Big Data, Cloud & DevOps

IT discovery is the foundation to your IT asset management (ITAM) solution. If discovery is unreliable, then all of the asset information you are trying collect will not be reliable. Don’t let IT asset discovery become a stumbling block to your ITAM solution. Be sure to have clear objectives and a clear vision of the reports that will be needed to support those objectives. Set your discovery tools to discover and monitor assets relevant to your objectives. Most important, don’t overwhelm your IT employees with unnecessary discovery information, especially during the early phases of the project.

Going forward, access to data and the ability to derive new risk-related insights from it will be a key factor for competitiveness in the insurance industry. New approaches to encourage prudent behavior can be envisaged through Big Data, thus new technologies allow the role of insurance to evolve from pure risk protection towards risk prediction and prevention. Using Big Data analytics, insurance can offer personalized policies, precisely assess risks, prevent fraudulent activities, and increase the efficiency of internal processes. Let’s take a closer look at several Big Data solutions for insurance.

When it turns out that the decisions that are based on a model are bad, it evokes a few obvious questions: Is my model broken? Is my data pipeline broken? Has the thing I’m modeling changed? Answering these questions should be no harder than looking at a dashboard. People tend to rebuild from scratch instead of reverse engineer. Here are the 5 important things that will keep your work robust and relevant, while saving you lots of time that would otherwise be wasted on unnecessary operational firefighting.

The term Big Data has been around 2005, when it was launched by O’Reilly Media in 2005.  However, the usage of Big Data and the need to understand all available data has been around much longer. While it looks like Big Data is around for a long time already, in fact Big Data is as far as the internet was in 1993. The large Big Data revolution is still ahead of us so a lot will change in the coming years. Let the Big Data era begin!

The old way of doing system architecture will not disappear entirely, but it is already past time we started thinking about how to improve the efficiency of our system architecture practices so they better support today’s rapidly evolving business climate. The next major effect of networks on the evolution of system architecture was the desire to integrate systems. It did not take long to realize that entering the same data into different systems was time-consuming and error-prone, so we began to try and integrate systems so they could share data. 

Increased access to vast amounts of information, social media, and ubiquitous computing makes it seem as if the complexity of our world is increasing faster than we can comprehend it. But technology is not driving complexity; it is only making complexity more visible. Trying to eliminate complexity is an impossible task, and traditional approaches to enterprise architecture have proven ineffective in dealing with it. By thinking about enterprise architecture in a new way, we can make that complexity work for us, and harness emergent behaviors to help achieve an organization’s goals.

IT as a whole has really moved from the things that help the business do day-to-day jobs, to becoming the engine that actually drives the organization. Often, new products and services can’t be launched without IT, and it’s also becoming the point that IT is the product or service being launched. Nearly two-thirds of CIOs say that driving revenue through the creation of new products and services is among their responsibilities today. CIOs offer seven steps to shifting to revenue-driven IT.

In recent years, the development of massive computing and storing capacities in the hand of a few internet juggernauts led to the rise of the cloud economy. Companies of all sizes have been moving their mission-critical servers and operations to the data centers. On the face of it, the development of Infrastructure as a Service (IaaS) should be good news for the state of cybersecurity. In this context, it is easy to believe that moving to the cloud could mean solving many of your cybersecurity issues.

Here is advice on getting started with doing data analysis in Python and I thought it might benefit others if published here. This is for someone new to Python that’s looking for the easiest path from zero to one. Here’s a quick summary of the important libraries you’ll interact with frequently. You will likely always need to refer to the documentation for whatever library you’re using, so just keep it open in your browser.

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.

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.