The Rise and Fall of Nokia

In this article from PandoDaily, Farhad Manjoo predicts that Nokia’s end as a company is near. Specifically, he claims that Nokia’s reliance on other companies’ software is the primary reason why Nokia is currently struggling; whereas successful mobile companies such as Google and Apple provide the software and, occasionally, the hardware for the massively popular Android and iPhone devices, “Nokia…always thought of itself as being in the device business. It made hardware, and it only cared about software to the extent that it needed code to run on those devices.” A memo sent to Nokia’s employees by recently appointed CEO Stephen Elop supports Manjoo’s claim. In it, Elop points out that while Nokia had continued to create solid hardware, they had failed to create entire device ecosystems.

In spite of Elop’s timely analysis of the issue, Nokia is nevertheless in decline. Referencing Apple’s 1990s similar decline in the face of Microsoft’s market ubiquity, Manjoo suggests that Apple was able to regain strength by “changing the rules of the game”, offering a solution that went beyond simply providing PCs. Nokia, however, isn’t showing the same type of innovation. In an attempt to quickly reverse their deterioration, Nokia has partnered with Windows in distributing their mobile OS. However, their success now relies on that of the Windows mobile OS, and Nokia/Windows Lumia phones aren’t selling as well as either company would hope. While Elop has hopes for the upcoming Windows 8 phones, Manjoo is not as optimistic regarding Nokia’s overall fate.

On a broader scale, however, this article is very telling as to how the device market has changed in the past years. Nokia’s decline suggests that it’s no longer economically feasible for IT production companies to produce only one element of a total product and subsequently expect to be both independent from other companies and financially successful. Rather, to succeed in the current business climate, companies need to consider providing combined IT solutions to consumers’ problems.

The Development of Data Visualization

Data visualization techniques can be used to further a user’s understanding of massive data sets, and can be used for nearly any information type. Many popular data visualization techniques are adapted from already well-known aesthetics such as pie charts, heat mapping technologies, and subway maps. Additionally, data visualization standards have been influenced by the concepts of proximity, similarity, and size; many designers group associated topics near to each other and use large or small graphical representations for frequently or rarely occurring data points respectively. Smashing Magazine’s article “Data Visualization: Modern Approaches” gives a brief overview of particularly compelling data visualizations, including ChalkLabs CTO Bruce Herr’s visualization of Wikipedia articles.

While data visualization techniques are undeniably effective for static data sets (such as a city’s population demographics at a specific time), they’re also extremely useful when representing live or variable sets of information. For example, OnISP’s live call map is a live visual representation of all of the calls being made or received by phones using OnISP’s VoIP service.

This type of visualization is particularly effective due to its activity; simply listing how many users a system has or how many connections are made per minute can be informative, but it’s not particularly attention grabbing. In contrast, a real-time map tracking each connection made through OnISP gives the user a more concrete example of the significance of the represented data points. The user is able to personally experience the data, subsequently assisting in his understanding and processing of the represented information.

The increased use of data visualization is particularly important for businesses to keep in mind when considering how best to communicate pertinent information from their Big Data sets. Data visualization makes it easier for users to understand large, complicated data sets, ultimately increasing user ease of use and efficiency.

The White House Big Data Initiative

Big Data is the term applied to the general issue of the manipulation of increasingly large and complicated data sets. We take data, process it, analyze it, and then come to a conclusion based off of the given data.  The problem that users face is that the sheer volume of data through which they need to sort is becoming impractical given traditional methodologies.

In line with this growing need for Big Data solutions, the Obama Administration has recently announced the “Big Data Research and Development Initiative.” Pumping $200M of federal funding into a field largely dominated by open source communities, this initiative is full of promise. Security Week’s article, “Obama Administration Places $200 Million Bet On Big Data,” outlines the opinions of many White House officials on the subject, as well as the effects this decision will have on military and scientific fields.  “In the same way that past Federal investments in information-technology R&D led to dramatic advances in supercomputing and the creation of the Internet,” the Big Data initiative “promises to transform our ability to use Big Data for scientific discovery, environmental and biomedical research, education, and national security,” said Dr. John P. Holdren, Assistant to the President and Director of the White House Office of Science and Technology Policy.

In addition to the fields mentioned within the article, the Big Data Research and Development Initiative will also have a significant effect on commercial businesses.  Internet-based social media services and marketing groups have vast quantities of data at their disposal, but such quantities of information are difficult to process using traditional sorting methods.  Instead of having to manually sift through large quantities of complicated data to fully understand the state of your business, Big Data analytics is capable of finding patterns that common data crawling techniques seldom are able to find. Due to this increased ability to process information, companies using Big Data solutions are able to make faster and more informed business decisions than businesses that are still using traditional data processing methods; this ability to make informed decisions based on large quantities of data is an important facet of business intelligence. About 34% of organizations have reported applying Big Data analytics to large quantities of data and, in order to remain competitive, more businesses will need to begin utilizing Big Data solutions.

This overview is in response to Security Week’s article, “Obama Administration Places $200 Million Bet on Big Data“.