Defining Big Data
Recently, AGBeat addressed “Big Data” which is defined as large data sets which cannot be managed with simple, common software that captures and processes the data, and is typically consisting of at least dozens of terabytes in a single data set. The challenges of Big Data are, well, big, and most attention is being paid to the massive amounts of data being generated by social media sites like Facebook and Foursquare.
In fact, Big Data was a popular theme at the recent South by Southwest Conference in Austin, with technologists and marketers bringing their unique backgrounds to the conversation, each addressing the collection of and processing of the unprecedented data being collected, for the first time outside of the government, and the concerns that go along with consumers blindly offering up the data.
6 Big Data trends
Bassel Ojjeh, CEO of nPario and former Senior VP of the Data Technology division at Yahoo gave AGBeat an exclusive look at what he is seeing as the top six trends in Big Data:
- Consolidation of Big Data players by either system integrators or hardware makers. Big Data has a big appetite for consulting as well as hardware and storage.
- Hadoop becoming the source of raw data and connectors from there to enterprise data warehouses like Netezza, etc.
- The slow death of RDBMS as we grew up to know them. Which makes for a good question of what will Oracle do.
- Evolution of startups from those who focused on infrastructure plays (Cloudera as an example) to industry specific and application specific plays.
- Integration of data from Natural user interfaces and smart devices with social and behavioral data.
- “Global Impact – Big Data empowering more Arab Springs around the world”. We already saw this in few occasions.
Operating at the intersection of technology and advertising, nPario delivers Big Data publisher and marketing solutions. nPario provides “Audience DNA” to reduce consumer data complexity and to deliver ROI and is the only player in the industry offering these solutions on an open and extensible architecture. nPario is able to extract consumer insights from all sources and transform them into a set of integrated marketing apps that product owners, account executives and clients can use to drive their campaigns. The company helps business users to drive more relevant experiences for their customers through data. They have a multi-patented Big Data platform that was built for and managed by one of the largest online portals in the world.
5 ways professionals are mastering Big Data
Kami Huyse, CEO of Zoetica (an agency that connects brands and nonprofits with their communities for social good) recently crafted a list of five essential skills to master Big Data that is geared toward public relations professionals but we believe is applicable to almost any professional:
- Become an analyst. Don’t be intimidated by data and analytics.
- Learn Excel. One of the best gifts you can give yourself is to take an advanced Excel course to learn how to manipulate data in spreadsheets.
- Collect Data. Consider collecting your own data to supplement what you get from any tools you use.
- Evaluate Tools. By all means keep an eye out for new tools.
- Ask questions. Lots of them. With all of these big data tools, understanding the methodology new tools use to analyze data will be critical.
More details can be found on SpinSucks.com, but Huyse mainly notes that it is important not to get intimidated – the very phrase Big Data can be intimidating, but it is within reach of companies to grasp the wealth of information available to them.
Tonia Ries, founder of Modern Media and The Realtime Report and conferences said, “Understanding how to query, read, map and manipulate data — not what the typical PR or marketing person signed up for, but so critical. I look at it the same way I look at programming: I don’t need to know exactly how to do it, but I need to understand enough about it so I can ask the right questions and use the tools that are built by the programmers.”
“There is a ton of data that people can get their heads around and gain valuable insights with a few simple tools,” said Matt Hixson, CEO of Tellagence. “Learning excel is a great example of DIY analytics. You can gain a ton of insights from doing that. Where it gets complex is when you get to relationships and groups of relationships around specific subjects that form communities. We have tons of data points today but most of us end up putting a mental model of how it all fits together in our head. I think over the coming months people will see new accessible applications that allow them to visualize and understand what they have only pieced together in the past.”
Big Data is here and it is not for the nerds, it is something many companies are already tackling, and all businesses will be thinking about in coming years – it is better to get a jump on it sooner than later to maximize its potential.
Public relations professionals, marketing and communications staff or even CEOs have DIY options, but have amazing tools like nPario within reach, but the commonality of what everyone above is saying is that it is not a trendy phrase, it is a relevant business concept, and we would add that it is a concept most will ignore because it sounds too sophisticated and data nerdy, so professionals in the know will have the advantage.
AI technology is using facial recognition to hire the “right” people
(TECH NEWS) Artificial intelligence (AI) technology has made its way into the hiring process and while the intentions are good, I vote we proceed with extreme caution.
Artificial intelligence technology has made its way into the hiring process and while the intentions are good, I vote we proceed with extreme caution.
A UK based consumer goods giant, Unilever, is just one of several UK companies who have begun using AI technology to sort through initial job candidates. The goal of this technology is to increase the number of candidates whom a company can interview at the initial stages of the hiring process and to improve response time for those candidates.
The AI, developed by American company Hirevue, analyzes a candidate’s language, tone, and facial expression during a video interview. Hirevue insists that their product is different from traditional facial recognition technologies because it analyzes far more data points.
Hirevue’s chief technology officer, Loren Larsen, says, “We get about 25,000 data points from 15 minutes of video per candidate. The text, the audio and the video come together to give us a very clear analysis and rich data set of how someone is responding, the emotions and cognitions they go through.”
This data is then used to rank candidates on a scale of 1 to 100 against a database of traits identified in previously successful candidates.
There are two main flaws to this system. First, unless this AI technology is pulling from a huge diverse data pool it could be unintentionally discriminating against people without even being aware of it. Human bias is not as easy to remove from the equation as AI proponents would have you believe.
As an example, how does this AI handle people who are disabled or whose facial expressions that read differently than the general population, such as people with Down Syndrome or those who have survived traumatic facial injuries?
Second, seeking to hire someone who possess the same qualities as the person who was previously successful at a role is shortsighted. There are many ways to accomplish the same task with above average results. Companies who adopt this low-risk mentality could be missing out on great opportunities long-term. You will never know what actually works best if you don’t try.
The big question here is whether or not AI technology is ready to influence the job market on this scale.
The ‘move fast and break things’ trend is finally over
(TECH NEWS) Time is running out for this decade — and for a popular Big Tech phrase responsible for a lot of collateral damage. What’s next?
Time is running out for the decade. With less than 20 days left, it’s got us reflecting on the journeys of different economic sectors in the United States. And no industry has had a more tumultuous time of it than Big Tech.
A lot has changed in ten years. For starters, Americans have become increasingly disillusioned with Silicon Valley. The Pew Research Center found that only 50 percent of Americans believe technology firms have a positive effect on the country. That statistic is not too bad on its own, but that’s down 21 percent from only four years ago. Gallup found in 2019 that 48 percent of Americans also want more regulations on Big Tech. And The New York Times called the 2010s as “the decade Big Tech lost its way”.
Maybe that’s why big wigs at these tech firms have been quietly ditching a concept that was their Golden Rule in the early part of the decade: Move Fast and Break Things.
This concept is a modern take on the adage “you can’t make an omelet without breaking a few eggs.” For most of these firms, any innovation justified some of the collateral damage within its wake. And this scrappy “build it now and worry about it later” philosophy was a favorite of not just Facebook and Twitter, but also of many venture capital firms too.
The Move Fast and Break Things manta encouraged devs to push their coding changes to go live and let the chips fall where they may. But bugs pile up. Enter technical debt.
“Technical debt happens every time you do things that might get you closer to your goal now but create problems that you’ll have to fix later,” said The Quantified VC in an article on Medium. “As you move fast and break things, you will certainly accumulate technical debt.”
If enough technical debt comes into play, any new line of code could be the thing that topples a firm like a house of cards. And now that the consumer is used to tech in their daily routines, interruptions in service are extremely bad news for everyone.
As Mark Zuckerburg himself said it: “When you build something that you don’t have to fix 10 times, you can move forward on top of what you’ve built.”
To get back some of the trust that has ebbed from Big Tech over the years, firms can’t just keep with the Move Fast and Break Things status quo.
“The public will continue to grow weary of perceived abuses by tech companies, and will favor businesses that address economic, social, and environmental problems,” said Hemant Taneja in his article for Harvard Business Review. “Minimum viable products must be replaced by minimum virtuous products that … build in guards against potential harms.”
It’s not about chasing the bottom dollar at the cost of the consumer. Losing trust will hurt any company if left unchecked for long.
There’s a cap on advancement in our current technological state. It’s called Moore’s Law. And we’re rapidly approaching the theoretical limits of it.
“When you understand the fundamental technology that underlies a product or service, you can move quickly, trying out nearly endless permutations until you arrive at an optimized solution. That’s often far more effective than a more planned, deliberate approach,” said Greg Satell in his article for HBR.
Soon enough, Big Tech will be in relatively new waters with quantum computing, biofeedback and AI. There’s no way to move as fast as these technology firms have in the past. And even if they could, should they?
Big Tech has experienced major growing pains since the dawn of our new Millenium. And now that some firms are entering their 20s, there’s a choice to be made. Continue to grow up or keep using an idea that’s worn out it’s welcome with the consumer and that has no guarantee will work with future technologies.
Maybe that’s why Facebook’s motto is now “Move Fast with Stable Infrastructure.”
Computer vision helps AI create a recipe from just a photo
(TECH NEWS) It’s so hard to find the right recipe for that beautiful meal you saw on tv or online. Well computer vision helps AI recreate it from a picture!
Ever seen at a photo of a delicious looking meal on Instagram and wondered how the heck to make that? Now there’s an AI for that, kind of.
Facebook’s AI research lab has been developing a system that can analyze a photo of food and then create a recipe. So, is Facebook trying to take on all the food bloggers of the world now too?
Well, not exactly, the AI is part of an ongoing effort to teach AI how to see and then understand the visual world. Food is just a fun and challenging training exercise. They have been referring to it as “inverse cooking.”
According to Facebook, “The “inverse cooking” system uses computer vision, technology that extracts information from digital images and videos to give computers a high level of understanding of the visual world,”
The concept of computer vision isn’t new. Computer vision is the guiding force behind mobile apps that can identify something just by snapping a picture. If you’ve ever taken a photo of your credit card on an app instead of typing out all the numbers, then you’ve seen computer vision in action.
Facebook researchers insist that this is no ordinary computer vision because their system uses two networks to arrive at the solution, therefore increasing accuracy. According to Facebook research scientist Michal Drozdzal, the system works by dividing the problem into two parts. A neutral network works to identify ingredients that are visible in the image, while the second network pulls a recipe from a kind of database.
These two networks have been the key to researcher’s success with more complicated dishes where you can’t necessarily see every ingredient. Of course, the tech team hasn’t stepped foot in the kitchen yet, so the jury is still out.
This sounds neat and all, but why should you care if the computer is learning how to cook?
Research projects like this one carry AI technology a long way. As the AI gets smarter and expands its limits, researchers are able to conceptualize new ways to put the technology to use in our everyday lives. For now, AI like this is saving you the trouble of typing out your entire credit card number, but someday it could analyze images on a much grander scale.
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