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Top 5 Information Technology Innovations in Big Data

Top 5 Information Technology Innovations in Big Data


Big data evolves almost daily. The latest IT innovations have increased Big Data volumes and prospects. These challenges as well as opportunities make large volumes of data more valuable for corporations and private individuals. Big Data technology has accommodated rapidly growing digital data. Therefore, it is very important to obtain helpful perceptions from the massive amounts of available data.


Big Data Analytics

Big Data Analytics makes it possible to reduce high volumes of data and dig up information that can help in crucial decision-making process. The key is to leverage information. Big data influences quick data aggregation and management of enormous statistics. Internet communications technology including cloud computing has helped leverage Big Data.


So far, it has paved the way for sustainable opportunities such as management of data, energy grid, water, and blending of computers with wireless communication techniques. Big data projects are stimulated by the union of different technologies within and outside the ICT domain to deliver highly developed apps for diverse markets.


Aside from the confluence across a variety of technology clusters, forthcoming applications for big data analytics are expected to come together with sophisticated technologies. These include artificial intelligence, biometric approaches and processing of natural language. It fortifies applications with machine-learning capabilities for higher intelligence. A shift towards advanced intelligence platforms will act as springboard for a number of future technologies like Cognitive Computing and Cloud Robotics.


Data Security Innovation

Last year was quite tough for data security. That is why corporate and technology stakeholders are very concerned regarding this aspect in 2015. This is reinforced further by the presence of data primarily generation of mobile data, digital enterprise and real time connectivity. All these have changed the landscape making it more difficult when it comes to protection of data assets.


Therefore, analytics play a progressively important role in data security. The advanced and systematic analysis of data has modified intrusion-detection, privacy issues, digital embedding pattern in digital files, and malware prevention. Stringent security measures (use of capabilities in advanced analytics) for privacy and security concerns can set a business apart from competitors. It generates comfort and confidence with clients and consumers alike. The key is for corporations to improve roles of managers accountable for data security in their respective organizations.


Internet of Things


The Internet of Things is expected to develop fast in 2015. In fact, tools and techniques in analytics for dealing with large amounts of structured as well as unstructured data have already emerged. It is only in systems integration where things have lagged behind. Consumer and industrial applications can gain from industry standards particularly because traditional analytics architectures and techniques are not compatible with high-velocity data produced by sensors.


Data Monetization


This is another innovation. According to many analysts and researchers, companies will monetize their own data for financial profits. It seems to make sense for web-based enterprises and some industrial companies. These companies are determined to restructure their strategies surrounding data as asset. Majority of the data consists of intellectual property so businesses need to take into account costs versus benefits of making such data openly accessible.


There are likely drawbacks such as failure to set up a business model for data monetization, undervaluing technology and costs involved as well as disregarding concerns related to data accuracy. With cognitive analytics means we can now automate analytical thinking through machine learning. Cognitive analytics do not replace traditional programs in information and analytics but these have more capacity to enhance knowledge-intensive undertakings. This system makes use of information technology, computing power and human interface to create hypotheses, formulate conclusions and make recommendations. Cognitive computing allows recommendations to be graded according to accuracy of responses.


In traditional analytics, data that stands for complex challenges or questions is evaluated. The, the patterns are classified while historical or prognostic insights are made for decision-making processes on said issues. Cognitive analytics is a step higher by feeding knowledge back into the analytics ecosystem. It is applied to the step by step process and fresh or relevant challenges.


Open Source

Open source solutions have been prevalent at the Silicon Valley for over 10 years. Many websites are now using the LAMP stack.  This is a standard open source Internet platform for managing dynamic servers and sites. LAMP includes Apache, Linux, My SQL, and PHP. The last is said to be the suitable development program and operation of high-performance web apps requiring a dependable foundation.


There are other novel solutions such as HADOOP which has secured a position in conventional enterprises. Open source platforms are normally free of charge or economical. Communities around them allow accelerated development and repetition. Information technology innovators can put such technologies functional and effective although risk management should be part of the process. Users must have a comprehensible picture of infrastructure built on open source solutions to facilitate exposure to possible risks.



You can expect more innovations to come as Big Data becomes more useful for business organizations and individual users.

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