A lot has been written specifically about the potential use of blockchain technologies in fresh food supply chains. The vision is that industry-wide blockchains can provide stronger assurance of origin and chain-of-custody, faster and more precise recalls, fresher produce and meat, reduce waste and spoilage, and fewer contamination incidents. A strong case can be made that the greatest value potential is in improving freshness and safety. How blockchain can help establish provenance and recall capabilities, in particular by providing traceability? To understand the role of blockchain in providing traceability, and hence provenance and recall, we examine four ways to achieve traceability.
Data Modeling refers to the practice of documenting software and business system design. A Data model is used to document, define, organize, and show how the data structures within a given database, architecture, application, or platform are connected, stored, accessed, and processed within the given system and between other systems. Data modeling is required to manage data as a resource, integrate existing Information systems, design databases and repositories, and understanding the business. Using proper modeling and reporting, you can spot business trends, spending patterns, and make predictions that will help your business navigate challenges and opportunities.
In layman’s terms, blockchain is a modern, digital ledger designed to record and secure all transactions that happen under its reach. Before a transaction or digital “block” can enter the network, it must be verified. This verification is part of what makes blockchain technology so alluring because it's transparent, accurate and decentralized. A deeper and more intrusive understanding, blockchain requires a considerable guide. For now, we’re going to focus on the pros and cons of the technology, which will probably influence whether or not you want to adopt it for yourself or within your organization.
If you’re interested in a career in tech, you have a lot of options. Following your interests is a great place to start when choosing a career path, but it also makes sense to look for jobs that have strong growth potential. Some of these jobs might require that you gain additional skills, or even go back to school for a new degree, but the effort can really pay off. Here are 5 of the tech careers that are expected to boom in 2019—could one of them be the perfect fit for you?
People often see the Industrial IoT (IIoT) in a narrow view, namely the ability to increase efficiency, productivity, and cost savings. While that’s true, it’s a limited list of benefits based on one’s understanding of connectivity, data, and information to influence behavior. There are several ways to lead organizational change, and it all depends on one’s role and how they view their role in the organization. With IIoT sensors and analytics, continuous condition monitoring is available in a cost-effective manner to have information flowing in real-time to further enhance productivity, efficiency, health, and safety.
The combination of DevOps, Continuous Delivery, and Continuous Integration is transforming the practice of product management. DevOps is now the next phase in modern software development and product management. Product managers, more than ever, must do a better job of prioritizing requirements. DevOps requires product management and other parts of the organization to also change. It’s an exciting time to be a product manager. Agile and DevOp create new opportunities. Add security into your product DNA. Work closely with your teammates to maximize process delivery and customer value. Support the changes required to ensure successful product launches and customer engagement
SQL is a critical skill. What is SQL anyway? SQL is a query language for talking to structured databases. Pretty much all databases that have tables and rows will accept SQL based queries. SQL has many flavors but the fundamentals stay the same. Professionals and amateurs alike use SQL to find, create, update and delete information from their sources of record. Understanding SQL queries can be fundamental to your work. Professionals and amateurs alike use SQL to find, create, update and delete information from their sources of record.
The most common questions asked is how do I become a data scientist? It is a fair question for those that are deciding to pivot that direction because they want to eliminate the learning waste that traditional educations are full of. The number one reason I think you will never be a data scientist is your lack of passion. Honestly, passion fixes everything. The biggest flaw in most people attempting to break into data science is their lack of breadth. People who are passionate have great breadth.
Here we focus on blockchain, Identity Management, and Corporate Social Responsibility, a timeless subject that has taken on added meaning. Identity management technology exists that can obviate the need for passwords or storing personal information in servers all over the place. There has also been a movement to try and give individuals a single, universal digital identity. With regulations on the wane (for now) as an inhibitor of bad behavior, it is heartening to see business leaders stepping up to the plate to try and create cultures and actions of corporate responsibility.
The field service component, distribution, channel, aftermarket service/repair, and integrator are the glue to the entire IoT value chain. The opportunity service organizations have to leverage IoT to transform their business is incredible. It is a tool that will allow field workers to spot issues continuously as an expanded “remote monitoring” opportunity, maybe as a SaaS model. This could be a new revenue stream, but really a chance to see more issues with continual data streaming in instead of quarterly route-based maintenance. The value lies in domain specific expertise and vertical specialization, but delivery is the key.
It is necessary to differentiate between indirect attacks and direct attacks on IoT devices. In indirect attacks, the goal of compromising IoT devices is to use them to conduct cyberattacks against other external targets. In direct attacks, the goal is to conduct some sort of ‘local malfeasance’ right there at the device itself. The IoT security asserts that manufacturers and deployers of IoT devices and systems, especially potential targets for direct attacks, have a moral obligation to vigorously and comprehensively address security. The following seven principles can serve as guideposts to enable stronger IoT security.
For an IIoT project to be successful, you need to have customer-centric IIoT approach. Start by gaining internal agreement on your target customer such as what business outcomes do they expect from your IoT-enabled products and services. Then the targeted segmentation will deliver more engaging customer experiences, higher customer lifetime value, and more valued customer outcomes. As you bring your products to market, learn from your wins and losses. Continuously flesh out user issues, new product features, and integration requirements. So put the odds of IIoT success back into your favor. Take a customer-centric, integrated team (IT) approach.
In the fast-changing world, it has become difficult to catch up with all the new concepts and technologies. It is even more complicated to distinguish which of them are really useful and which ones is just hype. In the field of data analytics, it was big data that started this era of doubt. Now, when this concept is clearer, a new wave is coming: Big data for IoT. Despite all the hype around IoT, it is just one of multiple big data sources. But how exactly IoT is connected to big data?
With Big Data increasing and the time-consuming task of data preparation, the industry is looking for better ways to improve efficiency and speed up the change of data into meaningful information. With that said, when analyzing the data journey and the constituents data serves, metadata is the common recurring theme that enables an organization that replaces “data wrangling” with data discovery. Metadata management must be a core competency, a place of innovation and of strategic importance. Data regulations and sensitive data will need to be managed, and it all starts at a metadata level in order leverage, yet protect those it intends to serve.
IoT can positively change the way we do business and the way we live our lives. IoT is a new revolution in our society, or it is just one more step in the technological evolution of the digital revolution. Today, the debate continues but whether evolution or revolution, The Internet of Things is here to stay. IoT is often presented as a revolution that is changing the face of society or the industry in a profound manner. It is an evolution that has its origins in technologies and functionalities developed by visionary automation suppliers.
IoT security breaches are expected to reach an all-time high. It’s important to differentiate between indirect attacks, using IoT devices to conduct cyberattacks against another target, and direct attacks, where the end goal is to compromise and access the IoT device itself. With direct attacks, the goal is access to the IoT device – and by extension the sensors, machines, and environment that the device is connected to. As such, this type has the potential to be even more disruptive and destructive. Criminals, terrorists, and malicious foreign governments may use connected devices to cause havoc or harm. Seven principles can serve as guideposts to enable stronger IoT security.
Everybody loves DevOps. That’s because DevOps promises to satisfy the deepest longings of digital business—including fast execution on innovative ideas, competitively differentiated customer experiences, and significantly improved operational efficiencies. But who does DevOps love? It’s a fundamental challenge for anyone leading a DevOps initiative. What passions and motivations are driving your DevOps teams? How do you know? And if those motivations aren’t the right ones, how do you re-direct them? Metrics, it turns out, may hold the answers.
As companies embark on their journey toward the 4th Industrial Revolution, the ones that are willing to bring in new leadership and make the organizational enhancements to power new digital customer relationships will be the ones that rise above the fray. Digital transformation is both a technology and a management challenge. Focusing on one without the other is not a recipe for success. Instead, companies should adopt a more holistic approach – one that starts with the target customer and ends with organizational alignment based on your targeted outcomes. Remember, start your organizational changes slowly.
Hard work has always been an important competency for aspiring students to become data scientists. Despite having studied there was still a noticeable gap between what they had studied and what industry wanted. You can be a great data scientist, but you can't if you stay in a silo. So going to Meet-ups, reading Kaggle forums, reading recommended data science books, following technical thought leaders, can help ensure you are at least heading in the right direction. Finding an industry mentor can also be very helpful. Lastly, fall in love with it. Passion for the topic and intrinsic motivation will help you stand out from the school of fish in the market.
Metrics are important for any manager seeking to continuously improve critical work processes and the resulting work-product. That’s why DevOps leaders need DevOps metrics. With the right ones, those leaders can guide their organization’s adoption of DevOps best practices—progressively optimizing staff productivity, business agility and customer experience. But, what are the right metrics for DevOps success? And, what are the wrong ones? Useful metrics must enable DevOps leaders to make better decisions about workflows, incentives, policies, training, tools or some other “lever” of transformation.
We hear a lot about the Internet of Things, but what is the Internet of Data? When people talk about the “Internet of Data”, what they are referring to is the collection of data from edge devices and performing deep analysis of that data to gain insights. To truly enable the “Internet of Data”, machine learning and AI processing need to be moved directly to the edge. In order to do this companies need to look for solutions that can handle the entire data value chain directly on the edge and do not create throughput bottlenecks, but use a more democratized architecture.
While the progress towards smarter building infrastructure is impressive, it is important to remember that it is not without risk. Unfortunately, the diverse range of IoT systems within smart buildings are still running old, unpatched software and frequently communicate using nonstandard protocols. This makes malicious activity and potential security threats much harder to detect. Moving forward, it is imperative that the building industry and developers strictly deploy smart systems that have security built in from the start. When it comes to connectivity, the implementation of VPNs is critical for protecting smart buildings and ensuring device data is kept private and secure.
There are three foundational elements which seem to be consistently ignored in from IIoT projects themselves. One of these three foundational elements is more engaging customer experiences, higher customer lifetime value and more valued customer outcomes. The second one is to establish a target organizational end-state and a roadmap. Start with a minimum viable organization (MVO) to get started and make course corrections as needed. The third one is to ensure that valuable and measurable outcomes are not only delivered to your customers, but also to your employees, your company, and your partners.
A data scientist skills framework should take the big, messy data-scientist-by-data-scientist’s-skills matrix and try to reduce it to a few informative dimensions that minimally overlap. A skills framework establishes common ground for conversations, even when those conversations are among people of wildly diverging perspectives. A good framework doesn’t guarantee that a conversation will be productive, but a bad framework comes pretty darn close to guaranteeing that it won’t be. If we can be more clear and precise about what a data scientist needs to be able to do, we can make both groups happier than they are now.