customers;
Records from mobile phones
Machine data is picked up by sensors connected to the Internet of Things (IoT).

Data is prepared and processed

After data is collected and stored in a data warehouse or data lake, data professionals must organize, configure, and divide the data for analytical queries. Analytical queries work better when the data are prepared and processed carefully.

Data cleaning

Data is cleaned up to make it better. Data professionals use scripting tools or data quality software to clean up the data. They look for mistakes and inconsistencies, like duplicates or errors in how the data is formatted, and then organize and clean up the data.

Data analysis

Analytics software is used to analyze the data that has been collected, processed, and cleaned. There are tools for:

Data mining involves looking through large data sets for patterns and links.
Predictive analytics builds models to predict how customers will act and what other actions, scenarios, and trends will happen in the future.
machine learning, which uses different algorithms to look at big sets of data,
Deep learning is a branch of machine learning that is more advanced.
Software for analyzing text and numbers
Artificial Intelligence (AI)
Software commonly used for business intelligence
Data visualization tools

Tools and technologies for analyzing large amounts of data

Many tools and technologies are used to help analyze big data. Most big data analytics processes use the following technologies and tools:

Hadoop is a free framework for storing and working with large amounts of data. Hadoop can handle both structured and unstructured data in large amounts.
Predictive analytics hardware and software process large amounts of complex data and use machine learning and statistical algorithms to predict what will happen in the future. Organizations use predictive analytics tools to find fraud, market their products, evaluate risks and run their business.
Stream analytics tools are used to filter, combine, and analyze large amounts of data that may be stored in many different formats or on many other platforms.
Distributed storage data: Data is stored in multiple places and copied, usually on a non-relational database. This can be done to protect against the failure of individual nodes, the loss or corruption of large amounts of data, or to give fast access.
NoSQL databases are non-relational data management systems that help when working with large data sets in different places. They don’t need a fixed schema, making them perfect for raw and unorganized data.
A data lake is a big place to store raw data in its original format until it is needed. A flat architecture is used for data lakes.
A data warehouse is where a lot of data from many different sources is kept. Data warehouses usually store data using schemas that have already been set up.
Knowledge discovery/big data mining tools help businesses mine large amounts of structured and unstructured big data.
In-memory data fabric distributes a lot of data around the system’s memory. This makes it easier to access and process data quickly.
Data virtualization lets you access data without any technical limits.
Data integration software lets platforms like Apache, Hadoop, MongoDB, and Amazon EMR work together to make big data easier to use.
Data quality software is used to clean and add large data sets.
Data preprocessing software is used to get data ready to be analyzed further. Data is set up, and messy data is cleaned up.
Spark is an open-source cluster computing framework that can be used to process both batch and stream data.

Big data analytics applications often use data from both internal systems and external sources, such as weather data or demographic data on customers gathered by third-party information service providers. Streaming analytics applications are also becoming more common in big data environments. Users want to analyze data coming into Hadoop systems in real-time with tools like Spark, Flink, and Storm.

Early big data systems were mainly used on-site, especially in large organizations that collected, organized, and analyzed a lot of data. But Amazon Web Services (AWS), Google, and Microsoft, which offer cloud platforms, have made it easier to set up and manage Hadoop clusters in the cloud. The same is true for companies that sell Hadoop, like Cloudera, which helps distribute the big data framework on AWS, Google, and Microsoft Azure clouds. Users can now set up clusters in the cloud, run them for as long as needed, and then take them offline. This is possible with usage-based pricing that doesn’t require ongoing software licenses.

In supply chain analytics, big data is becoming more and more valuable. Big supply chain analytics uses big data and quantitative methods to help everyone in the supply chain make better decisions. Big supply chain analytics, in particular, increases the size of data sets for more analysis. This goes beyond the traditional internal data on enterprise resource planning (ERP) and supplies chain management (SCM) systems. Also, big supply chain analytics uses both new and old data sources to apply highly effective statistical methods.

The uses and examples of big data analytics

Here are some ways that organizations can benefit from big data analytics:

Customer acquisition and retention. Companies can use customer data to improve their marketing and act on trends to make their customers happier. For example, Amazon, Netflix, and Spotify’s personalization engines can give customers a better experience and make them loyal.
Targeted ads. Personalization data, like what a user has bought in the past, how they interact with others, and what product pages they’ve looked at, can be used to create compelling, targeted ad campaigns for each user and people.
Product development. Big data analytics can give businesses information about the viability of a product, how to decide on its action, how to measure its progress, and how to make improvements that fit what their customers want.
Price optimization. To make the most money, retailers may choose pricing models that use and model data from different data sources.
Supply chain and channel analytics. Predictive analytical models can help restock before they run out, B2B supplier networks, inventory management, route optimization, and letting people know about possible delivery delays.
Risk management. Using data patterns, big data analytics can find new risks that need to be managed so that risk management strategies work well.
Improved decision-making. Business users can help organizations make decisions faster and better when they pull insights from relevant data.

The advantages of big data analytics

Using big data analytics has many benefits, such as:

Rapidly analyzing large amounts of data from various sources in multiple formats and types.
Making better-informed decisions more quickly for successful strategizing can benefit and improve the supply chain, operations, and other strategic decision-making sectors.
Cost can be reduced due to new business process efficiencies and optimizations.
A greater understanding of customer demands, behavior, and sentiment can lead to better marketing insights and product development.
Risk management strategies are better and more accurate because they use much data.

Problems with analyzing big data

Even though there are many benefits to using big data analytics, there are also some problems that come with it:

Accessibility of data. With larger amounts of data, storage and processing become more complicated. Big data should be stored and maintained correctly so that data scientists and analysts with less experience can use it.
Data quality maintenance. Data quality management for big data coming in from different sources and formats requires time, effort, and resources to maintain properly.
Data security. Big data systems are hard to protect because they are so complicated. In a big data ecosystem that is so complicated, it can be hard to deal with security concerns in the right way.
Choosing the right tools. There are a lot of big data analytics tools and platforms on the market, making it hard to choose the right one. Organizations must know how to choose the best tool for their users’ needs and infrastructure.
Some organizations are having trouble filling in the gaps because they might not have enough people with data analytics positions, and hiring experienced data scientists and engineers costs a lot.