Big Data, Cloud & DevOps

It’s no mystery that data opens many new doors for both the disruptive data unicorns of the 21st century and traditional companies seeking to optimize business benefits. Due to the value of sensor data, the Internet of Things (IOT) and Industrial Internet of Things (IIOT) offer great promise to organizations seeking to establish entirely new markets or enable greater, more beneficial differentiation. Only time will tell if established market leaders can adapt to the modern data environment using purpose-built, cloud-optimized data analytics platforms, equipped with machine learning, or be rendered irrelevant by the more agile data-driven upstarts.

Organizations are constantly at risk of paying a hefty penalty for not complying with rules and regulations that dictate how they should operate and do business. It’s clear that the topic of compliance is broader than just Data Protection regulations and covers other global and regional regulations, industry-specific mandates and trading partner specific contracts. This article briefly discusses how an effective MDM strategy can be instrumental to facilitate an organization’s path to compliance.

A person who is really into data will start seeing numbers living and breathing all around them. The shortcut to demonstrating a strong passion in analytics is to cultivate some genuine interest in data. The big challenge often comes down to demonstrating and defending the passion for analytics in a data science interview. How can one go about doing this to stand out from the crowd? Drawing from my experience and from what many practitioners look for in candidates, here are 5 steps to achieving this. 

Big data can help a brand or business gain valuable insights on their customers and as a result refine their marketing efforts to improve engagement and increase conversions. As the world of digital continues to evolve, more big data metrics are being generated from an ever-expanding range of sources, meaning businesses like yours can really drill down and find out everything they need to know about their customers, both on a mass and individual basis.

Adopting a microservices architecture is not a one-size-fits-all approach. Despite being less and less popular, a monolith has its strong and durable advantages which work better for many use cases. The microservices architecture is more beneficial for complex and evolving applications. It offers effective solutions for handling a complicated system of different functions and services within one application. Microservices are ideal when it comes to the platforms covering many user journeys and workflows. But without proper microservices expertise, applying this model would be impossible. 

Downtime costs industrial manufacturers dozens of millions a year. Going for Big Data Analytics and predictive maintenance may be a great solution for companies that want to anticipate tech failures and slash downtime costs. It is worth noting that Big Data engineering amounts for around 70% of any Data Science project and that’s what businesses need to focus on first of all.  Companies that have implemented predictive maintenance have already improved their decision-making and reduced average downtime by more than 50%.

Life insurance finds its self uniquely placed to reap the benefits of AI and big data as its modus operandi is based on assessing large quantities of highly relevant data. Yet, despite the seemingly natural fit, insurance providers, and in particular life insurers, have failed to capitalize on new technologies- to the detriment of all involved. life insurers executives are newly hungry to integrate new technologies, with big data and AI emerging as central elements in their innovative efforts.

Data scientist is a loosely used term, a title that’s heavily abused in the industry. Quite like Big Data or, say AI. In practice, the title is often used as an umbrella term for related roles and is variously interpreted by companies in the industry.  What’s a realistic expectation on the skills needed to make a career in data science? And, can aspirants pick and choose skills of interest to carve out a preferred role, one that builds on strengths, while also being in demand?

The banking and finance industries are enjoying greater productivity and profitability than ever, not to mention finding new ways to serve customers and protect their interests. Some of these innovations are visible and obvious while others work quietly behind the scenes. In both cases, we can expect a better experience for clients and shareholders alike as new technologies come of age. Let’s look at a few of the ways this revolution is coming about.

We’re addicted to data. The addiction to data crosses a line when organizations focus on uncovering massive volumes of information with too little focus on what it all really means. It’s not enough to just share data—it has to be the right data for the right person, with clear next steps rooted in best practices. To reveal the truth and truly drive change, data must be customized and actionable. Here’s why both are vital. 

Bad Data or Poor Data means false information or inaccurate data that can be created by duplication of data, wrong formatting or by an uncomplicated error of typos. Bad Data can turn into an expensive mistake for your business. It can be a difficult problem to deal with. A recent survey shows that bad data costs companies an average of $9.7 million per year. Bad data and the problems that come with it are here to stay for now, and the only thing that we can do is deal with it.