Every organization has realized that data is an asset and utilizes crucial data in multiple applications. In data monetization, blockchain can be a major contributor due to its advanced applications and decentralized nature. With the help of blockchain, data monetization will be accessible to consumers soon. Using blockchain-based data monetization, consumers can monetize and negotiate the value of their data. Also, consumers can control which data can be collected by organizations to ensure data security and privacy.
The IOT has taken on a greater level of importance in a wide range of industries. Those who have been monitoring these effects have been paying close attention to the world of healthcare. The risks that are associated with providing care are reduced and so are the costs. The IOT into the medical sector has been a boon for all parties involved. App development companies and businesses have already been making major strides in this regard. Elderly patients and those who require around the clock supervision are benefiting immensely.
Despite a great deal of lip service and a small amount of capital invested, most corporations are still not data-driven, nor do they use machine learning (ML) and artificial intelligence (AI) to guide their strategic investments in business models. Companies are finally embracing analytics, but still have shown little appetite to be data driven, let alone use ML and AI to help them understand the key drivers of value in today’s highly competitive environment: capital allocation and business model design.
Many organizations are realizing the value of their data so they are beginning to treat their data as a company asset; hence, the rise of the Chief Data Officer (CDO). Data provided from IT service management reports and metrics will be vital information for the CDO as he/she defines strategy for new technology, process, policy, security, and IT architecture. ITSM managers should expect the CDO role to have a direct impact on how IT service management will be implemented, delivered, measured, and most importantly, integrated with other IT solutions within the organization.
Creating the right groundwork for automation or bots or Robotic Process Automation (RPA) or whatever name you want to call it is essential if you do not want to regret the outcome later. The terms have been used interchangeably in the article below. No automation is inexpensive; this article serves as a helpful checklist as you plan the rollout of your automation project or work out what went wrong. Three prerequisites focus on process side of automation which at times get overlooked during the planning phase.
Time Series Forecasting is an important area of machine learning with the goal to predict things that involve a time component. It is often neglected because the involved time component makes it a bit harder. It basically allows us to forecast any variable that can be tracked and collected over time. Examples are a stocks closing price, annual population data or sales figures. A Time Series Forecasting model is just using the collected data to forecast future values.
The vast amount of data generated and to be processed in connected devices through internet of things is expected to be cumbersome. Artificial intelligence based solutions will make IoT more efficient, thus planting the seed for the global AI in IoT market. The coming years are expected to be the turning years for this market. The AI in IoT market will be influenced by AI’s capacity to provide tools and frameworks for automating processes and real time decisions.
Thanks to advancements in computing, many businesses look to integrate new technologies to their work as part of their digital adoption strategies. Digital adoption is a must for any organisation today. Your choice of tools and solutions providers could very well determine on the success of your effort. Having a solid strategy in deciding which tools to adopt is vital. Don’t be afraid to pivot and switch directions and look for better solutions. Successful digital adoption relies on building a culture that embraces change and continuous improvement.
The best way to get buy-in from any stakeholder is to get them to join you in the ceremonies and participate in the day-to-day Scrum execution. Organised presentations and training is also essential to create a feeling that the transition to Agile in the best approach. On the other hand, the need to foster the Agile atmosphere and prove that it is not FrAgile, does not give you carte blanche to ignore the feedback of those stakeholders who do not necessarily see the value.
Whether you represent a brand-new startup or you've been in the game a while and your company is just ready for a change, choosing a new IT vendor is a decision you'll want to make in equal parts on merit, culture, and personality. Here's what to look for and how to find it. Does the company you're looking at support a wide variety of technologies and brands, or only one or two? Do they provide other managed services you might want to branch out with in the future, or do they just do one or two things well?
Some of the excitement around RPA is just plain hype, while a bit of it has some substance. RPA has potential – but it does not fix bad processes. Before automating small parts of processes it makes sense to see the big picture. For optimal results leaders need to think about is how an organization’s end to end processes are performing – for both customers and the company – and where RPA may provide the greatest value. That’s how organizations can put process back into RPA.
Few organisations realise that almost 80% of what they store is 'dark data', or data they do not even know exists. Dark data is all of the information collected and stored by a digitalised organisation that is not maintained or monitored. To effectively manage and control data, organisations should set up a single location where all its data assets can be discovered, classified and categorised. This is all part of proper data management, which is not only important for compliance and security, it can also lead to better business decisions.
Workplace safety is a tremendous responsibility. Keeping personnel and assets safe is becoming an increasingly manageable challenge, thanks to modern technologies — including big data and predictive analytical tools. Physical and stress-related health and safety issues are common in offices and warehouses alike. What are some of the ways technology can help keep us safer and better attuned to workplace risks? Companies can use predictive modeling to learn where injuries tend to occur and are likely to happen in the future.
AI will enable organisations to leverage data and embed smartness in every process and customer touchpoint. When you put smartness to work, it will empower your employees and customers and make your organisation more humane. In today’s organisations, a lot of employees have to deal with a lot of administrative tasks and bureaucratic processes. However, in the organisation of tomorrow, such tasks and processes will be managed by AI. Within the organisation of tomorrow, humans and AI will work together.
Analytics projects fail not because the solution doesn't work, but because the business fails to realise value from its investment, or the technology is not used at all. The cost of this failure is enormous. The first step towards having analytics take its rightful place in the organisation is for data to be regarded as an asset, on par with every other asset owned by the business. There are seven key factors that can mean the difference between an analytics project succeeding, or adding to the high statistic of big data project failures.
Auditing might sound like a stuffy, old-fashioned concept. But that couldn't be further from the truth now that modern technologies are bringing this industry into the 21st century. The role of the auditor will start to look a little different as these technologies mature further. Their roles will have to gravitate more toward that of analyst or data scientist. They'll also need to be more than conversant in cybersecurity and other types of modern threats that might be relevant to auditing.
The Internet of Things involves the digitization of physical assets, and that includes pretty much every company. And just like other markets, some of the largest market participants will assume that they have plenty of time to respond, or that they are too big and too entrenched for the change to significantly impact their businesses. But they will be wrong. But how can you tell if you are going to be a digital winner? What are some of the signs that you are falling behind and possibly falling into the loser category?
Thanks to advances in deep learning, AI algorithms have become capable to automate text-related tasks that previously required the skills of human operators. Many companies completely rely on AI algorithms to process text content and make important decisions. But deep learning algorithms are also vulnerable to their own unique type of security threats. With AI becoming more and more prominent in tasks such as filtering spam, detecting fake news, processing resumes and analyzing the sentiment of social media posts, it’s important that we understand what these threats are and find ways to deal with them.
Your salespeople provide the humanistic touch needed to close sales and maintain customer relationships. But if they're not using analytics and AI tools these days, they're working harder than they need to. Consider the benefit of being able to engage in more realistic company operations planning, courtesy of intelligent sales forecasting. Having a way to more accurately predict next quarter's or next year's revenue based on your historical and real-time sales team performance goes a long way in mapping out and prioritizing major spending and acquisition decisions, too.
With RPA, there is a lot of focus on the tech and efficiency savings, and little consideration is given to the people whose lives would be affected by the changes. There must be a way to achieve efficiencies with RPA and to look after the people within an organisation. Organisations need to stop viewing RPA as a point solution to deliver singular short-term savings, and instead look at how automation affects the entire organisation. Organisations can get so much more out of RPA if they connect the dots, focusing on the humans that are affected by RPA as well as technology.
Despite rigorous research and aggressive adoption, IoT infrastructure faces major security issues. Hence, several researchers and developers are exploring enhanced security protocols provided by blockchain. Blockchain has already introduced a secure platform for cryptocurrency transactions. Similarly, the utilization of blockchain in IoT will lead to the development of distributed ledger for interfacing multiple connected IoT devices. With such an approach, data storage and networking of IoT-powered devices will be drastically improved.
Regardless of industry, companies all over the world are shifting to new business models based on technology and platforms, rather than the products and services of the industrial age—and those that make this shift and leap the digital divide are rewarded with dramatically higher market valuations and corresponding price-to-sales ratios. If you want to know what the market really thinks about a company, there’s one pretty simple way to tell: just look at its price to sales ratio. This one little number encapsulates performance, value, and trajectory, and it’s a lot harder to manipulate than price to earnings ratio.
Traditionally, pipelines involve overnight batch processing, i.e. collecting data, sending it through an enterprise message bus and processing it to provide pre-calculated results and guidance for the next day’s operations. Whilst this works in some industries, it is really insufficient in others, and especially when it comes to ML applications. When developing a model, data scientists work in some development environment tailored for Statistics and Machine Learning (Python, R etc) and are able to train and test models all in one ‘sandboxed’ environment while writing relatively little code.
A change management process that requires software license contracts to be considered for every change request can help organizations avoid unnecessary costs resulting from a software audit. However, an inexperienced license expert on the CAB might approve changes that could incur more costs simply because they do not understand their software license agreements and the impact of the RFC. Be sure to hire or partner with the expertise required for evaluating software license contracts.