Although advances in data analytics have enabled businesses to acquire expanded insight into large structured and unstructured datasets, these advances have limited privacy and misappropriation risks.
Having greater control over the life cycle of data and confidentiality agreements has alleviated these risks, but outsourcing sensitive or regulated data processing components to third parties is still broadly viewed as fraught with risk.
Any third party, including competitors, could share all sensitive data or data processes and algorithms, subject to the provider’s controls. However, it would open unimagined avenues of enterprise collaboration, integration, and specialisation.
Homomorphic encryption voids this significant gap. As commercial viability is still a challenge, compelling use cases are emerging. In the coming years, any organisation that tends to become a center of excellence in big data analytics will be left with no choice but to embrace homomorphic encryption.
Encryption is a digital locker where information is secured until locked inside. Plaintext data is converted to ciphertext, applying a sufficiently complicated algorithm to make the data unreadable without a decryption key. Once analysis, compliance, or any other use case requires encrypted data, it must be converted back to plaintext that can compromise security.
It addresses the core weakness by allowing the analysis of data in its ciphertext form. An early homomorphic encryption innovator, Craig Gentry, described the process by manipulating a locked box’s contents through gloves that are accessed through ports outside of the box.
It’s difficult for a third party to access locked content or what another party is working on. The box is returned to the controller once the processor has completed the assigned task, and custody is intact.
Gentry’s dissertation made homomorphic encryption achievable, but there’s a significant barrier, which is computational overhead. Processing ciphertext makes a lot of overhead as the calculations are done bit by bit. IBM claims that it has improved processing overhead and now runs 75 times faster than before. A broader range of alternative schemes has notably improved processing speeds.
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Big Data has played a major role in defining the expansion of businesses of all kinds as it helps the companies to understand their audience and devise their business techniques in accordance with the requirement.
The importance of ‘Data’ has been spoken very highly in the modern-day business. Thus, while using big data analysis, the companies must keep away from these minor mistakes otherwise it could have a major impact on their performances. Big Data analysis can be the silver bullet that can answer your questions and help your business to scale newer heights.
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The rapid growth of technology has led to many people opting for online services, and thus the collection and maintenance of data becomes a significant factor for any company. Big data analytics service providers can help the companies get a massive edge over their competitors as they would manage the data well and allow the businesses to make better business decisions. It will provide you with a combination of increased customer experience, revenue, and reduced cost and thus will create a win-win situation for your business. Big data technologies will be your perfect ally in excelling in the cut-throat business environment and come out with flying colors.
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If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
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Traditional data processing application has limitations of its own in terms of processing the large chunk of complex data and this is where the big data processing application comes into play. Big data processing app can easily process complex and large information with their advanced capabilities.
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An extensively researched list of top Microsoft big data analytics and solution with ratings & reviews to help find the best Microsoft big data solutions development companies around the world.
An exclusive list of Microsoft Big Data consulting and solution providers, after examining various factors of expert big data analytics firms and found the equivalent matches that boast the ace qualities with proven fineness in data analytics. For business growth and enterprise acceleration getting inputs from the whole data of the organization have become necessary, thus we bring to you the most trustworthy Microsoft Big Data consultants and solutions providers for your assistance.
Let’s take a look at the List of Best Microsoft big data solutions Companies.
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