Big Data Analytics: Turning Big Data into Big Money
Unique insights to implement big data analytics and reap big returns to your bottom line
Focusing on the business and financial value of big data analytics, respected technology journalist Frank J. Ohlhorst shares his insights on the newly emerging field of big data analytics in Big Data Analytics. This breakthrough book demonstrates the importance of analytics, defines the processes, highlights the tangible and intangible values and discusses how you can turn a business liability into actionable material that can be used to redefine markets, improve profits and identify new business opportunities.
- Reveals big data analytics as the next wave for businesses looking for competitive advantage
- Takes an in-depth look at the financial value of big data analytics
- Offers tools and best practices for working with big data
Once the domain of large on-line retailers such as eBay and Amazon, big data is now accessible by businesses of all sizes and across industries. From how to mine the data your company collects, to the data that is available on the outside, Big Data Analytics shows how you can leverage big data into a key component in your business's growth strategy.
include an archive component, which is important for organizations that are dealing with historical trends or long-term retention requirements. From a capacity and dollar standpoint, tape is still the most economical storage medium. Today, systems that support multiterabyte cartridges are becoming the de facto standard in many of these environments. The biggest effect on cost containment can be traced to the use of commodity hardware. This is a good thing, since the majority of Big Data
With the 1890 census, things began to change, thanks to the introduction of the first Big Data platform: a mechanical device called the Hollerith Tabulating System, which worked with punch cards that could hold about 80 variables. The Hollerith Tabulating System revolutionized the value of census data, making it actionable and increasing its value an untold amount. Analysis now took six weeks instead of seven years. That allowed the government to act on information in a reasonable amount of time.
of structured, semistructured, and unstructured data. Although the promises wrapped around Big Data are very real, there is still a wide gap between its potential and its realization. That wide gap is highlighted by those who have successfully used the concepts of Big Data at the outset. For example, it is estimated that Google alone contributed $54 billion to the U.S. economy in 2009, a significant economic effect, mostly attributed to the ability to handle large data sets in an efficient
that have shunned business intelligence solutions in the past and have come to rely on other methods to develop their markets and meet their goals. For the enterprise market, Big Data analytics has proven its value, and examples abound. Companies such as Facebook, Amazon, and Google have come to rely on Big Data analytics as part of their primary marketing schemes as well as a means of servicing their customers better. For example, Amazon has leveraged its Big Data well to create an extremely
There are far-reaching implications in how big science is working with Big Data; it is helping to redefine how data are stored, mined, and analyzed. Large-scale experiments are generating more data than can be held at a lab’s data center (e.g., the Large Hadron Collider at CERN generates over 15 petabytes of data per year), which in turn requires that the data be immediately transferred to other laboratories for processing—a true model of distributed analysis and processing. Other scientific