The fallout from this report prompted NIST to retire Dual_EC_DRBG from its recommendations and to advise users to transition to other random number generators. After the NIST advisory, Juniper admitted that ScreenOS used the Dual_EC_DRBG, but claimed that it did so "in a way that should not be vulnerable to the possible issue that has been brought to light." Instead of using the P and Q constants recommended by NIST, which are supposed to be points on an elliptic curve, ScreenOS uses "self-generated basis points." Furthermore, the output of Dual_EC is then used as input for another random number generator called FIPS/ANSI X.9.31 that's then used in ScreenOS cryptographic operations, the company said at the time.
We all know that BYOD stands for “bring your own device,” but within 10 years I believe we’ll know that acronym by its new meaning: “bring your owndata center.” But don’t take my word for it; by 2016, Gartner predicts that 30 percent of BYOD strategies will leverage personal applications and data for enterprise purposes. What that means is that the line between personal and enterprise data usage will blur. Now we see an unmistakable trend emerging – millennials are redefining not only when and where they work, but also how they get their work done. This is no passing phase, either. It will present an ongoing challenge for IT device makers and service providers – not to mention CIOs trying to formulate a coherent BYOD strategy – for years to come.
It is usually better if you are not the first to evangelize the use of data. That said, data scientists will be most successful if they put themselves in situations where they have value to offer a business. Not all problems that are statistically interesting are important to a business. If you can deliver insights, products or predictions that have the potential to help the business then people will usually listen. Of course this is most effective when the data scientist clearly articulates the problem they are solving and what its impact will be. The perceived importance of data science is also a critical aspect of choosing where to work – you should ask yourself if the company values what you will be working on and whether data science can really make it better. If this is the case then things will be much easier.
Interestingly, a number of startups disrupting the big data virtual reality markets are gaming studios. And that is because gaming studios have had the extensive experience and the unique ability to analyze and visualize tonnes of data. It is high time the remainder of the scientific community and other industries leverage that expertise. But there is doubt if these startups are best placed to lead any mass adoption movement for virtual reality. An important factor to consider is that virtual reality startups are mostly project driven based and lack the mind-set to create products with industry wide applications. Another challenge highlighted by the skeptics is that there isn’t great content for virtual reality yet. Maybe true, maybe not. Undoubtedly, creating and telling a data story in an immersive environment is a mammoth challenge.
This technology platform is open and programmable. As such, it holds the potential for unleashing countless new applications and as-yet-unrealized capabilities that have the potential to transform everything in the next 25 years. At its core, the blockchain is a global database – an incorruptible digital ledger of economic transactions that can be programmed to record not just financial transactions, but virtually everything of value and importance to humankind: birth and death certificates, marriage licenses, deeds and titles of ownership, educational degrees, financial accounts, medical procedures, insurance claims, votes, transactions between smart objects and anything else that can be expressed in code. This ledger represents the truth because mass collaboration constantly reconciles it.
Consider the digitally connected lifestyles we lead today. The devices some of us interact with on a daily basis are able to track our movements, vital signs, exercise, sleep and even reproductive health. We’re disconnected for fewer hours of the day than we’re online, and I think we’re less apprehensive to storing various data types in the cloud (where they can be accessed, with consent, by third-parties). Sure, the news might paint a different story, but the fact is that we’re still using the web and its wealth of products. On a population level, therefore, we have the chance to interrogate data sets that have never before existed. From these, we could glean insights into how nature and nurture influence the genesis and development of disease. That’s huge.
The near pervasiveness of social technology has delivered death back into our daily interactions. With the exception of our friends and closest kin, we typically encounter news of deaths through social media. The same feed that informs us about sports scores and plot twists on ‘‘Empire’’ also tells us, without any ceremony, that a life has come to an end. This could be a blurring of a sacred line, the conflation of the profound with something profane. But this flattening has a benefit: We can no longer avert our eyes from tragedy. We have seen how people used social media to ensure that Americans did not ignore the deaths of people like Freddie Gray, Walter Scott and Sandra Bland, amplifying them into a rallying cry for justice. The mass shootings in Paris and San Bernardino felt, somehow, closer to our lives because they played out on our screens and in our browsers.
We’ve recently had an important insight about how the trajectories of technological improvement are different in different industries. In some industries the trajectory of technological improvement is very steep, like the disk drive industry where every eight years some firm was getting eliminated. In others, the trajectory of improvement is gentler, like in discount retailing. And finally in others, the trajectory is flat, as it was historically in higher education prior to online learning. This has important implications for disruption. When it’s flat, disruption doesn’t occur. New technology and business models can bring significant change to an industry where disruption hasn’t yet occurred, as Airbnb is bringing to the hotel industry.
Need for data capture and analysis have brought organizations to a point where it is important to merge and use various data systems and mart to harness the complete value of that data. So, new techniques and technologies need to be employed to achieve this goal. Organizations need to develop the basic infrastructure and capability to support data capture, data integration, data analysis and reporting. This also implies that you need to invest in new technology, upgrade legacy systems and do change management to train personnel. There is also a need for new technologies that can help satisfy the need for data maneuvering and consumption in an easier fashion.
Corporate history is littered with examples of companies that failed to see the significance of new technologies,or were too slow to act upon them. The digital disruption impacting on all sectors of the economyrequires organizations to embrace and leverage technologies more quickly than in the past, and with imagination. New business models are emerging that present opportunities and threats to established models. The rate and magnitude of technological change is both exciting and challenging and requires organizations to be more agile, flexible and creative. Globalization, increased competition, and heightened user expectations present organizations with significant challenges in continuing to be successful and remain relevant.
Quote for the day:
"Every time you have to speak, you are auditioning for leadership." -- James Humes