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?
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?
As data becomes more important to doing business, companies are finding themselves in need of various data-related workers. Sometimes, the titles aren’t clear, and it’s difficult to determine whether you need a data scientist, a data engineer, or perhaps both. Here are the descriptions of each one, as well as where the two jobs overlap. Data Scientist
Ah, legislators. No matter which side of the pond they’re on, they somehow manage to misunderstand everything, and even when they do understand, still manage to mess things up. Since big data is poorly understood even among mainstream business folks, it’s not surprising that it’s misunderstood in places like Capitol Hill and Whitehall. Here’s how legislators worldwide are getting big data wrong, and making all the wrong decisions because of it. Legislators Don’t Understand Big Data Recently, lawmakers in ten U.S. states were asked what big data was. A couple thought it was just an effort to share information across agencies. Some thought it was the result of using state-of-the-art technology to turn vast data sets into something that makes […] Continue Reading
You’re looking for a data scientist and the resumes are pouring in. Most of the job candidates have the hard skills it takes to succeed: strong technical skills, a solid mathematical background, some programming knowledge, etc. — but it’s the soft skills that will set the successful data scientist apart from the unsuccessful one. What qualities are essential for filling your position? 1. A Love for Solving Problems
A report released by ad tech firm Turn is causing quite a stir in the advertising community. According toTurn, advertisers are spending a mindboggling 500% more to advertise to the Millennial generation (18- to 35-year-olds) than they spend to reach all other demographic groups combined (this includes Traditionalists over age 70, Baby Boomers, Generation X, and Generation Z). Advertisers spend four times as much to reach Millennials with display advertising, four times more to reach them on social media, four and a half times more to reach them via mobile, and six times more to attract them through video advertising. Who are these Millennials and why are advertisers treating them like kings and queens? Are they really worth all this […] Continue Reading