Big Data Analysis

Garry Spear
Business team with laptop in the office

Successful companies devote time and money to understanding emerging trends in their industries and analyzing customer needs and characteristics. These analytical findings can lead to many positive business opportunities. "Big data" analysis is one important analytical tool that many businesses are using to take advantage of these opportunities in this information age.

Understanding Analysis and Analytics

Big data analysis is the process of examining the large volumes of data that most sophisticated businesses collect each day from a variety of sources. The primary goal is to assist companies in making more informed business decisions by enabling them to access a large volume of past and real-time transactional data. Large volumes of data provide more valid and consistent data results. This large volume of usable data provides a business with the opportunity to make the best possible business decisions based on these data results.

Companies collect a surprising amount of data from many sources every single day. Once this data has been analyzed, companies then use data analytics to implement improved business strategies. Big data analysis looks backward and mines the data, and big data analytics uses that mined data for business planning and implementation.

Structured Data Sources

Around 20 percent of big data comes from data collected from structured data sources, which have a defined length and are usually stored in a database. Examples of structured data include numbers, dates and word groupings entered on forms, applications and records. Instant access to company websites via the Internet has revolutionized the collection of structured data. As new technology has emerged, structured data is now routinely produced by company databases in very large volumes, and it's often done in real-time.

Unstructured Data Sources

The remainder of big data comes from unstructured data sources such as customer emails and forms, social media activity content, server logs, and other sources of less-defined data. Unstructured data does not reside in standard databases. In this age of social media, unstructured data plays a bigger role in business with each passing year and is often not utilized by businesses in a constructive manner. Big data tool sets are being developed or evolving across the globe to help businesses organize and use this important component of big data in a valuable way.

Big Data Technologies

In an article published at Computer Weekly, Dave Lounsbury, the chief technology officer at The Open Group, states "Mobile devices, social networks and real-time information are driving big data. Be prepared to handle this by developing competence in data architecture and analysis tools."

Popular big data technologies are produced and operated by a number of companies, and include Hadoop®, Storm®, Cloudera® and Gridgain®. Although each product has different features and capabilities, all provide massive storage, huge processing power and flexibility in tasks, programming language, and query complexity.

Advantages

Analysis offers significant advantages:

  • Existing forms of managing large volumes of data can be expensive. They often require a company to reinvent its processes and software systems to handle large volumes of data. Big data analysis simplifies this process by performing advanced data processing and analysis at high speeds, usually in a cloud computing environment. These technologies make the process more flexible for users and allow them to scale processes to specific needs. Big data tool sets such as Hadoop and other technologies which use cloud-based analytics can provide substantial cost advantages.
  • This data analysis also provides fast results. The real-time feature of big data increases responses to inquiries, trends and customer needs. Many companies need fast data which can be acted upon quickly and accurately. For example, feedback from real-time analysis can allow a company to personalize content, advertisements, websites and email responses to suit customer preferences while they are 'hot' contacts.

Disadvantages

There are also some disadvantages to this type of data analysis:

  • Big data detects subtle correlations in data very effectively (much more than in smaller data bases), but it's not good at assigning meaning or importance to correlations.
  • Using real-time insights from high speed big data info requires a different way of applying analytic solutions to business decisions. It may also require faster reaction times and new ways to organize work patterns and staff responsibilities.

Who Uses Big Data?

Big data is providing a new information frontier in many industries.

Banking

Many banks were the first companies to wade into this type of data analysis, and institutions such as Wells Fargo and Citibank have substantial Hadoop projects underway which augment existing storage and processing capabilities for those businesses. Banking institutions use big data to minimize fraud and other banking risks, increase customer satisfaction and access, and to ensure regulatory compliance.

Doctor against data analysis backdrop

Health Care

The health care industry can especially benefit from big data analysis to ensure good patient outcomes, regulatory compliance, accurate information, timely response capabilities, and patient confidentiality. BlueShield of California has recently implemented a big data platform that will help provide innovative, evidence-based health care.

Retail

Positive customer relationships and advertising effectiveness are critical areas in the retail industry which can benefit from this type of data analysis and analytics. Real time feedback from this analysis is an amazing tool which can meet both of these business needs. Stage Stores, the $2 billion department store company that operates Bealls and other mid-level departmenrt stores, has used big data analysis and analytics to drive sales in its chain.

United Parcel Service (UPS)

The start-up technology companies of the ".com" era were among the first businesses to embrace this form of analysis and analytics. Over time, established, innovative businesses began to see the advantages of having access to big data feedback, and they joined the movement. United Parcel Service (UPS) is one global business which has utilized big data in many aspects of its business. It was actually one of the first major companies to use big data in its daily operations in the 1980s.

UPS uses big data analysis and analytics to track package movements, coordinate transactions and increase company efficiency in many ways. According to the International Institute of Analytics, the company now tracks data on 16.3 million packages per day for 8.8 million customers, with an average of 39.5 million tracking requests from customers each day. UPS stores over 16 petabytes of data in its big data tools. UPS' ORION initative is an especially interesting use of this data analysis. The initiative collects and uses data from telematics sensors installed in over 46,000 UPS vehicles. This data helps UPS design route structures to monitor and control fuel and other vehicle efficiency factors.

What Does Big Data Mean to Your Business?

The use of big data analysis and analytics must begin with an analysis of your business to determine if there is a practical and cost effective use for big data. Businesses that need comprehensive and fast feedback on trends and customer preferences will find this kind of analysis a valuable tool. Other businesses in highly regulated arenas can use big data and analytics to control risks and provide compliance-related information quickly and accurately. By using existing cloud-based solutions, the use of big data can be customized to every business size and financial budget.

Results in Real Time

Smart business managers need to put aside old conceptions about data collection and how it's used if they want to stay competitive in today's marketplace. Big data analysis and analytics are business tools that are here to stay because the information age demands real-time insights and feedback.

Big Data Analysis