WHAT IS BIG DATA ANALYTICS?
Big data analytics examines large amounts of data to uncover hidden patterns, correlations and other insights. With today’s technology, it’s possible to analyze data and get answers from it immediately – an effort that’s slower and less efficient with more traditional business intelligence solutions.
HISTORY AND EVOLUTION OF BIG DATA ANALYTICS
The concept of big data has been around for years; most organizations now understand that if they capture all the data that streams into their businesses, they can apply analytics and get significant value from it. But even in the 1950s, decades before anyone uttered the term “big data,” businesses were using basic analytics to uncover insights and trends. The new benefits that big data analytics brings to the table, however, are speed and efficiency. Whereas a few years ago a business would have gathered information, run analytics and unearthed information that could be used for future decisions, today that business can identify insights for immediate decisions. The ability to work faster – and stay agile – gives organizations a competitive edge they didn’t have before.
WHY IS BIG DATA ANALYTICS IMPORTANT?
Big data analytics helps organizations harness their data and use it to identify new opportunities leads to smarter business moves, more efficient operations, higher profits and happier customers. In his report Big Data in Big Companies, IIA Director of Research Tom Davenport interviewed more than 50 businesses to understand how they used big data. He found they got value in the following ways: 1. Cost reduction. Big data technologies such as Hadoop and cloud-based analytics bring significant cost advantages when it comes to storing large amounts of data and plus they can identify more efficient ways of doing business.
2. Faster, better decision making. With the speed of Hadoop and in-memory analytics, combined with the ability to analyze new sources of data, businesses are able to analyze information immediately which makes decisions based on what they’ve learned. Big Data Analytics: A Concept National Conference on Recent Trends in Computer Science and Information Technology 2 | Page (NCRTCSIT-2016) 3. New products and services. With the ability to gauge customer needs and satisfaction through analytics comes the power to give customers what they want. Davenport points out that with big data analytics, more companies are creating new products to meet customers’ needs.
BIG DATA ANALYTICS IN TODAY’S WORLD
Most organizations have big data. And many understand the need to harness that data and extract value from it. These resources cover the latest thinking on the intersection of big data and analytics. High-performance analytics lets user do things you never thought about before because the data volumes were just way too big. For instance, it can get timely insights to make decisions about fleeting opportunities, get precise answers for hardto-solve problems and uncover new growth opportunities and using while using IT resources more effectively
Who is using it?
1.1. Travel and hospitality. Keeping customers happy is key to the travel and hotel industry, but customer satisfaction can be hard to gauge in a timely manner. Resorts and casinos have only a short window of opportunity to turn around a customer experience that’s going south fast. Big data analytics gives these businesses the ability to collect customer data, apply analytics and immediately identify potential problems before it’s too late.
1.2. Health care Big data is a given in the health care industry. Patient records, health plans, insurance information whereas other types of information is difficult to manage – but are full of key insights once analytics are applied. That’s why big data analytics technology is so important to heath care. By analyzing large amounts of information – both structured and unstructured, health care providers provide lifesaving diagnoses or treatment options almost immediately.
1.3. Government Certain government agencies face a big challenge: tighten the budget without compromising quality or productivity. This is particularly troublesome with law enforcement agencies, which are struggling to keep crime rates down with relatively scarce resources. Many agencies use big data analytics; the technology streamlines operations while giving the agency a more holistic view of criminal activity.
1.4. Retail Customer service has evolved in the past several years, as savvier shoppers expect retailers to understand exactly what they need, when they need it. Big data analytics technology helps retailers meet those demands. Armed with endless amounts of data from customer loyalty programs, buying habits and other sources, retailers have an in-depth understanding of their customers, they can also predict trends, recommend new products and boost profitability.
Big data analytics is the process of examining large data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information. The analytical findings can lead to more effective marketing, new revenue opportunities, better customer service, improved operational efficiency, competitive advantages over rival organizations and other business benefits.
Big data analytics examines large amounts of data to uncover hidden patterns, correlations and other insights. With today’s technology, it’s possible to analyze your data and get answers from it almost immediately – an effort that’s slower and less efficient with more traditional business intelligence solutions.
Although the demand for big data analyticsis high, there is currently a shortage of data scientists and other analysts who have experience working with big data in a distributed, open source environment. In the enterprise, vendors have responded to this shortage by creating Hadoop appliances to help companies take advantage of the semi-structured and unstructured data they own.
Big data can be contrasted with small data, another evolving term that's often used to describe data whose volume and format can be easily used for self ervice nalytics.A commonly quoted axiom is that "big data is for machines; small data is for people."
Big data can be contrasted with small data another evolving term that's often used to describe data whose volume and format can be easily used for self service analytics. A commonly quoted axiom is that "big data is for machines; small data is for people."
In some cases,Hadoop clusters and NoSQL systems are being used as landing pads and staging areas for data before it gets loaded into a data warehouse for analysis, often in a summarized form that is more conducive to relational structures.
High-performance analytics lets you do things you never thought about before because the data volumes were just way too big. For instance, you can get timely insights to make decisions about fleeting opportunities, get precise answers for hard-to-solve problems and uncover new growth opportunities – all while using IT resources more effectively.
The concept of big data has been around for years; most organizations now understand that if they capture all the data that streams into their businesses, they can apply analytics and get significant value from it. But even in the 1950s, decades before anyone uttered the term “big data,” businesses were using basic analytics (essentially numbers in a spreadsheet that were manually examined) to uncover insights and trends.
The new benefits that big data analytics brings to the table, however, are speed and efficiency. Whereas a few years ago a business would have gathered information, run analytics and unearthed information that could be used for future decisions, today that business can identify insights for immediate decisions. The ability to work faster – and stay agile – gives organizations a competitive edge they didn’t have before.
Big data analytics is the process of examining large data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information. The analytical findings can lead to more effective marketing, new revenue opportunities, better customer service, improved operational efficiency, competitive advantages over rival organizations and other business benefits.
Big data analytics examines large amounts of data to uncover hidden patterns, correlations and other insights. With today’s technology, it’s possible to analyze your data and get answers from it almost immediately – an effort that’s slower and less efficient with more traditional business intelligence solutions.
Although the demand for big data analyticsis high, there is currently a shortage of data scientists and other analysts who have experience working with big data in a distributed, open source environment. In the enterprise, vendors have responded to this shortage by creating Hadoop appliances to help companies take advantage of the semi-structured and unstructured data they own.
Big data can be contrasted with small data, another evolving term that's often used to describe data whose volume and format can be easily used for self ervice nalytics.A commonly quoted axiom is that "big data is for machines; small data is for people."
Big data can be contrasted with small data another evolving term that's often used to describe data whose volume and format can be easily used for self service analytics. A commonly quoted axiom is that "big data is for machines; small data is for people."
In some cases,Hadoop clusters and NoSQL systems are being used as landing pads and staging areas for data before it gets loaded into a data warehouse for analysis, often in a summarized form that is more conducive to relational structures.
History and evolution of big data analytics
The concept of big data has been around for years; most organizations now understand that if they capture all the data that streams into their businesses, they can apply analytics and get significant value from it. But even in the 1950s, decades before anyone uttered the term “big data,” businesses were using basic analytics (essentially numbers in a spreadsheet that were manually examined) to uncover insights and trends.
The new benefits that big data analytics brings to the table, however, are speed and efficiency. Whereas a few years ago a business would have gathered information, run analytics and unearthed information that could be used for future decisions, today that business can identify insights for immediate decisions. The ability to work faster – and stay agile – gives organizations a competitive edge they didn’t have before.
Why is big data analytics important?
Big data analytics helps organizations harness their data and use it to identify new opportunities. That, in turn, leads to smarter business moves, more efficient operations, higher profits and happier customers. In his report Big Data in Big Companies, IIA Director of Research Tom Davenport interviewed more than 50 businesses to understand how they used big data. He found they got value in the following ways:
Cost reduction. Big data technologies such as Hadoop and cloud-based analytics bring significant cost advantages when it comes to storing large amounts of data – plus they can identify more efficient ways of doing business.
Faster, better decision making. With the speed of Hadoop and in-memory analytics, combined with the ability to analyze new sources of data, businesses are able to analyze information immediately – and make decisions based on what they’ve learned.
New products and services. With the ability to gauge customer needs and satisfaction through analytics comes the power to give customers what they want. Davenport points out that with big data analytics, more companies are creating new products to meet customers’ needs.
Increasingly though, big data vendors are pushing the concept of a Hadoop data lake that serves as the central repository for an organization's incoming streams of raw databases. In such architectures, subsets of the data can then be filtered for analysis in data warehouses and analytical databases, or it can be analyzed directly in Hadoop using batch query tools, stream processing software and SQL on Hadoop technologies that run interactive, ad hoc queries written in SQl.
Big data can be analyzed with the software tools commonly used as part of advanced analytics disciplines such as predictive analytics, datamining, text analytics and statsitical analytics. Mainstream BI software and data visulazition tools can also play a role in the analysis process.
Potential pitfalls that can trip up organizations on big data analytics initiatives include a lack of internal analytics skills and the high cost of hiring experienced analytics professionals. The amount of information that's typically involved, and its variety, can also cause data management headaches, including data analytics and consistency issues.
Big data can be analyzed with the software tools commonly used as part of advanced analytics disciplines such as predictive analytics, datamining, text analytics and statsitical analytics. Mainstream BI software and data visulazition tools can also play a role in the analysis process.
Potential pitfalls that can trip up organizations on big data analytics initiatives include a lack of internal analytics skills and the high cost of hiring experienced analytics professionals. The amount of information that's typically involved, and its variety, can also cause data management headaches, including data analytics and consistency issues.
In addition, integrating Hadoop systems and data warehouses can be a challenge, although various vendors now offer software connectors between Hadoop and relational databases, as well as other data integration tools with big data capabilities
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