Big Data Velocity
Big Data Velocity refers to the speed at which large volumes of data are generated, processed, and analyzed. It encompasses the rapid rate at which data is being produced, collected, and updated, often in real-time. This aspect of big data is crucial for organizations to effectively capture, store, and utilize data to gain valuable insights and make informed decisions.
7 Key excerpts on "Big Data Velocity"
eBook - ePubCyber Society, Big Data, and Evaluation
Comparative Policy Evaluation
- Gustav Jakob Petersson, Jonathan D. Breul, Gustav Jakob Petersson, Jonathan D. Breul(Authors)
- 2017(Publication Date)
- Routledge(Publisher)
...This granularity of Big Data has made some observers draw a parallel to the invention of the microscope, which allowed researchers to look at the physical world in much greater detail (Taylor et al., 2014). Another central component of Big Data is that of velocity. Big Data is generated, captured, processed, and made available in close to real time, providing a continuous stream of information. As just one example, Wal-Mart processes more than one million customer transactions, generating more than 2.5 petabytes of data every hour (Kitchin, 2014). The unprecedented velocity of Big Data stands in marked contrast to traditional data sources, which are often episodic and in effect delayed in time. The third important characteristic of Big Data is that it is massive not only in scale but also in variety. Big Data spans from traditional data sources, digitized with intent, to passively generated data in the form of unintended digital traces left behind on the Internet. In this way, Big Data creates the potential to render into data many aspects of the world, which have never been collected or even considered as data before. Finally, and advancing beyond the three Vs (volume, velocity, and variety) promoted by Gartner (2012), others have emphasized veracity, either as another defining characteristic of Big Data or as a challenge Big Data providers unavoidably face. Veracity is in the context of Big Data referring to data that may be structured or unstructured, uncertain, and imprecise. The importance of the veracity issue regarding Big Data stems in large part from the velocity and variety alluded to above, from the ways in which Big Data is generated—as opposed to traditional survey data that would be collected as part of a prearranged and controlled process. In summary, the term Big Data is used in many different ways, mirroring a new world of data...
eBook - ePub- Brian C. Castellani, Rajeev Rajaram(Authors)
- 2022(Publication Date)
- SAGE Publications Ltd(Publisher)
...For example, in the Forbes article we mentioned earlier about how big data is changing the airline industry, its author explained that ‘today, through thousands of sensors and sophisticated digitised systems, the newest generation of jets collects exponentially more, with each flight generating more than 30 times the amount of data the previous generation of wide-bodied jets produced. . . . By 2026, annual data generation should reach 98 billion gigabytes, or 98 million terabytes, according to a 2016 estimate by Oliver Wyman.’ 3 Variety: Big data today is also generated through a wide array of types and formats: structured and unstructured, relational, transactional, longitudinal, discrete, dynamic, visual, textual, numeric, audio, geospatial, physical, ecological, biological, psychological, social, economic, cultural, political and so on and so forth. Velocity: In our big data world, the issue is not just the speed at which massive amounts of data are being generated but also the speed at which they often need to be acquired and processed. Also, there is a significant amount of big data that remains important for very short moments of time: for example, delayed flight schedules, ticket price fluctuations or sudden interruptions in travel that an airport has to respond to quickly. And then there are the complex ways this increased data velocity, in turn, speeds up the decision-making process – forcing decisions, often times, into a matter on nanoseconds rather than days, weeks or months; all of these present major challenges to the hardware and software of companies and users – not to mention the ‘knockoff’ effects on social life that come from this increased speed in decision-making. Variability: While the velocity and volume of big data appear constant, in actuality they are rather variable, with inconsistencies in their flow, as in the case of a sudden Twitter trend or online searches in response to a disease outbreak...
eBook - ePubBig Data Analytics
Turning Big Data into Big Money
- Frank J. Ohlhorst(Author)
- 2012(Publication Date)
- Wiley(Publisher)
...Purity of the information is critical for value. 4. Velocity. Often time sensitive, Big Data must be used as it is streaming into the enterprise in order to maximize its value to the business, but it must also still be available from the archival sources as well. These 4Vs of Big Data lay out the path to analytics, with each having intrinsic value in the process of discovering value. Nevertheless, the complexity of Big Data does not end with just four dimensions. There are other factors at work as well: the processes that Big Data drives. These processes are a conglomeration of technologies and analytics that are used to define the value of data sources, which translates to actionable elements that move businesses forward. Many of those technologies or concepts are not new but have come to fall under the umbrella of Big Data. Best defined as analysis categories, these technologies and concepts include the following: Traditional business intelligence (BI). This consists of a broad category of applications and technologies for gathering, storing, analyzing, and providing access to data. BI delivers actionable information, which helps enterprise users make better business decisions using fact-based support systems. BI works by using an in-depth analysis of detailed business data, provided by databases, application data, and other tangible data sources. In some circles, BI can provide historical, current, and predictive views of business operations. Data mining. This is a process in which data are analyzed from different perspectives and then turned into summary data that are deemed useful. Data mining is normally used with data at rest or with archival data...
eBook - ePubInnovating Analytics
How the Next Generation of Net Promoter Can Increase Sales and Drive Business Results
- Larry Freed(Author)
- 2013(Publication Date)
- Wiley(Publisher)
...Remember microfiche? Remember stacks of old, yellowing newspapers and magazines in libraries? No more. I bet my sons have never even used microfiche. With the amount of digital data doubling every three years, as of 2013 less than 2 percent of all stored information is nondigital. An extraordinary change. So what is a workable definition of big data? For me, it is the explosion of structured and unstructured data about people caused by the digitization and networking of everything: computers, smartphones, GPS devices, embedded microprocessors, and sensors, all connected by the mobile Internet that is generating data about people at an exponential rate. Big data is driven by the three Vs: an increasing Volume of data with a wide range of Variety and gathered and processed at a higher Velocity. Big Data Volume The increase in volume provides us a bigger set of data to manipulate. This provides higher accuracy, a lower margin of error, and the ability to analyze the data into many more discrete segments. As entrepreneur and former director of the MIT Media Lab Frank Moss explains in an interview on MSN 1 : Every time we perform a search, tweet, send an e-mail, post a blog, comment on one, use a cell phone, shop online, update our profile on a social networking site, use a credit card, or even go to the gym, we leave behind a mountain of data, a digital footprint, that provides a treasure trove of information about our lifestyles, financial activities, health habits, social interactions, and much more. He adds that this trend has been “accelerated by the spectacular success of social networks like Facebook, Twitter, Foursquare, and LinkedIn and video- or picture-sharing services like YouTube and Flickr. When acting together, these services generate exponential rates of growth of data about people in astonishingly short periods of time.” More statistics show the scope of big data...
eBook - ePubSEO Help
20 Practical Steps to Power your Content Creation, Marketing and Branding in the new AI world of Google Search
- David Amerland(Author)
- 2020(Publication Date)
- Cool Publications(Publisher)
...Understanding the Flow of Data Everything we do on the web is data. A comment made, a picture shared, a meme created, content you have slaved over for weeks and a video that took you a few minutes to shoot on a smartphone. All of this is data. Data has an impact and that impact is governed by the 4Vs: Volume, Velocity, Variety and Veracity. These four attributes, taken individually and together help define the importance of a piece of information which is to say that they basically become the identifiers that allow us to filter important data from not-so-important data. Across the web this distinction is important because it is used by semantic search as a first-layer of filtering to ascertain what is happening. It is this first-layer that I want to focus on right now because it is this that will guide most of your metrics when it comes to that moment when you will have to measure the effectiveness of your marketing efforts. Fig. 18.1 - What happens to a piece of data on the web depends, largely, upon how far it travels and who then interacts with it. The 4Vs that define all data, constitute a signal that means something about the data that you are sharing. How much data is being created (Volume), how fast it gets to travel (Velocity), who interacts, blogs about it, reshares it in a different context (Variety) and how trustworthy is everyone who’s involved (Veracity) are what actually defines the intrinsic value of the data that you share. To put this in a simpler perspective, the greatest idea in the world, shared in the woods with some bears is unlikely to have any impact on the world or do anything to enhance your reputation (unless, perhaps bears start using smartphones and social media). You probably know from your own interaction with content that all of these things play a role when you decide what to read and what to reshare. How we evaluate content does not rely just on the perceived quality of it...
eBook - ePubIt's All Analytics!
The Foundations of Al, Big Data and Data Science Landscape for Professionals in Healthcare, Business, and Government
- Scott Burk, Gary D. Miner(Authors)
- 2020(Publication Date)
- Productivity Press(Publisher)
...You can count on data access getting faster; data will continue to get bigger. Many of the challenges that have persisted for years remain today because the data is always changing. As data volumes grow, the data variety expands; data velocity increases and existing technology cannot handle it. Technology must respond and adapt to these changes. The search for the “single version of the truth” is an ever-present reason for new concepts and solutions. Technology will continue to enable all growth. Data sourcing via APIs (application programming interfaces) will continue to grow and diversify, creating larger volumes. Network speeds and 5G will increase velocity; cloud and cloud-related technologies will expand data variety. The use of analytics is the only way to prove the value of data, otherwise, collecting data is just a cost. Postscript We have covered many data-related concepts and technologies in this chapter. We have provided information on Big Data to Small Data, from structured to unstructured, from old data technologies to new ones. Data has replaced oil as the most valuable resource, that is – if we use it, to do that – It’s All Analytics! In the next chapter, we will cover statistics, a much feared subject, and one that is often very misunderstood. However, it is a very important subject for all forms of analytics. We will also cover prescriptive analytics and causal models. References Buneman, Peter. May, 1997. “Semistructured data,” in Proceedings of the sixteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems, 117–21, https://doi.org/10.1145/263661.263675. https://dl.acm.org/doi/10.1145/263661.263675. Burk, Scott and Miner, Gary. Expected release 2021. Designing an Integrated AI, Analytics, and Data Science Architecture for Your Organization. Taylor and Francis Press. Columbus, Louis. April 7, 2019...
eBook - ePubBlockchain
From birth of Internet to future of Blockchain
- Srinivas Mahankali(Author)
- 2019(Publication Date)
- BPB Publications(Publisher)
...CHAPTER 7 Big Data Evolution, Landscape, Exponent & Implications The huge interaction provided by the Web 2.0 and the increasing commercial interaction led to the generation of vast amount of data that not only provided huge opportunity for publishers to gain insights, but also was problematic to handle in the short run. UNIT ABBREVIATION STORAGE Bit B Binary Digit, Single 1 or 0 Nibble - 4 bits Byte/Octet B 3 bits Kilobyte KB 1024 bytes Megabyte MB 1024 KB Gigabyte GB 1024 MB Terabyte TB 1024 GB Petabyte PB 1024 TB Exabyte EB 1024 PB Zettabyte ZB 1024 EB Yottabyte YB 1024ZB The world's technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s; as of 2012, every day 2.5 exa-bytes (2.5x1018) of data are generated. Based on an IDC report prediction, the global data volume will grow exponentially from 4.4 zettabytes to 44 zettabytes between 2013 and 2020.By 2025, IDC predicts there will be 163 zettabytes of data. One question for large enterprises is determining who should own Big Data initiatives that affect the entire organization. Source: Wikipedia The increasing number of Data sources in an organization and their complexity led to reduction in speed of processing time and operations. Data is characterized by its: • Variety • Velocity • Volume • Veracity & • Value (embedded in the data that needs to be mined) While there have always been a number of Statistical tools and techniques to analyses data to come out with right inferences and corresponding actions, the increasing volumes of data prove to be unmanageable. The different types of analytics that are performed on the data, summarized in the following figure, gave immense power to those using the same to target their customers with precision and walk away with high market shares...
