Big Data Analytics
Writer : Michael Aurora EG
What is big data analytics, and how does it differ from traditional data analytics?
It is often difficult to find patterns and correlations in massive amounts of data in order to derive actionable insights, such as market trends or customer preferences, through the use of big data analytics.
Data analytics technologies and techniques allow organizations to analyze large data sets and gain new insights. BI queries are designed to answer the most fundamental questions about a company's operations and financial performance.
Complex applications with elements like predictive models, statistical algorithms and what-if analysis powered by analytics systems are part of big data analytics.
What is the significance of big data analytics?
Big data analytics systems and software can be used by businesses to make data-driven decisions that can lead to better business results. New revenue opportunities, customer personalization and improved operational efficiency are just some of the possible benefits of implementing this technology. These advantages can be used to gain an advantage over competitors with a well-executed strategy.
In what ways does big data analytics work?
It's becoming more and more common for analytics professionals to work with structured transaction data as well as other types of data that aren't typically used in BI and analytics programs.
Analyzing big data involves four distinct steps, as follows:
- Data is gathered from a wide range of sources by data professionals. Semi-structured and unstructured data are frequently found together. The following are some common data sources that may be used by different organizations:
- internet clickstream data;
- web server logs;
- cloud applications;
- mobile applications;
- social media content;
- email responses from customers and survey results
- mobile phone records; and
- sensors that are connected to the internet of things collect machine data (IoT).
- Assembling and analyzing information is a given. Organizing, configuring, and partitioning the collected data for use in analytical queries is a necessary step after it has been stored in a data warehouse or lake by data professionals. Analytical queries perform better when the data is thoroughly prepared and processed.
- Efforts are made to improve the data's quality by cleansing it. Scripting tools and data quality software are commonly used by data professionals to clean up the data. Duplicates and formatting mistakes are spotted and corrected, while the data is organized and cleaned up.
- Analytical software is used to assess the collected, processed, and cleaned data. Among the tools included are those for:
- The process of extracting patterns and relationships from large amounts of data
- modeling to predict future customer behavior and other scenarios and trends through the use of predictive analytics
- using various algorithms to analyze large data sets through machine learning
- Text mining and statistical analysis software use deep learning, a more advanced branch of machine learning.
- Mainstream business intelligence software and data visualization tools using artificial intelligence (AI)
Technologies and tools for big data analytics
Big data analytics is supported by a wide range of tools and technologies. Big data analytics processes are supported by a wide range of technologies and tools, including:
- For big data storage and processing, Hadoop is an open source framework. As a result, Hadoop has the ability to deal with both structured and unstructured data.
- In order to make predictions about future events, predictive analytics hardware and software processes large amounts of complex data using machine learning and statistical algorithms. Predictive analytics tools are used in fraud detection, marketing, risk assessment, and operations by organizations.
- In order to analyze large amounts of data, stream analytics tools are used to filter, aggregate, and analyze data from a variety of sources.
- Non-relational databases are commonly used for replicating distributed storage data. There are many reasons for this, including the protection against node failures and corrupted data, as well as the provision of low-latency access.
- Non-relational data management systems, such as NoSQL databases, are helpful when dealing with large distributed data sets. As a result, they are ideal for dealing with raw and unstructured data.
- In the absence of immediate use, a data lake serves as a large storage facility for raw, native-format data. Flat architecture is used in data lakes.
- Repositories that hold large amounts of data from various sources are known as "data warehouses." schema-based data warehouses typically store data.
- Companies are able to mine massive amounts of structured and unstructured big data with the help of knowledge discovery and big data mining tools.
- Large amounts of data can be distributed across system memory resources using an in-memory data fabric. This reduces the amount of time it takes to access and process data.
- It is possible to access data in a non-technical manner thanks to data virtualization.
- Big data can be streamlined across Apache, Hadoop, MongoDB, and Amazon EMR using data integration software.
- Cleansing and enriching large data sets with data quality software.
- Preparation software for further analysis of data. Unstructured data is cleaned and formatted.
- Spark, an open-source framework for batch and stream data processing, is a cluster computing platform.
It is common for big data analytics applications to incorporate data from both internal systems and external sources, such as weather data or demographic data on customers compiled by third-party information service providers. Data stream processing engines like Spark, Flink, and Storm are increasingly being used in big data environments to perform real-time analytics on data that is fed into Hadoop systems.
Before cloud computing was widely adopted, most big data systems were installed on-premises, especially in large organizations that had to deal with a large volume of data. Amazon Web Services (AWS), Google, and Microsoft have all made it easier to set up and manage Hadoop clusters in the cloud. There are no exceptions when it comes to Hadoop providers such as Cloudera, which supports distribution of the big data framework on the AWS, Google, and Microsoft Azure cloud platforms alike. For the first time, users can now spin up clusters in the cloud, run them for as long as they need, and then take them offline with usage-based pricing that doesn't require ongoing software licenses.
The use of big data in supply chain analysis has grown in recent years. Big data and quantitative methods are used in big supply chain analytics to improve decision-making processes throughout the supply chain. Large-scale supply chain analytics, in particular, enlarges data sets for more comprehensive analysis beyond the traditional internal data found in ERP and SCM systems. In addition, big supply chain analytics employs statistical methods that are highly effective on both new and old data sources.
Big data analytics uses and examples
Here are a few examples of how big data analytics can benefit organizations:
- Acquisition and retention of customers. The marketing efforts of companies can benefit from consumer data, which can be used to act on trends to improve customer satisfaction. Personalization engines, such as those used by Amazon, Netflix, and Spotify, can increase customer satisfaction and loyalty.
- Sponsored content. Using information about a user's past purchases, interactions, and product page views, as well as other types of personalization data, companies can create highly effective targeted ad campaigns.
- the process of creating a new product. Product viability, development decisions, progress measurement and improvement direction can all be influenced by the use of big data analytics.
- Optimization of the price. In order to maximize profits, retailers may use pricing models that incorporate data from various sources.
- Analytics for the supply chain and distribution channels. Supply chain management, inventory management, route optimization, and the notification of delivery delays can all benefit from predictive analytical models.
- Contingency planning For effective risk management strategies, big data analytics can uncover new dangers hidden within existing data patterns.
- A better ability to make smart decisions. Organizations can benefit from the faster and better decisions that can be made using the insights that business users derive from relevant data.
Big data analytics has a positive impact on society.
Big data analytics has a number of advantages, including:
- Analysis of large amounts of data from a variety of sources and in a variety of formats and types in a timely manner.
- As a result, the supply chain and operations can be improved and the strategic decision-making process can be accelerated.
- Savings that can be achieved through the development of new, more efficient, and more effective business processes.
- Marketing insights and product development information can be gained by better understanding customer needs, behavior, and sentiment.
- Data-driven risk management strategies that are based on large sample sizes.
Big data analytics challenges
The use of big data analytics comes with a number of challenges, in spite of the numerous advantages it provides:
- Affordability of information. Data storage and processing become more difficult as data volumes increase. Because of this, even novice data scientists and analysts will benefit from properly storing and maintaining big data.
- Quality control of the data. With so much data coming in from so many different places and in so many different formats, it takes a lot of effort and resources to properly manage data quality for big data.
- Protection of sensitive data. Complexity of big data systems presents unique security issues. Addressing security concerns in a big data ecosystem this complex can be a difficult task.
- Determining which tools to use. Organizations need to know how to choose the best big data analytics tool or platform for their users' needs and infrastructure, because there are a lot of options out there.
- Some organizations are having difficulty filling analytics-related positions due to a lack of internal resources and the high cost of bringing on board data scientists and engineers with relevant experience.
Analysis of large amounts of data
A growing amount of data became known as "big data" in the mid-to-late '90s when it was first used to describe it. As an analyst at Meta Group Inc. in 2001, Doug Laney helped to expand the definition of "big data" to include more information. The growth was described in this expansion:
- An increasing amount of data is being stored and used by organizations; a wide variety of data is being generated; and a high rate of creation and updating.
Big data has come to be known as the "3Vs" because of these three key elements. After Gartner acquired Meta Group and hired Laney in 2005, this concept became more widely known..
During the history of big data, the Hadoop distributed processing framework was introduced. In 2006, Apache released Hadoop as an open source project. Commodity hardware was used to create a clustered platform that could run big data applications. Managing large datasets with the Hadoop framework is common practice.
Big data analytics and related technologies like Hadoop began to take hold in organizations and the public eye in 2011.
Early on in the Hadoop ecosystem's development, large internet and e-commerce companies like Yahoo and Google were among the first to adopt big data applications, along with analytics and marketing service providers.
Many more people are turning to big data analytics to power their digital transformations now that it has gained popularity among a broader audience. It is used by a wide range of businesses, including retailers, financial services companies, insurers, healthcare organizations and manufacturers.
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