Speaker Profile :
Peter Lake
Sheffield
Big Data : is a massive volume of both structured and unstructured data(consisting of billions to trillions of records of millions of people) that is so large that it's difficult to process using traditional database and software techniques.
The Forrester definition of Big Data :
Big Data is the frontier of a firm's ability to store, process, and access(SPA) all the data it needs to operate effectively, make decisions, reduce risks and serve customers.
SAP(Systems Applications and Products) :
Peter Lake
Sheffield
Big Data : is a massive volume of both structured and unstructured data(consisting of billions to trillions of records of millions of people) that is so large that it's difficult to process using traditional database and software techniques.
The Forrester definition of Big Data :
Big Data is the frontier of a firm's ability to store, process, and access(SPA) all the data it needs to operate effectively, make decisions, reduce risks and serve customers.
SAP(Systems Applications and Products) :
- Rapidly deploy solutions specific to your industry or line of business
- Receive instant answers to complex business questions
- Capture and store massive volumes of structured and unstructured data
- Unlock valuable insight from any data source, even social media sites like Facebook or Twitter
- Give your decision makers anywhere, anytime access to powerful analytical tools
4 V's of Big Data :
- Volume : The quantity of data that is generated is very important in this context.It is the size of the data which determines the value and potential of the data under consideration and whether it can actually be considered as Big Data or not.The name ‘Big Data’ itself contains a term which is related to size and hence the characteristic.
- Velocity :The term ‘velocity’ in the context refers to the speed of generation of data or how fast the data is generated and processed to meet the demands and the challenges which lie ahead in the path of growth and development.
- Variety : The next aspect of Big Data is its variety.This means that the category to which Big Data belongs to is also a very essential fact that needs to be known by the data analysts.This helps the people, who are closely analyzing the data and are associated with it, to effectively use the data to their advantage and thus upholding the importance of the Big Data.
- Veracity : This refers to the uncertainty of the data available to marketers. This may also be applied to the variability of data streaming that can be inconsistent, making it harder for organizations to react quickly and more appropriately. Generally, big data veracity has an impact on the confidence reposed by the marketer to their database. However, in a volatile big data environment, accuracy becomes an issue among digital marketers regarding the collected data for their business.
Who benefits from Big Data ?
Big data is big business. The IT research firm Gartner estimates that total software, social media, and IT services spending related to big data and analytics topped $28 billion worldwide in 2012. All estimates predict rapid growth. In addition to vendors, at least three types of organizations are harvesting value from big data.
- Companies with a tradition of fact-based decision making. Procter & Gamble and UPS are exemplars. In the 1920s P&G became the first company to make significant product and advertising decisions on the basis of detailed market research data laboriously gathered during door-to-door conversations with consumers. Today P&G uses computer modeling and simulation to analyze multiple data sources—comments collected from social media, consumer sales data, RFID data, and information from the company’s highly digitized processes—and makes fact-based decisions on a daily basis.
UPS started tracking the movements of its vehicles and packages in the 1980s. More recently, the company began using big data from telematics sensors installed in its vehicles together with mapping data and other real-time reports of drop-offs and pickups from its drivers. Using these data, UPS designs routes that, for example, minimize the number of left turns a driver must make to deliver a load. Such changes can generate big payoffs, because they are deployed with more than 100,000 drivers around the world. In 2011, guided by analysis of big data, UPS avoided adding more than 11,000 metric tons of CO2 to the atmosphere and saved $30 million in fuel costs. - Engineering and research functions. Many engineering-based companies rely on analysis of big data to make critical operating decisions. For example, as long ago as the 1960s ExxonMobil invented 3-D seismic technology, which revolutionized how the oil and gas industry decided where to drill. Collecting and processing 3-D images of geologic formations beneath the earth’s surface provided more and better data for those decisions. Today the company’s scientists and engineers use 4-D analysis (which shows changes in a field over time) to further reduce the costs and risks of exploration. Researchers at pharmaceutical and biotech companies are also using big data and powerful processing to help drive business decisions.
- The best web-native companies. Companies that connect with customers solely via the internet can capture enormous amounts of data about customer behavior. This is the perfect big-data opportunity for making fact-based decisions. One technique, which has become almost a governing ethos for Google, Amazon, Netflix, and eBay, is A/B testing, in which some users are diverted to a slightly different version of a web page, which is presenting a new idea or product. The behavior of those users (B) is then compared with that of users on the existing page (A), and the results are often subjected to sophisticated statistical analysis. This technique transforms much product-development decision making from a subjective to an objective exercise. Product designers are often shocked to learn how bad their instincts and rules of thumb are. In a neat twist, Google and Amazon are now providing tools that will help other companies follow the same approach.
Scaling :
- Horizontal Scaling : means that you scale by adding more machines into your pool of resources. It will process the data using multiple servers or spread along different servers.
Example : The Hadoop - Vertical Scaling : means that you scale by adding more power (CPU, RAM) to your existing machine.
Example : Oracle
In Big Data case, it is best to use Horizontal Scaling technique so it can perform faster to process the data. If we use Vertical Scaling, it will be slower to process the data.
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