Software & UX/UI

How prescriptive analytics is becoming more and more mainstream as new technologies are being developed, and what companies can do to take advantage of this movement.

A brief overview of digital analytics and steps that digital analytics experts take to leverage data to improve business conversions.

Financial products are mostly mathematical bets: statistical equations that – on average – should return a profit. In the simpler and more repetetive parts of this game, we can substitute Machine Learning for human brainpower and automate a lot of decisions. There is a lot to be gained for finance businesses in applying AI. And – in fact – plenty are already doing so successfully. To give you an idea – here’s a summary of the top applications of AI in Finance.

Microsoft Excel is widely used in almost every industry. Its intuitive interface and ease of use for organizing data, performing calculations, and analysis of data sets has led to it being commonly used in countless different fields globally.

Data science is the discipline of making data useful. Data science is a ‘concept to unify statistics, data analysis, machine learning and their related methods’ in order to ‘understand and analyze actual phenomena’ with data. When all the facts you need are visible to you, you can use descriptive analytics for making as many decisions as you please. It’s through our actions — our decisions — that we affect the world around us. So it is making data useful.

Retail coupled with the benefits and challenges leverages with adopting today’s leading technologies like AI and machine learning. Specifically, the key to humanizing experiences and influencing action is to treat each outcome as unique and dynamically respond to each customer individually, a feat which can only be scaled with machine learning. But in order to meaningfully impact the customer experience on this one-to one level, machine learning algorithms need to ingest vast amounts of data.  And the more data sources, the more successful an algorithm will be at predicting the desired outcome for each user.

Machine learning in finance may work magic, even though there is no magic behind it. Still, the success of machine learning project depends more on building efficient infrastructure, collecting suitable datasets, and applying the right algorithms. Machine learning is making significant inroads in the financial services industry. It helps reduce operational costs thanks to process automation, increase revenues thanks to better productivity and enhanced user experiences, and better compliance and reinforced security.  Let’s see why financial companies should care, what solutions they can implement with AI and machine learning, and how exactly they can apply this technology.

Welcome to this series of tutorials around Microservices hosted on AWS, we will start out with the basics and follow up with more advanced tutorials, if you have any special requests for a specific topic, just let me know and if enough people shout out about it, I will cover that topic.

We interviewed our expert Rebecca D. Wooten. Here’s her insights on emerging technologies, and how apps and gaming softwares are changing.

Information security analysts perform data analysis to identify vulnerabilities and threats to a company’s digital assets. These analytics are used to configure threat detection tools to optimize preventative measures and mitigate business risk. Trouble is the supply of analytics talent isn’t rising fast enough to meet this increasing cybersecurity demand. Some companies are hiring or partnering to meet overall cybersecurity needs, but the most common approach is to improve the technical skills the existing in the existing workforce. But today, a specific focus on cybersecurity analytics acumen seems to be lacking.

What prompted the idea for Experfy and how did it come to fruition? It all began with my experience with