Shopping around for a Big Data analytics solution is a daunting task for anyone. But for those who are somewhat familiar with data science, a common area of misunderstanding — and underestimating — is clustering techniques. Whether the assumption is that all clustering techniques are created equal or that a company needs only one or two clustering techniques, business buyers are often left scratching their heads. The fact is several types of clustering techniques exist — each with its own strengths and weaknesses — and companies need access to a variety of techniques to accomplish optimal results.
What’s the big deal about Millennials? For starters, they’re now the biggest demographic in America. But their needs are unlike any generation before them. Here’s what retailers must know to succeed in marketing to this 80-million–strong population.
Millennials are now the largest living generation in the United States, according to the US Census Bureau. Defined loosely as those born from the early 80s to the early 2000s, Millennials account for roughly 25% of the US population at 83.1 million, exceeding the 75.4 million Baby Boomers in the country. They grew up with household computers, cell phones, and myriad other forms of technology, and they entered the work force during the Great Recession, which has made them price-conscious, tech-savvy shoppers — and robust savers. About half of them still live at home, and they’ve taken over the work force. While most are holding off on marriage, nearly 40% have children and 9% live with domestic partners, indicating that they’re starting families and establishing households (i.e. shopping and spending). Perhaps the most telling stats come from a study from FutureCast, which says that Millennials’ purchases are most influenced by their social circles and that they’re the first generation to influence older generations.
In this interview with PYMNTS, Laks Srinivasan discusses the challenges and opportunities the Big Data brings to the world's largest enterprises.
Big Data may be everywhere, but that doesn’t mean that companies are able to actually get the most out of it in an efficient and scalable way.
With the notion that the world’s flow of computable information would eventually become the oil of the twenty-first century, Opera Solutions was launched back in 2004 with the goal of addressing the challenges and opportunities emerging as a result of the influx of data that came from more people having increased access to technology and a greater ability to generate even more data.
AI is helping people create exciting applications and new ways to serve customers, cure diseases, prevent security threats, and much more. Rapid progress continues to unlock increasingly more opportunities for enterprises and scientific research where AI can make a big impact. Many believe that the real-world potential for AI is highly promising. Speaking at a 2016 AI conference in London, Microsoft’s chief envisioning officer, Dave Coplin observed, “This technology will change how we relate to technology. It will change how we relate to each other. I would argue that it will even change how we perceive what it means to be human.” Apparently, the best is still to come.
If you’re in the business of pretty much anything, you’ve got a lot of important data coming in from a lot of different places — both internal and external. What you might be lacking are some best practices that could help you access or see all of that data and be in a position to extract important insights that could nudge your business into new competitive directions.
But what data is relevant to your business and where is it? Can you access it when you want to? Do you know that it’s accurate, current, clean, and complete? Can you easily pull all the data together, no matter what format it’s in or how often it changes?
Basically, is your data ready to support analytics?
In my role of leading Product Management and Presales at Opera Solutions, I am constantly exposed to direct customer interactions, most often in the early stages of the sales cycle. In these meetings, part of my job is to assess our prospective customer’s pain points and needs as much as they are assessing our products, technology, and capabilities. Thus, given the exposure we get at Opera Solutions, I am in a good position to understand real-world business needs around analytics across industries.
We often talk about corporate cultures, but the experience with hundreds of customers led me to think of corporate psychology, and juxtaposing Maslow’s hierarchy of needs to the current state of data science adoption and readiness in the industry. Companies need to recognize the stage they are in and not be seduced by the hype or promise of the technology. Data science adoption needs not follow a sequential maturity process; dynamic corporations can certainly accelerate things when the need and will exist. So for fun, here’s a take on Maslow’s Hierarchy of Needs adapted to data science.
To predict the future, one must look at the past, says the old adage. To determine what to expect in 2017, we thought it was best to draw lessons from 2016 despite our industry’s yearning for dramatic change. Laks Srinivasan, COO at Opera Solutions, shares his insights into the biggest Big Data trends of 2016 and reflects on where the market is going and how companies will react.
Artificial intelligence is no longer just evolving nomenclature in IT. Everyone is taking interest. With the mainstream press and bloggers from every corner weighing in, it is worth taking stock of the nomenclature and learning how to differentiate three overly used key terms: artificial intelligence (AI), machine learning, and deep learning. The simplest way to think of their relationship is to visualize them as a concentric model (as depicted in the figure below) with AI — the idea that came first and has since been evolving — having the largest area. This is followed by machine learning, which blossomed later and is shown as a subset of AI. Inside both of those is deep learning, which is just one class of machine learning algorithms but one that is currently driving today’s AI explosion.
A guide from the head of HR at a leading analytics company
Data scientists are in higher demand than ever before. According to the latest CrowdFlower survey, 79% of respondents reported a data scientist shortage in 2015. In 2016, that number grew to 83%. The race is on to find skilled people who can organize, structure, and make business sense out of Big Data sets. People with heavy STEM, analytics, and conceptual skills, and the attendant work-friendly personality traits (insatiable curiosity, ability to prioritize, and a healthy dose of skepticism, to name a few), can virtually write their own tickets.
Everywhere you turn, both business and IT talk about data science. But there’s also trepidation about how to get started, especially in the context of attaining an organization’s business goals and objectives beyond the realm of lab or departmental experimentation.