Big Data, Cloud & DevOps

Digital transformation is human transformation and that’s where you need to start. The first step towards a successful digital transformation is not the technology itself, but thinking about how you can empower your people through it. Where do you expect value to shift to? What new skills will your people need to learn in order to succeed? How can technology help them get where they need to be to serve your customers well?

It is crucial for the companies to give a cultural work environment to its employees if the employers want to increase the total output productivity. The culture-building process cannot simply be effective enough for implementation without the use of technology. Technology and culture building are two aspects that can be integrated into the immense growth of the business. In other words, building a culture at work is impossible without the integration of technology.

Data Science is an exciting job, but it can be very difficult to perform if you speak to a non-technical audience. Data and business are intimately related to each other and you must remember this point when you work with business-oriented people. The only way to survive is to find a middle point between a data-driven bottom-up approach and a business-driven top-down approach.  Finally, as Data Science is hard and time-consuming, delivering small results with a constant delivery rate is the only way you can keep your customers engaged.

The term digital transformation is often used in relation to IT transformation or business transformation but even though they are closely related, the terms are distinctly different. But digital transformation does not really refer to IT systems but rather to an organisation’s underlying business and its business processes going digital and becoming more agile. Digital transformation will also require the use of various technologies such as cloud computing, business analytics (BA), artificial intelligence (AI) and machine learning (ML) and, going forward, the Internet of Things. Here are five digital trends to note.

This article familiarizes you with data storage in Docker.  There are many ways to save data with Docker. Data in Docker can either be temporary or persistent. Data can be kept temporarily inside a Docker container in two ways. Many times you will want your data to exist even after the container is long gone. Many times you will want your data to exist even after the container is long gone. You need to persist your data.

As for dark data, it’s all the information companies collect in their regular business processes, don’t use, have no plans to use, but will never throw out. Its web logs, visitor tracking data, surveillance footage, email correspondences from past employees, and so much more. While dark data may never be used or be useful for many organizations, it’s something that should not be ignored. Then what are some of the best practices with dark data? What can be done to get the most value from it?

Recall that a Docker image is made of a Dockerfile + any necessary dependencies. Also recall that a Docker container is a Docker image brought to life. To work with Docker commands, you first need to know whether you’re dealing with an image or a container. Once you know what you’re working with you can find the right command for the job. This article highlights the key commands for running vanilla Docker. Here are a few things to know about Docker commands.

HR representatives can dig into data to tackle their needs. Doing so can help their companies and employees thrive in an ever-demanding landscape. HR data can help companies figure out what factors make employees most likely to quit. It can also pinpoint which workers might be feeling upset about their roles, allowing HR representatives to intervene proactively. HR people often use technologies to impact their work, big data among them. Here are six ways big data in HR has had an impact.

Digital transformation is bringing fundamental change to businesses across all sectors. Business leaders and IT professionals know tsunami of data is here, but many don’t yet have a comprehensive strategy to integrate it all, so they rely on stop-gap measures. Four data myths in particular hold companies back from creating an effective, long-term data integration strategy. Here’s a brief overview of each myth — and an explanation of why it’s leading businesses astray.

Facial recognition holds so much more potential. Massive opportunity lies in facial recognition software, from helping to identify missing or exploited children to speeding up lines in the airport. As marketers trying to leverage facial recognition as a vehicle to improving customer experience, is there still a way for us to use the technology? How can we tap into the benefits without crossing the line of personal privacy? Before the technology is unleashed into the corporate world, companies need to fully understand it. 

Marketers use big data to sell more products. Big data marketing, however, analysing data to improve marketing activities isn’t about more ads. It’s about serving the right ads to the right people at the right time. It’s about answering questions regarding what type of message resonates with customers, which landing page is the most efficient, or which social media platform should the company use to reach their target audience. Without big data, companies operate on unproven assumptions, which is never a good idea. That’s why businesses use big data marketing to solve puzzling issues.