It should come as no surprise that large tech enterprises are readily embracing AI as a means of improving the way they operate and their ability to engage with clients. However, for smaller organisaitons and companies that are less tech savvy, the ability to identify and implement AI can be overwhelming and complicated in equal measure. What are the main barriers stopping organisations – particularly those outside of the tech sector – from taking advantage of this powerful technology?
In order to disrupt business, machine learning models must adopt a product-focused approach, which is a much more significant undertaking. For a product-driven approach to use machine learning, it is important to think about the problem you are trying to solve from the beginning and to have some initial idea of how the machine learning solution might be used. The first step is to understand what pain points you are trying to tackle, and what kind of service-level agreement in terms of quality, availability and responsibility you need.
IoT is here now. With mobile data traffic up 82 percent year-on-year and 5G uptake going even faster than anticipated, we can expect cellular IoT connections to follow suit. How do we move from wired to wireless networks to capture the promise of trillions of dollars in value that industrial IoT (IIoT) will bring? We only need to look at the history of the wireless networks to know how to deploy the network of the future.
In this short article, a simple example of the use of Azure ML studio is shown. It’s a very useful tool in the machine learning industry and, although it has some limits with limited number of records, limited choice of models. Even the most code-oriented data scientist will love this simple tool. It’s pretty worth mentioning that, paying the appropriate fee, ML studio can be used for real-time training and prediction thanks to its strong REST API interface. This enables many possible machine learning scenarios.
The future, in fact, will be driven by humans collaborating with other humans to design work for machines to create value for other humans. Any viable cognitive strategy is not to cut costs but to extend capabilities. So perhaps most importantly, what business leaders need to understand about artificial intelligence is that it is not inherently utopian or apocalyptic, but a business tool. Much like any other business tool its performance is largely dependent on context and it is a leader’s job to help create that context.
To take advantage of machine learning, businesses will need to do one of two things: Invest a lot of resources in data scientists or developers with a background in machine learning or utilize machine learning as a service (MLaaS) offerings. The latter option can be much more cost-effective for a business that may not have the luxury of hiring ultra-skilled employees. In 2019, MLaaS will become mainstream. The technology behind MLaaS will become prominent in 2019 with the help of the cloud giants, media and consulting partnership opportunities.
While machine learning and other AI techniques will help improve the speed and quality of cybersecurity solutions, they will not be a replacement for many of the basic practices that companies often neglect. In cybersecurity today, we overestimate the capacities of machine learning. When talking about AI, many people have this illusion that they can just plug in software or hardware that is leveraging AI, and it will solve all their problems. It will not.
The emerging business process automation tool, RPA, is programmed such that it can deal with high-volume transactional tasks, invoice processing, email communication, and other back-office processes. The data that RPA deals with can or cannot be sensitive. The automation tool can even comprise of vital data like credentials, employee details, or customer information. What if hackers access the application platform, implant malicious code, and alter the rule-based processes? Well, if this happens, then businesses will face dire consequences. Let us now check the possible security risks in RPA.
According to Pareto Principle, also known as the 80/20 rule, only a few decisions create the bulk of corporate value. The question is: do you know which decisions are important in today’s digital and platform economy? Do you and your board know the vital few decisions that will truly determine whether your business is bad, good or great in age of technology, platforms, networks and machine learning? And if you do, how much capital and leadership effort is being devoted to the most important 20% versus the remaining 80%?
The market shares of graph databases keep increasing, as well as the number of products on the market, with seven times more vendors than 5 years ago. Although it seems difficult to agree on exact figures, all reports identify the same growth drivers. In this article, I present the current market, if not exhaustively, at least as well as possible. I divided the graph ecosystem into three main layers, even though the reality is more complex and these stratum are often permeable.
Financial services firms will have to fundamentally reconsider how humans and machines interact, both within their firms and with customers, if they want to take advantage of artificial intelligence technology. To get a new AI program off to a successful start, firms should be bold in their vision, generate confidence from management with early success with easily achievable projects, emphasize organization-wide implementation of the technology and adopt governance structures such as cybersecurity and compliance to scale along with AI.
The data industry has grown rapidly and will continue to grow for many years into the future. As with many industries that experience growth in this sort of manner, there are always new trends coming out to help companies manage their databases and the data held inside of them. These trends are meant to make data and database management more efficient, effective and streamlined. Without any further ado, this article is going to look at some of the biggest trends in data management and database management.
As technology continues to disrupt how and where we work, it will keep finding ways to remove monotonous tasks from our plates. It’s not about being lazy. The adage about most car accidents happening within five minutes of home applies to our work just as well: Errors occur during familiar, repetitive tasks. The point of employing human beings in the first place is to leverage their talents and creative drives. With that in mind, companies should consider automating back-office functions.
Today, a new debate is likely to begin over artificial intelligence. Much like in the early 1970s, we have increasing investment in a new technology, diminished productivity growth and “experts” predicting massive worker displacement. Yet now we have history and experience to guide us and can avoid making the same mistakes. Investment in digital technology in the 70s and 80s was focused on supporting existing business models. It wasn’t until the late 90s that we began to see significant new business models being created.
Today we have Artificial Intelligence (AI) to help you search for a needle in the haystack. Using the power of AI to supplement (not replace) the human judgment can make the Talent Acquisition process more effective. Making an informed choice can be the first step in having happy employees. Using AI for sourcing, screening and setting up interviews can be great ways to improve the candidate experience. Here are some things that will improve the quality of your hires.
Over the next two years, say some estimates, around 60% of companies in the business-to-business sector will begin redesigning their communication models around machine learning, AI, and “human augmentation.” These things already help power much of what we’ve been discussing, from helping a PR specialist find emails quickly using natural language to automatically sending alerts when it’s time to see if a client needs to stock up again. Whatever form communication takes at your company, there’s probably an exciting technology to help you do it better.
Identity access management will continue to grow in scope and scale. Biometrics may be useful; however, it should not be solely relied upon for identification. Blockchain technology may be a better choice for those who want to control their identity. Ease-of-use for cloud-based offerings is driving the demand for single sign-on services. Expansion of the IoT requires scalable and reliable infrastructure to establish the identities of the billions of new IoT devices and manage them via a massive network.
The technology behind blockchain is binary. A combination of network, cryptography, computing power and consensus algorithms to create a decentralized, immutable ledger that can be leveraged for smart contracts. A mathematical approach for creating something that is not mathematical at all, a distributed system of trust. With blockchain technology, we can gain the benefits of trust without the drawbacks of trusting. It will impact every system which requires authorities and intermediaries to function. And ultimately, it will transform the way we function and collaborate as a society.
One of the biggest ways the food industry has changed over recent decades is by adopting new advances in technology. The food industry used to be riskier for everyone involved. Crops could go bad if someone slacked off on the job and there used to be no choice but to waste food every night. Now, technology changes these issues and presents modern solutions to food industry experts. That's why it's been so quickly embraced by all involved, resulting in a more efficient and profitable industry.
With the rise of certain new technologies, the business process outsourcing (BPO) industry could be at risk of losing a large portion of its market share. As it turns out, it’s not just retailers that need to work hard to stay competitive, and BPO companies may find that 2019 could be a painful year for them if they are not keeping up with the industry’s latest trends. Here are top four business processes outsourcing trends to expect in 2019.
Like their for-profit peers, a growing number of nonprofit executives recognize that big data can help organizations achieve mission-critical objectives. a growing number of nonprofit leaders are taking advantage of data analysis to learn more about potential donors and promote important societal causes. Also, data analysis enables nonprofit leaders to discover new opportunities to make a difference. Nonprofit organizations now use artificial intelligence to search through many different kinds of information sources. They are searching for ways to use artificial intelligence to analyze constituent feedback and find ways to help in real-time.
Software-defined storage (SDS) hits all the right enterprise storage trends, but it’s still an emerging technology for the average organization. Therefore, outsourcing SDS to a hyperscale or other CSP is probably the best option for maintaining your data safety. With all of its appeal, some people and enterprises are wondering how safe their data are in an SDS solution. Here are some things to consider if you’re trying to answer that question for your own data.
There are lots of ways for data to inform and streamline business today. But the real potential isn’t evident until you dive into your teams’ daily responsibilities and learn more about how they work and how data can help them do a better version of it. Let’s look at what a data-driven team can look like in multiple contexts as well as some of the steps required to build that kind of environment.
Companies are increasingly leaning towards well-governed, self-service BI models that combine the necessary speed and flexibility. Both the self-service model and governed models are distinct entities. In the self-service model, the organization gets direct access to complex technical tools and datasets on-premise. They use these tools to extract data, creating reports and gleaning information from them. With the governed model, the data is fed to an IT team where they construct data pipelines from the data source and put it all into a central data warehouse.