Artificial Intelligence (AI) is hardly in its infancy stage. It is not that accessible and thus the AI based solutions that we are using or are being deployed are far inferior to what we expect in the next two to three decades. Two AI bots could often fight each other if pitted that way, and this fight could last for centuries the way we look at AI right now. All this indicates that AI needs strong leadership; a leader who can set the direction, control and govern the very innovation.
The supply chain in the pharmaceutical industry is complex, with drugs changing ownership from manufacturers to distributors, repackagers, and wholesalers before reaching the customer. Consequences include the counterfeit drug problem and inefficient processes for conducting recalls and returns processing. These inefficiencies result in financial losses and loss of trust with consumers. The blockchain could be an opportunity platform to increase trust and transparency, with customers being able to track pharmaceutical products throughout the supply chain. Only trusted parties are granted access to write on the blockchain.
Most business problems can’t be turned into a game, however; you have more than two players and no clear rules. The outcomes of business decisions are rarely a clear win or loss, and there are far too many variables. So it’s a lot more difficult for businesses to implement AI than it seems. AI is advancing rapidly and will surely make it easier to clean up and integrate data. But business leaders will still need to understand what it really does and create a vision for its use. That is when they will see the big benefits.
Traditional IT infrastructure data flows would be transformed because data integrity could never be accidentally or maliciously altered while moving around in a multi-cloud environment, taking the concern of losing or compromising data completely off the table. If you could apply blockchain technology to the continual verifiability and integrity of your stored data before sending it from one site to another, the source site data could be checked against the target site data by sending the crypto-hash first. This would be a game-changer in accelerating the movement of data.
Software is everywhere in the modern business, and you need to develop, update and deploy it fast. A vital enabler of rapid software delivery is DevOps - the merging of application development and IT operations. It speeds up the creation and launch of new products, features and customer experiences, and ensures that they're as effective as possible. In the digital economy, DevOps underpins the flexibility you need to give customers what they want when they want it. The time for full DevOps is now.
Corporations still aren’t paying enough attention to cybersecurity issues; perhaps because there’s been a startling lack of real penalty for failing to protect information from hackers. There’s the lack of mandatory reporting and the limits of voluntary reporting. And the lack of real protection for the personal information we’ve entrusted to various companies. There’s a need to recognize that securing information is hard work on an ongoing basis. Senior executives and the board should be talking about the information security program at least annually. They need to overinvest for the next few years to recover from prior underinvestment
What are the characteristics of organizations that will be the ultimate winners in this Great AI War? What are the behaviors and actions that will distinguish those organizations that capitalize on this AI gold rush while others fumble the future? Leading AI organizations realize that data and analytics are unlike any traditional corporate assets. As the world prepares for the impending great AI war, now is not the time for organizations to be shy or to cling to old, outdated business models.
The insurance industry is rapidly evolving away from its traditional model of assessing future risks and pricing based on historical records and demographics. For decades, underwriters relied on this data to predict everything from an individual’s expected lifespan to the probability of a driver being involved in an accident. But, as it has in so many industries today, technology is disrupting this long-standing practice. In the case of insurance, the internet of things phenomenon has led this revolution.
The uptake of automation within the supply chain has, until recently, been slow. However, the development of new capabilities for automation technologies means that a growing number of companies globally are relying on RPA to streamline the flow of goods on their supply-side. But how is the leading technology trend poised to impact supply chain management? What are potential use cases as well as their logistical benefits? What can be expected of software robots in the future? Let’s look at the potential for automation within the supply chain.
Blockchain technology can solve development problems as it improves existing instruments and enables the development of new ones. Blockchain-based applications particularly address institutional weaknesses and financial inclusion because they restrict deception, corruption and uncertainties. In the future, the blockchain can also be a development vehicle empowering people directly and mitigating power asymmetries. The governments of underdeveloped countries should support the implementation of applications to benefit general development. They should, therefore, clarify legal frameworks and establish an encouraging business environment.
Data Quality Management is one of the key functions of the Data Governance to manage and improve the quality of data within the organization. Data quality remediation cannot be fully automated as there may be newer errors that need to be resolved through manual intervention. There are still a sizeable number of Data quality issues that can be automated in combination with a Machine learning capability.In this case, a cognitive Robotic process automation solution which combines machine learning capabilities and traditional RPA capabilities can be a potent solution for a faster remediation of data quality issues.
The artificial intelligence revolution is upon us. Automation, which once started as a desire to make mundane tasks easier, has advanced rapidly to create fundamental and beneficial changes to human life. Despite its widespread advantages, some have turned the discussion around AI into the negative. Doomsday scenarios in movies such as The Terminator have led to two main fears surrounding AI: its ability to be used for malicious purposes and the possibility that robots and computers could make significant changes to the world at humankind's expense.
This paper focused on blockchain applications in the manufacturing industry and discloses potentials and challenges. Based on expert interviews and a market survey, a variety of use cases of blockchain technology in the manufacturing industry was identified. These use cases were analyzed using a cluster analysis and evaluated based on criteria for a beneficial application of blockchain. Future research opportunities lie in a deeper analysis of the business processes in the manufacturing industry to further exploit the advantages of the blockchain technology.
How to use simple Python libraries and built-in capabilities to scrape the web for movie information and store them in a local SQLite database. This article goes over a demo Python notebook to illustrate how to retrieve basic information about movies using a free API service and to save the movie posters and the downloaded information in a lightweight SQLite database. Above all, it demonstrates simple utilization of Python libraries such as urllib, json, and sqlite3, which are extremely useful (and powerful) tools for data analytics/ web data mining tasks.
Remember, data does not inspire people, stories do. If you do not want to be questioned about your worth to the organization, tell them stories. We need to wrap our vision in a story that inspires emotion and motivates action. We have to be very creative to make our data into stories that are beautiful and persuasive. The best way to get your message across all the clutter is to merge these two powerful ways of communication — data visualization and narrative.
This article goes over a demo Python notebook to illustrate how to crawl web pages for downloading raw information by HTML parsing using BeautifulSoup. Thereafter, it also illustrates the use of Regular Expression module to search and extract important pieces of information what the user demands. Above all, it demonstrates how or why there can be no simple, universal rule or program structure while mining messy HTML parsed texts. One has to examine the text structure and put in place appropriate error-handling checks to gracefully handle all the situations to maintain the flow of the program
We are seeing business units such as accounting and finance that are choosing, deploying and managing their own technology. RPA is an ideal candidate for that. In a typical financial process automation scenario, we can attain about 80 to 90 percent automation levels between capture and workflow for mature solutions like accounts payable, and we’re starting to approach those levels in other areas of FPA such as sales order processing, where we’re already well above 50 percent. In the case of those remaining tasks that have historically been difficult to automate, RPA can provide two key benefits.
RPA works with your current systems, no rip and replace needed, and can be up and running within a few weeks. The ROI is fast and undisputable, and while we worry about robots taking our jobs, the simple truth is that they free us from boring manual tasks so we can focus on higher-value work. It’s kind of like getting into that driver’s seat for the first time. All those dials and gears and pedals seem overwhelming, but all you really have to do is start out in an empty parking lot and put the car into drive.
Companies that have embraced data refineries are digital-first businesses, ones that were born online into the world of analytical data. Physical product companies are now using sensors to digitize their operations and generate their own proprietary data. Regardless of your industry, you are generating data. How are you housing it? What tools are you using to find value in it? What you’re doing to ensure your business isn’t left behind in the digital refinery revolution?
Ripple’s enterprise blockchain, network, RippleNet — is constantly growing. Now, more than 100 financial institutions — across banks, payments providers and specialised companies — wish to use the power of Ripple’s blockchain technology “to provide a global payments experience that delivers instant, certain, low-cost global payments to their customers”. Next to established banks like Banco Santander, Credit Agricole, Ripple is increasingly concluding partnerships with payment institutions that are involved in or dealing with emerging markets.
Are you into Machine Learning OR are you just a Statistician? Have you been asked this question yet? Machine learning is concerned more by the accuracy of final predictions rather than the laundry list of underlying distributions and asymptotic tests in statistical methods. That doesn’t necessarily mean that the math is not complex – it just says that the intent is much simpler to understand. Contrary to the common myth, all machine learning techniques are NOT adaptive.
Are you a Data Scientist looking for a Job? Are you a Recruiter looking for a Data Scientist? If you answered yes or NO to this questions you need to read this. Hope this post will help everyone in the Data Science world. Let’s join together and help each other transform the world into a better place. Remember to have fun and that there’s much more in life than work, I love what I do, but take time for your family and friends, be happy and be kind to one another.
The medical industry impacts every aspect of our daily lives. If data is transforming healthcare, it’s going to impact more than just your personal medical care. Health data analytics allows for powerful new cures to be researched more effectively. And if you’re looking for a job in the booming data science industry, medical data would be a smart choice for your specialty. Let’s take a closer look at some of the healthcare data innovations on the horizon – you might discover a project or niche that speaks to your personal skills and passions.
If you look up data science or machine learning jobs you’ll find an ocean of postings that ask for a PhD in machine learning or related field with 3+ years’ experience. Why a PhD? Why not a Masters’ degree? And why not 2 years’ experience? Or 18 months? In reality, there aren’t a lot of cases where the work of an ML engineer actually requires them to have a PhD. So what’s the real point of asking for one?