Big Data, Cloud & DevOps

The front end of a networked system is the side that a user, client, or customer sees. The back end is everything that happens behind the scenes that is not necessarily seen by the user. Access to networked services may be through a front end that is a web-based interface, a website, a mobile app,

Small businesses are a favorite target of cyber criminals — cyber attacks were up 424% in 2018 You may have heard the oft-quoted small business cyber security statistic that’s something akin to “60% of small companies that suffer a cyber attack are out of business within six months.” Heck, like many major media outlets, we’ve

This post is part of a series of articles about the GraphTech ecosystem. This is the second part. It covers the graph analytics landscape. The first part was about graph databases and the third part will list the existing graph visualization tools. Actors of the graph analytics landscape in 2019 This second layer is another back-end

One of the most often repeated episodes in the history of technology is when Steve Jobs was recruiting John Sculley from his lofty position as CEO at Pepsi to come to Apple. “Do you want to sell sugar water for the rest of your life,” Jobs asked, “or do you want to come with me and change the world?”It’s

Let’s continue our focus on Data Science methodologies. The reason for this focus is the need for more methodical delivery by many Data Science teams. In the first post of this series, I made the case for having a Data Science methodology and shared 3 popular options. I hope you found those useful, but I’m also conscious that they

In this post, we are focusing on data analysis and visualizations, which do not come from the devil, as many leaders of organizations think. We believe that data analysis and visualization can be understandable, discoverable, and manageable for the average person. Why Do People Not Trust Computer Analyses and Visualizations? The question is not that

Big data has already revolutionized several businesses across the globe. Big data technologies have been helping marketing and sales professionals better define products and services and managing sales network. According to Forbes, the impact of big data has been about 48% on the new age customer analytics, 21% on operational analytics, 12% on compliance/fraud, 10% on new product/service innovation, 10% on enterprise

Another tip for success with your Data Science team is to be more methodical.By this, I mean to establish and use a consistent methodology, process or workflow. This will enable repeatable results, simpler collaboration & knowledge transfer. If it is a well–designed methodology, it should also ensure appropriate QA stages and reduce the cost of rework.A few different influences have

Excessive complexity and lack of first line integration render many GRC metrics useless Many CISOs complain of communication problems with their business. They are not being listened to. They are not getting the budget they think they should get. They feel their business prioritises against security too often.It has been a recurring theme amongst information

Without good design practices, a DevOps implementation has no hope of delivering on its promise of accelerating innovation with high quality and scale.Anyone who has been a designer knows that good design for DevOps is easier said than done. My blog, “7 Pillars of DevOps,” identified seven essential best practices areas that make up successful DevOps. One of

Marketing plays a significant role in the success of any business, that offers a relationship between customer and organisation. Hence, it is very important to have strong marketing strategies to understand the market, competitors, customers and more. For that, companies need to focus on big data. “Marketing without data is like driving with your eyes closed.“ –