Experfy Insights

Value based prioritisation should be at the centre of any organisation and form the building block of any agile methodology. Companies that prioritise their strategy by value adapt quicker and work iteratively to continuously deliver a more valuable product to their customer, and at a much faster rate than their competitors. Working this way ensures that every team member is directing their energies for the same goal to unlock the full potential of the business.

Women lack representation in the total blockchain cryptocurrency investors. Probable chances are their lack of interest or reach to join blogs, forums, and spaces to get information related to blockchain investments. The scenario is similar to the initial lack of women representation in all other areas like politics, technology, and entertainment and so on. It is the ongoing trend of gender inequality that is inflicted in modern brains deeply. Undoubtedly, this industry demands a transformation as soon as possible.

There is no magical technology platform or service provider which can be – on its own – the answer to a fundamental transformative challenge around cyber security. The overarching challenge for the CISO lies in getting senior management to see that long-term change is rooted in a long-term vision and long-term planning which takes time to establish. Simply throwing money at the problem in the hope of making it disappear, without a proper consideration of those matters simply leads to failure and can only aggravate the perception by senior stakeholders that security is just a cost and a burden.

By helping surface information, visualization tools create a bridge between graph data and viewers. Graph libraries are an important layer of the graph technology landscape. They let you build custom visualization application for network data and you can pick from a large catalog depending on your favorite language, license requirement, budget or project needs. These are mainly JavaScript libraries but you’ll also find other languages. This post lists libraries of various ‘depth’, some offering basic layouts and interaction, other more advanced customization and integration options.

When done well, DevOps can remove inefficiencies by improving process and performance but only when clear outcomes are established. These improvements are transferable across a wide range of industries, with consistent benefits achievable for organisations of all sizes. So if you are looking for reasons why you should introduce DevOps,here are five benefits that  help you make that decision. However, these benefits are all intrinsically linked, so there's little chance that all will immediately appear once your DevOps journey has begun. 

Sidechains are a new concept in the blockchain industry and very well in the developing mode. It’s a new blockchain developed as a separate one attached with the original blockchain system. Well, the sidechain is attached or connected with the normal blockchain, and thus two blockchain networks are now available in a single system – main blockchain & sidechain. Connected via a 2-way passage, and the main objective of this new concept is to validate the transaction without affecting the whole system.

The technology support with AutoML and deep-learning is there now, where this is a 24hr curiosity for an engineer instead of a 7 figure high-risk 12–16-month science project. As these types of problems become low hanging fruit, we will see more job disruption. At first, the jobs will be augmented/validated, and then eventually they will be automated (except appraisals that are predicted to have issues e.g. on a ski resort, etc…). In the end, there is no reason why this wouldn’t be completely automated.

DevOps has come a long way in the last 10 years. From its humble beginnings, the mash-up between development and operations has grown into a major focus for businesses all over the world. Today, more than 70 per cent of companies claim to have adopted DevOps, and it's easy to see why. Sadly, most organisations are failing to hit numbers. Here's five key ways businesses are still getting it wrong - and what they can do about it.

While the open, decentralized internet is alive and well in the InternetOne layer, the InternetTwo layer has become highly centralized, dominated by a few huge companies. Blockchain technologies have the potential to address these serious internet problems by enabling the exchange of the critical data required to validate identities in a secure, decentralized manner without the need for a central platform or other intermediaries. Over time, blockchain-based applications could be used to coordinate the self-organizing activities of large numbers of individuals and institutions in a secure and decentralized manner, as was the case with the internet’s early objectives.

While DevOps is not natural, a supernatural result is achievable with the right cultural transformation supported by good tools that drive collaboration and automated flow between people and tools in the application delivery workflow. Implementing DevOps properly is challenging. DevOps depends on collaboration; smooth process flow from conception through deployment; and feedback between people, processes and technologies. It’s more natural for people and departments to hoard information and centralize control—traits that are contrary to DevOps best practices. 

The IoT is still expanding exponentially. How will we manage and maintain the networks, data, clouds, and connections that govern and transmit this data? An IoT skills gap may be standing in our way. If your company is not yet feeling the pain of the IoT skills gap, take heart: you soon will. The following are some tips for lessening the damage to your business. The easiest way to deal with the IoT skills gap in your workforce is upskilling and retraining programs.

RPA offers retailers the opportunity to automate processes and data management today, while simultaneously positioning them for the future. Early adopters are already reaping the benefits. Those retailers still uncertain of the opportunities that RPA can bring will be assured by the fact that RPA can provide more opportunities than threats for retail workforces. It’s a no-brainer for businesses, too, as the benefits of RPA easily transcend headcount and cost reduction. It’s time for retailers to welcome the (software) robots.

Every business needs to stay up to date on and comply with the latest encryption and privacy laws. Failure to comply will result in fines that can range upwards of tens of millions of dollars. But which laws do you need to comply with, and what do you have to do? For the sake of this article, we’re just going to focus on the regulations and laws that require encryption or reference the protection of encrypted data. These regulations and laws are sometimes called data encryption laws, data privacy laws or data protection laws. 

Machine learning is not new, but there is a new paradigm to do it, and maybe it’s the future of the field. Inside of the data fabric, we have new concepts like ontology, semantics, layers, knowledge-graph, etc; but all of those can improve the way we think about and do machine learning. In this paradigm, we discover hidden insights in the data fabric by using algorithms that are able to find those insights without being specifically programmed for that.

Machine learning (ML) has a surprise, too. One of the biggest misconceptions about ML deployment within organizations is comprehending the difficulty and the value. Integrating ML into your business workflows can be broken down into five activities. Optimizing an ML algorithm takes much less relative effort, but collecting data, building infrastructure, and integration each take much more work. The differences between expectations and reality are profound. Not every problem has an ML-powered solution, but many do, and even those that do not will benefit from this journey.

AI-based platforms are quantifying and getting value from you by gathering your contacts, your ideas, your skills, your free time, your shopping habits and even your very genes. The tradeoffs here are not so clear. Companies win big using your data. So, on top of worrying about whether or not AI will steal your job, you must also worry about AI stealing your time, information, ideas, relationships, and more. AI is breaking us down into our piece parts, and using them up.

SQL is a relational database management system used for storing, retrieving, updating and reading data from the database. As long as there is ‘data’ in data scientist, Structured Query Language, or see-quel, as we call it will remain an important part of it. You should understand that if you are crazy about data and playing with it, and want Data Science as your career choice, you should definitely learn SQL. Let us explore data science and its relationship with SQL.

As every enterprise is becoming more and more data-driven, it is key for the Board to realize that cyber security is becoming a central tenet both of its core business and of its social impact and governance strategies. This should the basis on which the cyber security imperative is cemented at Board level. Right where it always belonged. Here are Key factors for boards and executive management to consider in 2019 around cyber security and privacy.

We must recognize that artificial intelligence is a fluid term, whose definition changes with time. Therefore, we need to define what is the current context of AI. Can we consider anything that uses a machine learning algorithm as artificial intelligence? Should AI be limited to systems that employ neural networks and deep learning algorithms? Or maybe we should evaluate AI based on the cognitive behavior a system manifests, regardless of the underlying technology? If yes, what is the minimum level of cognitive accomplishment for a system to be considered AI?

There are many different basic sorting algorithms. Some perform faster and use less memory than others. Some are better suited to big data and some work better if the data are arranged in certain ways. Choosing which library and type of sorting algorithm to use can be tricky. Implementations change quickly. In this article, I’ll give you the lay of the land, provide tips to help you remember the methods and share the results of a speed test.

It is important to know which activation functions to use within your neural network. Be aware of the fact that you can use different activation functions at different layers. Most often the sigmoid function is used but often other functions can work much better. In this post you will learn the most common Activation Functions within Deep Learning and when you should use them. You will also discover why you mostly need to use non-linear activation functions.

Cyberattacks to disrupt the business are now ranked as the third-biggest threat, after phishing and malware. This comes as no surprise because distributed denial-of-service (DDoS) attacks, for instance, can trigger a major service interruption that will bring the business to a standstill. Outages have always been painful but given the trend toward moving workloads and applications off-premises, and operating revenue-critical platforms, business operations virtually come to a stop if the IP network collapses.

Generally, AR and VR are utilized in industry sectors such as finance, healthcare, construction, and retail. But, can augmented reality, along with robotics and virtual reality work together? Yes! Together, augmented reality, robotics, and virtual reality can be the ‘Three Amigos.’ VR and AR can offer an immersive medium to operate robots. With the help of low-latency networks, people can utilize robots remotely using intuitive AR and VR controls. Augmented reality, robotics, and virtual reality can be used together in various industry sectors such as manufacturing, healthcare, and private space research. 

Machine learning is one of the hottest topics in technology today. Parallel to the success of learning algorithms, the development of quantum computing hardware has accelerated over the last few years. In fact, we are at the threshold of achieving a quantum advantage, that is, a speed or other performance boost over classical algorithms, in certain specific application areas – machine learning being one of them. This sounds exciting, but don’t ditch your GPU cluster just yet; quantum computers and your parallel processing units solve different classes of problems.