One of the main problems with organizations attempting digital transformation is an embedded complexity in their processes. This complexity has usually arisen from being product-focused rather than customer-focused. While tackling the process innovation, it is not something that should be delayed. With two-speed IT, one now has to introduce a whole new IT model for the agile development, which includes more new processes, instead of striving for simplicity. The short-term goal of IT business units should be to move to the agile philosophy, which is a milestone on the roadmap to continuous delivery and implementing DevOps.
Data is slowly replacing experience and traditions in the way companies do business. It has already proven its value in different verticals, including finance, healthcare, and of course, retail. The first obstacle is to define the scope of the Big Data project. What are the most critical questions the company needs to answer? What data sources should they analyze? Are these already available? Is the data clean and reliable? While in the initial implementation phase Big Data-related modifications can affect usual workflows and slow down business, the opposite happens after they are set in place.
By harnessing data science to its full potential, top-ranking decision makers in all industries, not only make better-informed decisions but make them with clearer predictions of the future. With that advantage on their side, they are able to stabilize businesses that have not always had a clear vision and save businesses that are on the brink of collapse. Once goals have been established, data scientists can work their magic and theorize how to fix it. Data science alone is not an advantage for decision-making, data science combined with good leadership is.
Artificial intelligence is an incredibly complicated concept for application testing. There aren’t that many products that offer real AI/machine learning functionality for app QA. Your best bet is to find a QA team that has in-house machine learning solutions or uses one of the tools that we mentioned and their alternatives. This way, your app testing needs will get the maximum coverage that they deserve. It’s also important to remember that traditional QA automation still works. You don’t have to jump on the AI bandwagon just because everyone is using it in their marketing nowadays.
How to use an intranet to engage employees? A corporate intranet can help win employees’ loyalty, and HR managers should always be on the lookout for ways to improve it. A finely tuned intranet can help your employees find the information they are looking for easily, follow their activities and performance, stay tuned into corporate life and keep connected to their peers, thus getting support from the professional community and feeling more satisfied with the working process. Read this article to learn how to use an intranet to engage employees.
Artificial intelligence, machine learning, natural language processing, sentiment analysis and more are just a few of the techniques which are generically called Data Science. The real question is: what is the best choice for your company regarding these services? Should you train your existing staff, hire data scientists or outsource to a professional organization? There is no single correct answer to these questions, and each entity should start with an evaluation of their expectations and needs. In this article we’ll provide some guidelines to facilitate this decision.
Predictive analytics, as an evolving domain based on Big Data and AI, requires a considerable amount of information for training the model. Sometimes, locally, this is scarce or unscalable, but imagine putting together data from thousands of similar users. Suddenly, the lack of data is no longer a problem and patterns emerge more easily. This gives a real chance to smaller companies, like start-ups, to take advantage of the blockchain model for their operations and use the data generated in the process as a by-product to feed various prediction models.
Cyber Risk is recognized as a major threat by both insurers and their clients. Is there any proven way to manage this risk efficiently? Cyber insurance clients will have to beef up their cyber risk strategy, if they have one, and make sure that they are constantly up to date with the latest software, firmware, and hardware fixes, if possible. They must also train up employees to understand cyber risk. There is potential for insurtechs and development houses who can consult small and medium businesses on this; not everyone can afford to hire a CISO.Insurers will need to develop expertise in cyber insurance and start gathering their own cyber risk data. Insurers must also ensure that their own house is in order, as they are a good target for data breaches.