Big Data, Cloud & DevOps

Organizations across industries are adopting graph analytics to reinforce their anti-fraud programs. In this post, we examine three types of fraud graph analytics can help investigators combat: insurance fraud, credit card fraud, VAT fraud.   Detecting fraud is about connecting the dots > In many areas, fraud investigators have at their disposal large datasets in

In 2012, Harvard Business Review announced that Data Science will be the sexiest job of the 21st Century. Since then, the hype around data science has only grown. Recent reports have shown that demand for data scientists far exceeds the supply. However, the reality is most of these jobs are for those who already have experience. Entry

What is clear is that data science is solving problems. Data is everywhere, and the uses we are making out of it (science) are increasing and impacting society more and more. Let’s focus on Data Science. Let’s start by taking a look at what has happened in 2018 and then focus on hot topics for 2019. In 2018, the main developments included automation of workflows, explainability, fairness, commoditization of data science and improvements in feature engineering/cleaning tools. And they will continue to be some of the main focus of data scientists in 2019, and the following years. 

Traditional cloud computing architectures need to evolve to a more decentralized approach that processes data at or near the source. Not only could edge computing provide this capability, but it has the potential to increase data computing efficiencies. While traditional data centers will remain the core computing power for enterprises, we’ll begin to see edge computing technology become integral into data center strategies in 2019. Whether this means integrating a solution into current operations or small data centers built for edge analytics, the next year will be the year enterprises live on the “edge.”

Problems are only challenges if met with the right mindset and the tools with which to overcome them. AI and Big Data have become a powerful combination that effectively changes the way industries view daily operations. Whether it relates to enhancing the customer experience or developing completely new products to market, the basic value-adding proposition remains the same. AI is here, and it’s here to stay. How it is used to add value is yet to be fully discovered; hopefully, these provide a few ideas with which to build on.

A common fallacy exists for people building data science teams that: smart hires translate to successful data science teams. What are the number one reasons you think smart data science teams fail to offer business value? The number one reason smart data science teams fail to win and provide value at the rate that they should is money. Sure you pay them well, but they just don’t get the business drivers. They can’t speak the language your board members, managers, and customers need to hear. Despite their data genius, they are idiots in the business world.

In startup lingo, a “vanity metric” is a number that companies keep track of in order to convince the world — and sometimes themselves — that they’re doing better than they actually are. Vanity metrics are everywhere, and they can really hold us back when we optimize for them, rather that optimizing for something that matters. They cause us to spin our wheels, and not understand why our hard work isn’t leading to results.

DevOps is a new buzzword in computing circles. It encompasses many common sense ideas about the integration between business and technology and provides the narrative to bring development, delivery and operations together. DevOps is the practice of operations and development engineers participating together through the entire service lifecycle, from the design and development process all the way to production support. It replaces the traditional silo setup where you have a team that writes the code, another team to test it, yet another team to deploy it, and even another team yet to operate it.

When it comes to bringing innovation to the world of banking and finance, what sort of apps might we see in years to come, and what areas are potentially ripe for development? What is driving the development of new types of apps, and how will gadgets and voice-activated assistants such as Facebook Portal, Alexa and Google Home play their part? It’s only a matter of time before home assistants are well ingrained in the Fintech development landscape.

The tech sector continues to prioritize cybersecurity innovation and investments. Digital diplomacy will represent an important topic of discussion in tech in 2019. Artificial intelligence and machine learning represent one of the most exciting trends in technology: virtual assistants, autonomous cars, self-learning algorithms. These are challenges many tech companies and startups at looking at to push innovation forward. Blockchain is going to be a revolution in IT security because every transaction against your infrastructure is a strongly and cryptographically authenticated and granularly authorized.

Every year, there are exciting new developments in medicine and technology in healthcare. 2019 promises to be no different. Technologies that were in development in 2018 are set to deploy this year to improve patient outcomes. Laws and regulations are also changing, driving a shift in how care providers think about and deliver treatment. Patients’ role in their own treatment is also evolving alongside these technologies, allowing them to be more involved in their own treatment plans. So what tech trends can we expect to see in hospitals this year? These 8 may have the biggest impact.