Posts Tagged Part 3

With Great Power Comes Great Responsibility

The Enterprise Data Architecture, Part 3

Every year, we face challenge after challenge regarding data security.  Whether it’s personal identity theft, corporate data hacking, ransomware or industrial espionage, it seems like for every step forward, we take two-three steps backwards.  This has a large impact not only on private consumers, but on the business world in general.

For consumers, the biggest issue being dealt with is Identity Fraud.  With the increasing amount of Identity theft & fraud, consumers are facing more and more challenges to keep their personal data safe.  More and more data is being collected from the consumer, from browsing habits to shopping trends.  Additionally, critical personal information such as credit card information, phone numbers, address, or even occasionally social security numbers are being used on various online websites.  Let’s not even start a discussion about the credit card skimmers at various retailers….

And what happens after your personal data has been stolen…?

Image Source: Ponemon Institute

 

On the business side, it appears that no one is safe.  With the recent hack of Equifax, we have seen tremendous amounts of data stolen that could be devastating to consumers.  A reported 143 million people’s data was taken, potentially including Social Security numbers.  And the worse part?  None of us even gave Equifax permission to have our data to begin with.  The list of hacks for 2017, according to ZDNet, is frightening:

  • Equifax – 143 million accounts
  • Verizon – 14+ million accounts
  • Bell Canada – 1.9 million accounts
  • Edmodo – 77 million accounts
  • Handbrake (video encoder software for Mac) – unknown number of users
  • Wonga – 270 thousand accounts
  • Wannacry ransomware – 200 thousand computers
  • Sabre – thousands of business customers
  • Virgin America
  • Cellebrite
  • Cloudfare
  • iCloud
  • TSA
  • OneLogin
  • US Air Force

The list could go on.  We live in a time where data is everywhere.  This is the reality of Big Data.  Regardless of whether it’s your personal data or  business data, it is stored somewhere, and it is potentially susceptible to to unwanted exposure.  We know that hackers are going to try their best to get to that data.  So what are the real challenges for the security of Big Data?  And what can we do to make it more secure?  According to Data Center Knowledge, here is a list of the top 9 Big Data Security challenges, and potential ways the security could be improved.

Big Data Security Challenges:

  1. Most distributed systems’ computations have only a single level of protection, which is not recommended.
  2. Non-relational databases (NoSQL) are actively evolving, making it difficult for security solutions to keep up with demand.
  3. Automated data transfer requires additional security measures, which are often not available.
  4. When a system receives a large amount of information, it should be validated to remain trustworthy and accurate; this practice doesn’t always occur, however.
  5. Unethical IT specialists practicing information mining can gather personal data without asking users for permission or notifying them.
  6. Access control encryption and connections security can become dated and inaccessible to the IT specialists who rely on it.
  7. Some organizations cannot – or do not – institute access controls to divide the level of confidentiality within the company.
  8. Recommended detailed audits are not routinely performed on Big Data due to the huge amount of information involved.
  9. Due to the size of Big Data, its origins are not consistently monitored and tracked.

How can Big Data Security be Improved?

  1. The continued expansion of the antivirus industry.
  2. Focus on application security, rather than device security.
  3. Isolate devices and servers containing critical data.
  4. Introduce real-time security information and event management.
  5. Provide reactive and proactive protection.

As stated by James Norrie, dean of the Graham School of Business at York College of Pennsylvania, “Big companies made big data happen. Now, ‘big security’ must follow, despite the costs. Regulators and legislators need to remind them through coordinated actions that they can spend it now to protect us all in advance or pay it later in big fines when they don’t. But either way, they are going to pay. Otherwise, the only ones paying will be consumers.”

 

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The Future of the Cloud

The Enterprise Application Architecture, Part 3

So technology has been moving to the “cloud” over the past number of years.  More and more of the applications, hardware and devices we use all utilize distributed technology to some degree or another.  Each year, top technology companies predict trends for the growth of cloud technologies.  It was interesting to see what some of the 2017 predictions have been so far.


IDC (International Data Corporation):

Total spending on IT infrastructure products (server, enterprise storage, and Ethernet switches) for deployment in cloud environments will increase 15.3% year over year in 2017 to $41.7B. IDC predicts that public cloud data centers will account for the majority of this spending ( 60.5%) while off-premises private cloud environments will represent 14.9% of spending. On-premises private clouds will account for 62.3% of spending on private cloud IT infrastructure and will grow 13.1% year over year in 2017.

Source: Spending on IT Infrastructure for Public Cloud Deployments Will Return to Double-Digit Growth in 2017, According to IDC.


Gartner:

Gartner predicts the worldwide public cloud services market will grow 18% in 2017 to $246.8B, up from $209.2B in 2016. Infrastructure-as-a-Service (IaaS) is projected to grow 36.8% in 2017 and reach $34.6B. Software-as-a-Service (SaaS) is expected to increase 20.1%, reaching $46.3B in 2017.

Source: Gartner Says Worldwide Public Cloud Services Market to Grow 18 Percent in 2017.


Forrester:

  • Cloud computing has been one of the most exciting and disruptive forces in the tech market in the past decade. And it’s no longer an adjunct technology bolted onto a traditional infrastructure as a place to build a few customer-facing apps. Cloud applications (SaaS), business services, and platforms (IaaS/PaaS) now power a full spectrum of digital capabilities, from the core enterprise systems powering the back office to the mobile apps delivering new customer experiences.
  • The cloud market will accelerate even faster in 2017. Enterprises use multiple clouds today, and they’ll use even more in 2017 as CIOs step up to orchestrate cloud ecosystems that connect employees, customers, partners, vendors, and devices to serve rising customer expectations.
  • Some will push further, shifting from being cloud adopters to becoming cloud companies themselves. Following early examples like GE or Bosch, these companies will become stewards of their own client and product ecosystems.
  • Cloud expansion will exacerbate the cloud management challenge, pushing CIOs to also aggressively tap new and maturing enterprise-grade security, networking, and container solutions

Source: 2017 Predictions: Dynamics That Will Shape The Future In The Age Of The Customer


Oracle:

  • Cloud-based mission-critical workloads will take off.  Cloud has long promised the migration of all enterprise production workloads. But that migration has yet to happen. The chief barrier to cloud migration remains a lack of commitment and recourse to support production service-level agreements. On one hand, cloud providers are limiting their accountability as they lack the talent to support custom portfolios. On the other, they’re failing to provide sufficient control into the public data center to self-manage service-level agreements. The IaaS provider best equipped to take more responsibility and deliver the control tenants demand will be the one to drive cloud migration in 2017.
  • Corporate-owned data center numbers will plummet.  As organizations focus their IT spending on cloud computing, they’ll begin to migrate their workloads from corporate-owned data centers to purpose-built facilities, managed and run by enterprise cloud providers. Mark Hurd predicts that we’ll see corporate-owned data center numbers fall 80 percent by 2025, and that the same percentage of IT spending will be devoted to cloud services.
  • Enterprise cloud becomes the most secure place for IT processing.  This year’s threat landscape will be highly changeable. External threats—coupled with the need for better governance and privacy mandates—will make security a key priority for all lines of business. In years past, security was a major barrier to cloud investment. Data sovereignty, data privacy, and control issues deterred many organizations from pursuing cloud adoption. But in the future, those very same concerns will be the things that draw new organizations to the cloud.
  • Digital Transformation becomes the norm.  Our world is becoming increasingly digitally connected, and it’s transforming the way we live, work, and play. These same technological advancements provide unprecedented opportunities for businesses to expand, innovate, and create new value. Sectors including healthcare, manufacturing, and even urban planning have been reimagined and redefined by the cloud. To realize these opportunities, today’s enterprises must not only develop new cloud-ready tools, but also put digital at the center of their businesses. Hidden within today’s digital connections are the solutions to our most urgent business challenges.
  • The Rise of Intelligent Applications.  Artificial intelligence (AI) might sound like science fiction, but many of us use it every day. The software behind many online shopping sites and on-demand music services, for example, is a highly successful and highly pervasive form of AI. These systems depend on technology infrastructure capable of importing, analyzing, and interpreting huge volumes of data before acting on it—all without human intervention. And the next step for such technologies? To become an established part of customer service and other business operations.
  • AI gets real.  AI and robotics have carved out a niche in the manufacturing sector, and now these technologies are poised to bring their exciting benefits to a host of new industries. The AI space is white-hot, and it’s being fueled by the data explosion. Machine learning algorithms find patterns in enormous volumes of digital information and use that data to train, learn, and become even smarter. CIOs ignore the AI wave at their peril. According to Toby Redshaw, consultant and former American Express CIO, the company that ignores AI-powered technology will be “the guy at the gunfight with a knife.”
  • Developers do more with less coding.  This year, a new tool looks set to join cloud app developers’ toolkits. “Visual” or “low” coding will be everywhere in 2017. For many organizations, the real-time enterprise has meant a rethink of application development. IT teams are often stuck with a backlog of work, preventing them from delivering applications quickly enough to capitalize on new opportunities. Visual coding enables quick, straightforward development and extension of enterprise applications. “More than ever before, application development and delivery professionals must obsess over their UI designs,” say Forrester analysts John Rymer and Clay Richardson. “Low-code vendors employ familiar drag-and-drop, WYSIWYG techniques to speed user interface creation.”
  • The Cloud empowers small business innovation.  Cloud has become a catalyst for small business growth, allowing them to innovate freely, carve out new markets, and disrupt the status quo. The digital economy demands that companies of all sizes compete based on technology-enabled value. While some seek to evolve existing business practices, others are striving to launch new services that exploit extensive, low-cost computational power. Traditionally, access to such high-performance resources has been too expensive for smaller businesses. But what once cost 100 million USD up front is now available for 10 USD per hour.
  • 60 percent of IT organizations move systems management to the cloud.  More than 90 percent of companies have multiple systems management tools, but just six percent trust their incomplete data. Consequently, IT operations professionals struggle to create effective management approaches. The pace of business is increasing. As more organizations adopt DevOps practices and focus on digital experience, they’ll need to eliminate management data silos and embrace machine learning just to keep up. Some have already embraced systems management in the cloud, unifying management data across multiple clouds and on premises. Others are benefiting from data science applied to the operational management problem. Only Oracle Management Cloud provides an intelligent, unified, cloud-based approach that applies machine learning to the complete operational data set. And while many cloud tools are built exclusively for cloud systems, ours does both.
  • 50 percent of DevTest will move to the cloud.  Last year, we predicted that DevTest workloads would have all but completely migrated to the public cloud by 2025. At Oracle OpenWorld 2016, Mark Hurd revealed that “we are nearly halfway there” already. With on-premises hardware and software, IT teams have to buy, license, and configure everything to create development environments that hopefully match production environments. Hurd estimates that the industry could save 150 billion USD by migrating DevTest to the cloud.

Source: Oracle Cloud Predictions 2017


 

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How do you Adopt?

Digital Disruption, Part 3

Originally in 1962, and most recently in 2003, professor Everett Rogers detailed a systematic system by which innovation is adopted. In his book, Diffusion of Innovations, Rogers states that adopters of innovation (or in our current discussion, technology) can be broken down into 5 distinct groups of Innovators, Early Adopters, Early Majority, Late Majority and Laggards.

Image Source: phdsoft

 

Rogers breaks down the 5 categories as such (Rogers, 2003):

  1. Innovators2.5% of the population.  Innovators are willing to take risks, youngest in age, have the highest social class, have great financial lucidity, very social and have closest contact to scientific sources and interaction with other innovators.  Their high risk tolerance gives them the flexibility to try new technologies that are not yet proven out.
  2. Early Adopters13.5% of the population.  These individuals have the highest level of opinion leadership (the process by which a person influences the attitudes or actions of another person informally) among all the adopter categories.  Early adopters are typically younger in age, have a higher social status, have more financial lucidity, advanced education, and are more socially forward than late adopters.  They tend to be more discreet in adoption choices than the early adopters which enables them to have a greater voice in opinions.
  3. Early Majority34% of the population.  They adopt innovation after a varying degree of time, which is significantly longer than innovators and early adopters. Early Majority have above average social status, contact with early adopters and seldom hold positions of opinion leadership in a system.
  4. Late Majority34% of the population.  These individuals will adopt innovation after the average adopter.  They approach an innovation with a high degree of skepticism. Late majority adopters have below average social status, little financial liquidity, and very little opinion leadership.
  5. Laggards16% of the population.  They are the last to adopt an innovation.  These individuals typically have no opinion leadership at all, they have an aversion to change-agents, and tend to be advanced in age.  Laggards typically tend to be focused on “traditions”, likely to have lowest social status, lowest financial fluidity, be oldest of all other adopters, and socially in contact with only family and close friends.

This breakdown by Rogers was based on many years of research and market study.  We can see that age, social status, finances and personal influence have a large impact on the categories of adopters. This makes sense as younger people are typically overall more willing to take risks compared to older generations.  The amount of finances available to you also directly impacts the level of risks you are willing to take.  And the social aspect is a fascinating addition to the equation.  The more socially connected you are, the higher the chance of you finding out about a new innovation.  Word of mouth at first, and then marketing later can help spread information regarding new innovations, allowing more and more individuals to investigate the innovation and decide on their participation.

This same model converts directly into the business environment.  Business Analyst Daniel Newman described in Forbes magazine how those same 5 categories apply to people within your organizations (Newman, 2016):

  1. Innovators – Innovators are the visionaries willing to try new ideas and take risks along the way.
  2. Early adopters – People in this category are the thought leaders and change drivers within an organization. They may not express a willingness to try anything that comes along, but they’re comfortable with change and helping others understand the importance of change. Some organizations refer to these individuals as technology champions.
  3.  Early majority – These individuals aren’t thought leaders, but they are the people you see lined up outside of a store on the day a new technology comes to market. As soon as they see the demonstrated benefits of a change, they’re willing to jump on board.
  4. Late majority – Typically skeptics, this population waits until a larger population adopts an innovation to invest time and effort making a change.
  5. Laggards – Every organization has individuals “stuck in their ways.” Convincing these individuals to make a change is challenging.

Newman goes on to say that in order for organizations to be able to adopt new technologies, individuals within the organization need to demonstrate the Innovator, Early Adopter and Early Majority traits in order to successfully integrate the technology at a reasonable pace.

Are you an Innovator?  An Early Adopter?  Or are you a Laggard?  A simple look at the technology you use day-to-day may tell others more than you realize.

 

References:

Newman, Daniel. (2016).  Why you should align your Business Transformation too the Adoption Bell Curve.  Forbes.  Retrieved August, 30, 2017 from https://www.forbes.com/sites/danielnewman/2016/05/31/why-you-should-align-your-business-transformation-to-the-adoption-bell-curve/#264c11721160

Rogers, Everett. (2003). Diffusion of Innovations. Free Press.

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