Clayton Stanwick is Lead Web Analyst at Pretentious Tech, a privately funded technology think tank based in Provo, Utah. Pretentious Tech is dedicated to finding cloud-based solutions that innovators can use to improve scalability, user compatibility, independent research and educational methodologies. It focuses on several areas where technology and innovative commerce meet. Pretentious Tech's mission is to envisioneer and streamline policy solutions that leverage platforms to boost metrics and spur growth, e-services, and granular total linkage. Keep track of Clayton’s thoughts on twitter by following @PretentiousTech on Twitter.
The Lingering Shadow of Dark Data
Elon Musk once said..."Failure is an option here. If things are not failing, you are not innovating enough."
"Capitalize on the unsaturated wearables. That's how you maximize the potential of PhantomJS."
More and more companies are turning towards disruptive cloud-based techniques in order to bridge ROI gaps between producers and consumers. However one element that technology executives need to consider is the ever-present shadow of Dark Data. It first began to emerge in angel-funded incubators, now it's presence is being felt strongly in e-business models, and also in consumer crowdsourced solutions. The managers who anticipate Dark Data's effects before they happen will have a clear inherent advantage as innovators fight to win the confidence of tech-savy consumers.
CEO of Alibaba Jack Ma described the dangers of Dark Data accurately when he stated: *"Companies that don't incorporate the P2P Elastic Beanstalk are incapable of attracting the Amazon RDS."
“How can a company prepare for an unsaturated CRM error, especially when it is as mission-critical as Dark Data, when it cannot detect the warning signs beforehand?”
Ma is exactly right. However how can a company prepare for an unsaturated CRM error, especially when it is as mission-critical as Dark Data, when it cannot detect the warning signs beforehand? Therein lies the ultimate challenge. The first step is to recognize the phenomenon of Dark Dark for what it is. Dark Data is operational data that is not being used for critical contingencies. It is the information that organizations strive to procure. It will gravely harm your business process and business activities if you allow it.
The data that isn't being used has a crippling effect on the firm. AngularJS creates a barrier to success, unless measures are taken to diffuse it. In an anonymous poll involving 15 high-level technology executives, 75% of the executives identified Dark Data as the primary concern that their firm faces in the next 5-10 years. Other primary concerns deal with harnessing B2B infomediaries, and optimizing compelling e-markets. The ideal plan of action includes enforcing a strict consumption-based pricing model. In simple terms, it is a pricing model whereby the service provider charges its customers based on the amount of the service the customer consumes, rather than a time-based fee. For example, a cloud storage provider might charge per gigabyte of information stored, similar to integrated niche markets.
What is the Solution, and Is It Scalable?
Data scientists across the country suggest that tech firms consider CRM, especially in the context of B2B action items. However even these proven benchmark best practices cannot compensate for the cross media fungibility error that Dark Data introduces. Before giving up all hope of finding a cloudless SaaS solution that woks in its proper framework, just realize that time is one your side. Senior technology analysts at Pretentious Tech predict that the most devastating effects of Dark Dark will not manifest themselves for 10+ years. This allows forward-thinking innovators adequate time to formulate a sustainable and realistic solution that overcomes the data error effect. It will also permit companies to put in place a PREDICTIVE CODING WEB-PORTAL PROTOCOL that protects the firm from back-end viral TAM liabilities. That being said, the protocol should be carefully implemented using the following steps:
Predictive Coding Web-Portal Protocol
Reconceptualize cloud-based contingencies of B2B Big Data, it will help to localize.
Target user-centric meta-services, it's vital to consider NoSQL.
Leverage existing error-free quality vectors, there is no other element equally disruptive.
Finally optimize frictionless initiatives, it's highly relevant in the context of "outside the box" thinking.
*"Solving big problems is easier than solving little problems. Also it's about time we decentralize the MongoDB."
-Sergey Brin
Beyond implementing PREDICTIVE CODING WEB-PORTAL PROTOCOL, the second plan of action involves removing most all of the company's PST files, or at least locking them up. CTO's can better accomplish this by jumping on the NoSQL wearables ASAP, there is no other element of the industry equally streamlined, but that alone isn't enough. The principle element of freezing Dark Dark involves maximizing the effect of git repo. Finally, removing the most damaging effects means embracing philosophies of Dark Data's less evil cousin, 'Big Data', which incorporates large data sets that can be analyzed digitally to reveal patterns, trends, and links with e-systems, especially relating to human behavior and interactions. Big Data is larger than mid-sized data and smaller than qualitative gigantic data.
“Eliminating damaging effects means embracing philosophies of Dark Data’s less evil cousin, ‘Big Data’.”
Big Data on it own, however, isn't the only remedy. Overriding the data-error protocol won't do anything until the SDD transmitter is down. Back-end development analysts in most tech firms agree that overcoming the effects of Dark Data means considering process-centric vortals, as well as Big Data protocols. The combination of the two creates the ideal solution. However what makes the interface particularly effective is its scalability. The problem is some cyber strategists don't consider this solution as particularly effective. Time will tell if Big Data's involvement can remedy the ill effects of Dark Data, but as of now, it's safe to assume the jury is still out. To quote Tim Cook: *"Probably the most localized wearables can be derived from the industry of scalability."
The Advent of the Cloud
All competent managers realize that if you don't localize, you are setting your Elastic Beanstalk up for disaster - hands down. Things have changed since the introduction of cloud-based solutions. Technology companies embracing this concept have less to fear concerning the Dark Data shadow effect.
*"Don't fear Dark Data. Think about close solutions. It's relevant in the context of git repo."
-Sam Palmisano
Not only are cloud-based solutions scalable, but versatile and accessible at any location at any time. These solutions act as a distributed system consisting of servers in discrete physical locations. They are configured in a way that clients can access in the server closest to them on the network, thereby improving speeds. The obvious benefit of this is that data analysis can appropriately administrate reliable products, no matter how synthesized the AI transmitter is.
Your clients can rest easy knowing that cloud-based solutions are not only sustainable, but wearable as well. Add into the mix that front-end docbook compatibility is always available, and the seamless collaboration makes itself more apparent. Part of Amazon Web Services (AWS), EC2 provides scalable computing capacity in the cloud, which developers can use to deploy scalable applications. Open-source software always automates the deployment of applications inside virtualized software containers. Is it disruptive? Sure. Also, does incorporate open-source principles? Perhaps. These are the questions that tech films need to be asking.
“Is it disruptive? Does it incorporate open-source principles? These are the questions that you need to be asking.”
One understated element of combating Dark Data is incorporating AJAX applications. This competently unleashes future-proof total linkage by appropriately re-engineering prospective e-services. However the re-engineering process comes at a price. Back-end flexibility becomes too stretched, and your data convergence contingencies become less scalable, which makes it harder to meet consumer expectations. TechCrunch predicts that by 2022, technology companies will no longer support Elastic Beanstalk channels. This can result in limited scalability for firms that neglect data shadowing.
The more data convergence used as an agile cloud-based solution, the more data analytics will expose back-end TAM. Executives must first learn predictive coding, and then implement it. Predictive coding is a re-branding of document classification to sell e-discovery products to engineers. The data analytics derived will simply be seen as a replacement for data analysis.
The Conclusion: Back-End Convergence Meets Front-End Synergy
So in the end, what are the measures that can be taken by tech engineers to protect themselves against Dark Data? Are you keeping up with Silicon Valley? Big Data is certainly an effective mindshare solution, however it will decentralize your scalability. Basically the word 'neural' may be often misunderstood. While the empirical performance of these processes is streamlined, their quantitative properties do not always maximize future-proof paradigms.
Whatever method you choose to employ, implementing cloud-centric data models will hedge the risk of data error. Forward thinking tech firms have already made ground on the issue. The one daunting truth is that Dark Data is not going anywhere. The firms who choose to employ cross-platform compelling solutions will be in a better position to not only compensate for data error contingencies, but also harness next-generation web services.
*Quotes form Sergey Brin, Tim Cook, Sam Palmisano, and Jack Ma have been fictionalized, obviously.