This blog post is part of the Big Data Week Speaker interviews series. Harry shares his thoughts on Fintech challenges, the impact of big data in the Finance field, also offering a sneak peek into his talk at “Big Data in Use” Conference | Big Data Week 2016. Why is it important for businesses in your industry to be more data driven nowadays? Finance has always been data-rich and data-driven but the big data technologies have enabled us to use data to deliver an experience and product at a customer level whereas before data was only used to report aggregated information to managers. And if we don’t do it there is a legion of Fintech challengers looking to do it instead. […] Continue Reading
According to industry insider and InfoWorld columnist Andy Oliver, what you need to know about Hadoop is that it is no longer Hadoop. At least, it isn’t the Hadoop that everyone once knew and may or may not have loved. Hadoop’s co-creator Doug Cutting believes that the changes are a direct result of the open source roots of Hadoop and related projects, most notably Spark. Together, Hadoop and Spark are dominating the big data marketplace, with Hadoop commanding half of big data’s $100 billion annual market value, and Spark surpassing MapReduce in terms of popularity (at least among those searching for big data products on Google). While Hadoop is the go-to big data framework and Spark reigns supreme when it […] Continue Reading
What is machine learning? Machine learning (often shortened within the industry as ML) is a kind of artificial intelligence (AI), which is an offshoot of big data. AI, and by extension, ML, utilize big data in a different way than typical analysis. These practices are capable of taking in data, building assumptions based on the data, testing hypotheses about those assumptions, and drawing conclusions from the results. Though not nearly as complex and sophisticated as human learning, AI and ML can do rudimentary logic by themselves. Yes, it sounds a little creepy (and potentially even 2001 Space Odyessy-ish), but there are numerous practical uses for AI and ML. What can ML do for retailers?
Currently, there are roughly 23 billion connected devices on earth. By next year, that number will jump past 28 billion. By 2020, we will be contending with some 50-odd million connected devices. As those devices accumulate, the amount of data escalates, as well, doubling in size about every two years. By the year 2020, there will be 5,200 gigabytes of data for every person on the planet. This will account for an additional 40 zettabytes, which is about 57 times the number of grains of sand on all of the beaches around the globe put together. If you’re struggling to keep a few dozen or a few hundred users connected with reliable speed, performance, and security, imagine what it will […] Continue Reading
Arxan Technologies, the company specialized in software security and mobile application protection has released its 5th annual State of Application Security Report. This report takes an in-depth look into the security of some of the most popular mobile health and mobile finance applications available today. The company surveyed 126 popular mobile health and finance apps from the US, UK, Japan, and Germany.
As most organizational data sets grow beyond the capabilities of the traditional data warehouse, a lot of businesses are taking a look at the option of building a data warehouse. But scanning the tech news headlines and IT blogs, you’ll find two camps: the one saying that the data lake is the salvation of your data architecture and data plans, and the camp that maintains a data lake is nothing but a data swamp — the place where good data goes to die without a decent burial.
The past couple of years haven’t been easy ones for cyber security specialists, especially those charged with protecting the growing reservoirs of Big Data. The headlines featured more stories about data breaches than about most any other aspect of Big Data and related technologies — even though that time period marked some impressive improvements in terms of open source software, database technology, and the growing importance of the data scientist.
If you read the industry rags, you’ll start to believe that data scientists are rarer than diamonds, and more expensive, too. While they certainly aren’t in abundance, you can get a good data scientist if you’re willing to look, willing to pay for one, and a data scientist is actually what you need. Here’s everything you need to know before launching your next head-hunting campaign. Be Sure Your Organization is Ready for a Data Scientist
The most obvious reason to choose open source software is that it’s free. But for most businesses, especially enterprises, that isn’t as big a point as you might think. After all, most companies think little of dropping $50,000 to $100,000 (often more) on a great software package. Yet open source software dominates the realm of big data solutions, and, in fact, is making quite an impact across the arena of business and enterprise software. Why?