When there’s a bust, we expect a backlash that leaves people uncertain of the future potential for Bitcoin, cryptocurrencies, and ICOs. Meanwhile, the underlying buildout of the blockchain infrastructure that’s being created to support all this will ultimately enable a more trusted world. How long it takes to create that trust depends on more than technology. It depends on us understanding these cycles and how to handle the inevitable human emotions of fear and greed that inflate and burst the bubble. Hopefully, this article has prepared you to think for yourself beyond those emotions.
Cognitive Brain Cloud Computing coupled with Blockchain Based security models is having a moment using latest technical jargon in IT world. Artificial neural network algorithms like deep learning, which are very loosely based on the way the human brain operates, now allow digital computers to perform such extraordinary feats as translating the language, hunting for subtle patterns in huge amounts of data, and beating the best human players at Go. But even as engineers continue to push this mighty computing strategy, the energy efficiency of digital computing is fast approaching its limits.
Many people have been wondering whether blockchain will be the new paradigm in digital certification for the education and training world. As the buzz spreads, some already seem convinced that integrating blockchain technology into the issuance of academic records such as diplomas, affidavits, certificates, transcripts, etc. will be an effective response to the legal and technological expectations and constraints of the education and training world. It is of the utmost urgency to clarify the advantages, disadvantages, and limitations of blockchain in digital certification. Like all technology, its role is to provide a concrete, serious solution to real challenges, one that can integrate the existing environments of the different users and their equipment, their way of operating, and their organization.
The increasing security threat surface is a major challenge for businesses, particularly those functioning in the BFSI sector. There has been a lot of news of frauds as well as hacking from this sector, which is giving Chief Information Security Officers sleepless nights. Digital deployments bring with them increased vulnerabilities. The attack surface has increased because of the extensions within enterprise. Frequency and sophistication of cyber threats are continuously growing. In the evolving threat landscape, digital transformation technologies such as Artificial Intelligence and Machine Learning hold a great deal of hope.
Data analysis helps to make sense of our data otherwise they will remain a pile of unwieldy information; perhaps a pile of figures. This is essential because analytics assist humans in making decisions. Therefore, conducting the analysis to produce the best results for the decisions to be made is an important part of the process, as is appropriately presenting the results. Its an internal organisational function performed by Data Analysts that is more than merely presenting numbers and figures to management. It requires a much more in-depth approach to recording, analysing and dissecting data, and presenting the findings in an easily-digestible format.
When changes are made to any transaction or agreement it will be visible to the parties involved in the specific transaction, which minimize your chances of blunders. In principle, Blockchain technology provides Authenticating an identity, Establishing Contracts, and Recording Transactions. Blockchain technology can be applied in a lot of different fields. However, it must be adjusted to fit the requirements of a specific business. The discussion of Blockchain technology will keep on escalating as more people are developing and understanding the applications and capabilities of the Blockchain.
While IoT has many advantages, enterprises need to overcome some important problems in cloud computing to fully gain from these potential advantages. It’s a known fact that IoT and the cloud are impossible to separate—but only about a third of the data collected by the growing army of sensors is analyzed at source. While IoT has many advantages, enterprises need to overcome some important problems in cloud computing to fully gain from these potential advantages. Fog computing facilitates the operations of computing, storage, and networking services between end devices and cloud computing data centers.
Most companies get caught up in the first few stages of product development and miss out by not planning for Stage 4 sources of value. The challenge for manufacturers aiming to profit from IoT opportunities is to manage their product development road-map strategically. They have to anticipate solution “mash-ups” and data from different ‘vertical’ silos or third-party sources. The supply-side of the IoT market faces its own challenges. Basic connectivity will be commoditised once technology choices are simplified. By then, network and platform interoperability will drive value through new business models based on shared resources and data assets.
When the concept of AI was first introduced, the HR departments were not completely convinced by it, as they feared a heavy loss in the number of jobs because of the increased dependence on machines. But gradually the organizations have opened up to it. Technologies and tools like cloud computing, business analytics, e-recruitment, CPM (Computerized Performance Monitoring) have minimized the labor of HR personnel and given them considerable time to focus on other goals. Now the question arises, that if AI is such a convenience for the HR, then what is the debate all about?
Business Intelligence (BI) has been a foundational element of enterprise computing for over thirty years. you may also have heard of Operational Intelligence (OI). BI is a highly evolved form of decision support software whereas OI is an emerging next-generation form of digital automation. And that is a very big difference indeed. Using BI to mine Systems of Record data looks to improve “human-in-the-loop” business processes by arming decision-makers with a higher quality of insights. By contrast, using OI to mine machine data logs seeks to improve “human-above-the-loop” processes
You may wonder why all the focus on personal data. What do companies want it for anyway? Basically, the answer is, to make more money. You are probably unaware of all the ways your personal data is being collected. Your cell phone company is collecting your data, a plethora of “free” apps are collecting your data, that’s how they are able to offer it at no charge but it is costing you something, you’re paying with your personal information. The bottom line is, you should be deciding if this is something you are willing to share or not.
In a consumer centric market, it is becoming increasingly important for businesses to keep up with their changing needs. It could be a market trend or a simple drift in the kind of solutions that the consumers are looking for. That’s where predictive analytics comes into play.
The Blockchain technology allows companies conducting business with one another to securely and openly record transactions, store considerably more data in comparison with conventional database storage, resulting in more refined analysis and insights. Approximately six out of ten large companies are considering the use of Blockchain technology. Blockchain represents an amazing opportunity for companies to create safe and secure global infrastructure. The more general adoption of Blockchain technologies by mainstream businesses as well as trailblazers are attempting to carve out new niches where this technology could yield significant results, appears to be closer at hand every day.
The trend of evolving cyberattacks doesn’t seem to have slowed down. Instead of creating new malware, attackers have started to upgrade existing variants by configuring them with the right threat evasion parameters. In 2018, it’s clear that companies need to adopt a high-level cyber security mechanism to keep their data safe and secure.By combining an organization’s IT department with an advanced cybersecurity framework, AI is just what organizations need to prevent increasingly complex cyberattacks. Many CIOs and CISOs have already begun to incorporate artificial intelligence (AI) into their organization’s cybersecurity plan.
Investors in the financial industry are now having to confront the challenge of managing a large volume of data in this unstructured format, assembling in-house data scientists, engineers and IT staff who can transform it into insights. This is an extremely lengthy and expensive process. The majority of buy-sides do not have access to these types of resources, and that’s why big data vendors are essential. For hedge funds, asset managers and banks looking for a big data vendor,we have narrowed down the top 10 key areas to consider when deciding on an alternative data vendor.
Machines replaced operators, manual labor, now they are coming after many more professions. Technology aids at removing obstacles and difficulties from our lives, and so we must face these challenges. Every profession will have to feel these pressures or else it will mean we are not improving, innovating or trying to transform. Since we have no choice we cannot slow down. In fact, we have to speed up and think what else machines can do, because to be successful we have to be faster, effective and efficient than earlier. We have to understand, unskill, unlearn, unsettle, innovate traditional ways of thinking.
The IoT takes most industrial organizations into a new operating domain and requires a process of self-education to begin with. Most of the questions I encountered began around the two topics of connectivity technology choices and approaches to justify the IoT business case. A major challenge for industrials is to plan their strategies and business-cases with the right frame of reference. Much like navigating with a map, the best way to make significant progress involves zooming out to see the bigger picture.
Most big data programs are focused on certain types of data. These essential data types are considered most relevant to the organization’s overall goals. But what about all the data left over? Data exhaust can offer businesses significant value – if it’s leveraged properly. No, it doesn’t have to do with being exhausted by the amount of data your business collects although that’s a common sentiment among executives. Instead, it has to do with the amount of “leftover” data produced by an organization. When you set out to collect specific types of data, other information is collected at the same time.
As big data gradually comes into play in the world of business, machine learning has become one of the most important tools for companies to solve various kinds of problems. Firms across various industries are all trying to incorporate this rising technique into their business and get a competitive edge in understanding their consumers better. Here's taking a look at the basics of machine learning and how your organization can benefit from it.
There is a theorem telling us there is no single machine learning method that performs best in all problems. So how do we find the best one that fits our needs? This post suggests that before going into complex methods and spending time on fine-tuning your deep learning model, try simple ones. As you gear up towards more complex methods, you may find that simple one is sufficient for your needs. No matter how complicated or simple a method is, it will not perform best for all the problems.
The question now is how will you measure the success of your business idea? There are various factors to be looked into depending on what your business idea is and what the current standing of your business is. All owners want their businesses to be successful but are uncertain about how to measure their business ideas or performance. Financial profitability is important for any business but that is not the only indicator of the success, growth or viability of your business.
True IoT application opportunities, however, present a new challenge because of the value potential arising from cross-silo applications. The implication for IoT platforms is that they will need to support cross-platform application usage scenarios. This may be through inter-operability capabilities such as APIs with application and application-performance status capabilities to enable a dependable quality of service and rapid problem diagnosis. Coherent data models will also be required to allow sensors to be recognised automatically by non-related applications, for example, and also to support the flow of sensor data across platforms that might be configured for silo applications.
Artificial intelligence, machine learning, natural language processing, sentiment analysis and more are just a few of the techniques which are generically called Data Science. The real question is: what is the best choice for your company regarding these services? Should you train your existing staff, hire data scientists or outsource to a professional organization? There is no single correct answer to these questions, and each entity should start with an evaluation of their expectations and needs. In this article we’ll provide some guidelines to facilitate this decision.
Organizations are looking at how AI and IoT can reduce cost, drive efficiencies, and enhance competitive advantage and support emerging business models. The industry has, in the past, pursued a siloed approach to applications and technologies. As ubiquitous connectivity continues to permeate technology sectors, an increasing need to unite energy technologies, operational technologies and IT with consumer technologies is observed in the industry. Here are few examples of how Utilities Industry can benefit from top technology trends: