The simple answer is a quantity of digital information defined by a one followed by eighteen zeros: 1,000,000,000,000,000,000. To put it in context, a typical laptop computer comes with six gigabytes (billion bytes) of memory and a 500 gigabyte hard drive. By comparison, an exabyte is one billion gigabytes. It has been estimated that if all the words ever spoken by human beings could be captured and stored as text, the system would require approximately five exabytes of storage. The same study estimated that “If digitized with full formatting, the seventeen million books in the library of Congress contain about 136 Terabytes of information; five exabytes of information is equivalent in size to the information contained in 37,000 new libraries the size of the Library of Congress book collections.”[1]
Telecommunications giant Cisco Systems is projecting that, by 2020, the amount of data stored globally in data centers will reach 915 Exabytes[2], while global IP traffic will exceed 2,740 Exabytes per year[3]. These numbers are so large that they are difficult, if not impossible, to visualize. Even more impressive are their actual and projected rates of growth between 2015 and 2020: 40% and 24%, Compound Annual Growth Rates (CAGR), for data stored in data centers and for IP traffic, respectively.
The Library of Congress in Washington, D.C., is the largest library in the world. Cisco predicts that actual data stored in data centers world wide will reach 915 Exabytes. That's the equivalent of 6,771,000 Libraries of Congress, if all of its books were digitized in color, with full formatting!
Why should we care?
Because the deluge of data and information in our environment is blinding decision-makers across the spectrum, from personal situations, to operations, finance, diplomacy and war. We have access to more information than at any time in history, but find it increasingly difficult to quickly identify and access the information we need, when we need it. As Dr. Mica Endsley, one of the country’s top experts on situational awareness describes it:
The problem with today’s systems is not a lack of information, but finding what is needed when it is needed… Unfortunately, in the face of this torrent of data, many operators [decision-makers in our case] may be even less informed than ever before. This is because there is a huge gap between the tons of data produced and disseminated and the operator’s ability to find the necessary bits and process them together with the other bits to arrive at the actual information required for their decisions[4].
Corporations and governments spend billions of dollars every year on information systems and software to manage terabytes of private, locally stored information. Local data is miniscule when compared with what is available across global networks, and most decision-makers need access to both. Incorporating local and global data to deliver timely, actionable awareness requires a different paradigm built on contextually aggregating and presenting the right information, at the right time and with the proper context to decision-makers. This paradigm returns the human element to the core of system design requirements, a place that for decades has been occupied by technical specifications full of references to storage capacity, access times and other technical jargon, which though important, do not fully address decision-making requirements.
Solutions?
A proven approach is to ensure that information systems and cloud services specifications address the fast growing data rates, scope and content, operational-competitive needs of decision-makers. In other words, check your information systems specifications and make sure that the needs of decision-makers at applicable organizational levels and operational contexts are made explicitly clear in bid requests, are addressed by winning bids and are included in procurement documents. These are not trivial undertakings and, for large entities such as government agencies and private enterprises, an experienced technical team that is well-versed in the intricacies of information management, cloud services, data analysis, reporting, presentation, and the organization’s decision-making processes are indispensable to crafting a solid specification and ensuring that winning bids meet critical requirements.
Developing specifications help organizations identify, quantify and prioritize their needs. They are particularly important for those undergoing change in response to growth, external threats and opportunities, changing markets and disrupting technologies. Many organizations experiencing fast growth, for example, have stumbled when their systems and services failed to sustain expanding operations. These situations are frequently made worse when systems and services are quickly upgraded without full consideration of new and evolving needs. It’s not always easy to commit limited time to work on specifications, which leads to recurring cycles of expensive, operationally disrupting upgrades. These ultimately force organizations to specify their needs and select the most cost-effective systems to address them.
Uncited works
Applehans, W., Globe, A., & Laugero, G. (1999). Managing Knowledge, a practical web-based approach. Reading, MA: Addison Wesley Longman, Inc.
Badkey, W. B. (2008). Research Strategies, finding your way through the information fog. Lincoln: iUniverse.
Johnson, J. H., Fiore, S. M., & Smith, C. (2002). Application of Cognitive Load Theory to Developing a Measure of Team Decision Efficiency. US Navy, Naval Air Warfare Center Training Systems Division. Orlando: Naval Air Warfare Center Training Systems Division.
Sowell, T. (1996). Knowledge and Decisions (2nd ed.). New York: BasicBooks.
Citations
[1] How much information – 2003, Berkeley School of Information, accessed September 19, 2007, http://www2.sims.berkeley.edu/research/projects/how-much-info-2003/execsum.htm
[2] Cisco Global Cloud Index: Forecast and Methodology, 2015–2020, p. 12, Cisco Systems, https://www.cisco.com/c/dam/en/us/solutions/collateral/service-provider/global-cloud-index-gci/white-paper-c11-738085.pdf
[3] Cisco Visual Networking Index: Forecast and Methodology, 2016–2021, Table 1, Cisco Systems, https://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/complete-white-paper-c11-481360.html
[4] Endsley, M. R. (2000). Theoretical underpining of Situational Awareness. In M. R. Endsley, Situational Awareness: analysis and measurements (p. 24). Mahwa: Lawrence Erlbaum Associates, Inc.