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:
Companies offering services based on connected devices will increasingly have access to significant amounts of highly granular data about consumers and their connected devices. This trend is heightening privacy-related concerns about the way that such data might be used and the potential for consumers to be harmed. Policy makers and business organizations that have an interest in the long-term viability of the IoT market also need to ensure that consumers are not ruthlessly exploited under apparently beneficial situations. Through this perspective, concepts of trust and stewardship related to the use of private data can be developed into new and appealing value propositions.
By harnessing data science to its full potential, top-ranking decision makers in all industries, not only make better-informed decisions but make them with clearer predictions of the future. With that advantage on their side, they are able to stabilize businesses that have not always had a clear vision and save businesses that are on the brink of collapse. Once goals have been established, data scientists can work their magic and theorize how to fix it. Data science alone is not an advantage for decision-making, data science combined with good leadership is.
What do I mean by true real-time data? It is data that has just been generated and never been stored. Because once data has been stored, no matter for how long, it is no longer real-time. Can you imagine making vital business decisions based on three-month-old insights? How about a week old? Or a day old? Minutes-old data can be irrelevant for the real-time decisions that matter most to your business, yet many people don’t understand the difference between real-time analytics with real-time data and real-time analytics with stale data.
Smart Cities will be built on a combination of infrastructure from players, including telecoms operators, mobility operators, public safety agencies, and utilities, as well as infrastructure from cities themselves, cities that will run different legacy and platform-based IoT deployments. It is widely acknowledged that no single IoT platform will dominate the market. Here we see the increasing need for IoT platforms to exchange data to address the requirements of cross-application use cases. This will only be achieved by IoT platforms designed and managed for interoperability.
Big data, Internet of Things (IoT) applications and self-service portals will make it easier than ever for businesses to anticipate their customer's needs, alleviating a load of customer support agents while providing consumers an impressively instantaneous response to their queries. Customers have changed, so technology is molding customer service into a more self-servicing, instantaneous and data-driven platform where consumers are more satisfied, while also reducing the load on a customer support team. Increasing sophistication in technology and big data makes it easier than ever for businesses to address customer support issues before they even occur.
The smart city concept is now firmly on the operational agenda of government officials and private sector solution providers. The evolution towards grounded solutions means that adopters and solution providers require tangible strategies along with workable frameworks and planning tools to initiate their smart city initiatives. Look at how the emerging smart city industry acts as a host to multiple smart city reference architecture initiatives in parallel with multiple check-list criteria that aim to rank cities on smart city implementation roadmaps. Let’s focus on two structural features of city planning and management to illustrate the real-world challenges that city authorities will have to overcome. The first deals with differing economic profiles that characterize individual cities.