Sentiment plays a very important role in decision making and the ability of a machine to convert human language into machine readable code and convert it into actionable insights, the capability offered by natural language processing (NLP). The topic of sentiment brings us to affective computing. While NLP is capable of reading or converting words into a stream of logic that can be used as an input to a computation devise, there are subtle nuances that humans use to communicate.
A trap many businesses fall into is to try and automate as many processes as possible. However, automation for the sake of automation is a real problem, because digitising everything is not a realistic goal. Businesses need to prioritise in order to establish a road map for transformation. The question is – how to identify which processes to automate in order to achieve the greatest value? That’s where process mining comes to the fore. Process mining applies the concepts of data mining to business process. The technology is largely used for process discovery; compliance auditing and process enhancement.
Need to know how to select your Continuous Testing Solution? Focus on existing processes that are being followed within the organization that is evaluating a new tool. Try focusing on how your process needs to look like, see what objectives the business and management has for your projects, and then see how tools can fit into your processes. Features are critical for the success and enable your test automation coverage, however not every tool also fits your entire processes that starts from the creation of tests through analysis and maintenance. Happy Transformation and Testing!
CTOs and tech leaders need a blockchain guide. From what is the blockchain, to adoption issues and advice surrounding regulation, compliance and more, blockchain guide is an invaluable tool for CTOs and other tech leaders looking at blockchain for their business. When blockchain adoption is no longer an issue and the technology becomes fully mature, various forms of new ideas and business models can be instantly generated, tested and realised with rapid adjustment via this autonomous DMS structure, where collaboration can be automated. The possibility is infinite with blockchain!
If you are not a huge fan of the robot, and would not trust something if you don’t see where it keeps its brain, you can still embrace automation in the old-fashioned way. What should I automate? This is the first question you will need to answer, as you wouldn’t want to invest a lot of time into automating something that should actually be done manually. Ask yourself some questions regarding if the process you are trying to automate fits your defined criteria? If so you can move on to automation.
The trend of new technology being used by businesses around the world isn’t slowing down any time soon. It is critical to adapt and implement data management strategies that ensure an optimal customer experience for your digital properties. You have to invest in resources, infrastructures, and new processes to get a holistic customer view of the data coming from your digital touchpoints. If you want to keep up with the competition, and eventually surpass them, it’s time to start using the power of data to your advantage to build a better user experience.
With the advent of the software, high speed communications, AI and the internet, there are new horizons of innovation, which bring new competitive advantages to those that embrace them. Every company needs to have a strategy for all three horizons: continue to optimize the core business, but also create subscription-based digital offerings and build a digital platform with network effect. Failing to do so will mean yielding market value, customers, and employees to companies who do.
AI is the biggest commercial opportunity for companies and industries over the next 10-15 years. Yet, despite the promise of AI, many organizations’ efforts with it are falling short. Most firms are only using AI in ad hoc pilots or applying it to a single business process. Only a few firms are engaged in practices that support widespread adoption. Why the slow progress? To support the widespread adoption of AI, companies must make three fundamental shifts.
Economic and trade uncertainty is the new certainty. Every manufacturer needs to start taking a more data-driven approach to defining the initiatives and strategies that will keep their businesses growing. The best countermeasures capitalize on and scale the data manufacturers have been accumulating in some cases for decades. The following strategies enable manufacturers to capitalize on the data they’ve been aggregating and analyzing on suppliers, pricing, production and operations, quality, and service. In short, these strategies have an insight track on succeeding during challenging, uncertain economic times by delivering quicker results and immediate payout.
It’s important to reiterate the growing connectedness of consumer demands that will play a much more significant role in changing manufacturing for the better in the coming year than those technologies themselves. Today, every company is here to serve the customer, no matter how far away from the customer they may have functioned in the past. Transformational trends such as the (A)IoT and 5G will force them to do that even more in the coming decade. It will also make that level of accountability possible.
Following AI trends and understanding the benefits of technology have become a necessity for business leaders. Early adopters of AI can gain a competitive advantage over other businesses. Also, AI coupled with big data can help business leaders predict market trends and create effective strategies for adapting to changes in the market. Hence, organizations can incorporate AI in sales departments to augment the growth of their business. The integration of AI in sales can help businesses in scoring leads, optimizing product prices, and developing personalized marketing campaigns.
What you need to know about virtual support agents. The more time the IT help desk spends putting out fires by phone, through email, or in person, the less time they have to focus on resolving the bigger issues and applying their cognitive skills to more meaningful projects. Are chatbots or virtual support agents the answer? The success of virtual support depends on several key factors. Here’s how to identify those factors and evaluate whether or not VSAs are right for your organization.
There is now a major shortage of people who have the skills and knowledge to carry out major AI research projects across different industries. Under such circumstances, those who have deeper pockets have managed to hire AI scientists for their projects. This has led to an AI brain drain, drawing scientists and researchers away from the institutions where artificial intelligence was born and developed into the revolutionary technology it has become. The AI brain drain and the commercialization of artificial intelligence will mean less transparency in the industry.
Knowledge of the breaches is discouraging some organizations from upgrading their operational technology systems with productivity-enhancing digital technology. Aware that installing sensors on industrial equipment might open them up to compromise, many would prefer to suffer inefficiency as the price they must pay for keeping their systems secure. The new and dangerous threats to OT and critical infrastructure require an innovative defensive strategy. With digital transformation implemented and the convergence of IT and OT networks, many organizations are not ready for the attacks and threats they are facing.
SSDP or Self-Serve Data Preparation is a crucial component of Advanced Data Discovery. With self-serve data prep, data analytics moves out of the sole domain of analysts and IT and into the domain of business users. With true self-serve business intelligence and analytics solutions, the average business user can perform data preparation, test theories and hypotheses by prototyping on their own and share clear, objective data with others. Self-serve tools allow users to leverage knowledge and skill and better perform against forecasts and plans.
Today, it’s hard to find anyone who doesn’t believe in the power of digital technology. Whole industries have been disrupted. New applications driven by cloud computing, artificial intelligence and blockchain promise even greater advancement to come. Every business needs to race to adopt them in order to compete for the future. Ironically, amid all this transformation the digital revolution itself is ending. Over the next decade, new computing architectures will move to the fore. Simply waiting to adapt won’t be enough. The time to prepare is now.
Are you up for Kotlin? Kotlin is a new and powerful trend. It is not difficult to say that Kotlin has been adopted by the masses for app production. Kotlin has the ability to develop a native android application in an intuitive and concise syntax that can save hefty development costs. Though Kotlin is still young as a programming language and it has been proven as a stable platform for building production apps.
Financial services enterprises like insurance agencies and mortgage companies have processes that involve endless searching, matching, comparing and filing. This can be a huge time suck on businesses and also cause risky mistakes by human error. Robotic process automation in finance and accounting can help you get a better handle on your financial data and in many cases save time and money for your business, no matter what industry you’re in. RPA can lead to better insight and potentially innovation that can help your business thrive.
In today’s ultra competitive environment, every business must implement advanced technologies to stay ahead of disruption. If your business is not part of that journey, if you don’t innovate, it’s likely the business will go bust. The use of AI in robotics and hardware is something that every CTO, especially in industries like manufacturing, must be exploring. Integrating software with hardware is necessary to stay ahead of the curve. Integrating AI (software) into robotics, or hardware, is important, because of how the architecture of computers works.
Data integrity is critical to just about every aspect of a business, but when it comes to the human resources dept. it could easily be argued that every single piece of data is both mission-critical and sensitive. HR data not only contains identity information that would be valuable to thieves, but it also contains a wealth of information that is in the company’s best interest to protect. Threats to HR data integrity may seem numerous, but we can break them down into a relatively small set of categories.
Organizations in all industries are looking for ways to capitalize on technology megatrends like AI, machine learning and the IoT. More than just increasing efficiency and reducing costs, these enabling technologies allow for the implementation of new applications, services, and revenue models. In sectors like industrial automation, health care, and test & measurement, many electronic systems have been designed around integrated architectures. This makes it difficult for engineers to adopt the latest technologies, as upgrading an integrated system with new components often leads to compatibility issues.
There are some very basic steps required to work through a large data set, cleaning and preparing the data for any Data Science project. We want you to understand that you need to properly arrange and tidy up your data before the formulation of any model. Better and cleaner data outperforms the best algorithms. If you use a very simple algorithm on the cleanest data, you will get very impressive results. And, what is more, it is not that difficult to perform basic preprocessing!
DevOps and now DevSecOps provide the tools for a much-needed cultural change inside many of today’s enterprises. Success with DevSecOps comes from being able to separate the technology stack from the data you can derive and channel into business and technology decisions. You can’t buy DevSecOps—the practice of putting security practices into your DevOps methodology—but there’s marketing noise that may make you think that you can buy your way into DevSecOps. When you’re moving your enterprise teams to a DevSecOps model, you need to see it as more than just a technology stack. Here’s why.
A distributed ledger is a database of replicated, shared, and synchronized digital data that is geographically spread across multiple sites in a network. Distributed ledgers rely on similar principles of consensus to a blockchain. Although distributed ledger technology and blockchain share the same conceptual origin and purpose — a decentralized database or log of records, they are not exactly the same. Even though they are different, the terms Blockchain and Distributed Ledger Technology (DLT) are often used interchangeably. This has led to a significant amount of confusion. So, what’s the difference?