In a 36 minute online interview with Richard Jacobs of FutureTech, Ed Lorenzini and Scott Chase explain the what’s, the why’s, and the wisdom of AnalyzeClients. (Ed is the CEO and Scott is the CTO of Analyze Corporation.). If you are wondering whether AnalyzeClients can improve your business, their explanation will show you the power of applying science to data when facing business challenges. Check it out at the following link.
Analyze teaches complex subjects in the areas of Big Data and CyberSecurity. Though the topics may be complex, our instructors make the material understandable.
This picture, taken during a recent course provided to graduates working in the technology industry, enhanced their knowledge of Big Data. Analyze teaches courses in both a classroom format, as well as offering online instructor-led training.
Music is a collection of notes. When those notes are played and recorded, the medium used is most often digital in nature. At it’s essence, digital data is just a combination of ones and zeroes stored in electronic fashion. Thus the digital information that is generated by musical recordings can be evaluated in a similar manner to all other types of digital data. For example, big data analysis frequently involves lots of pattern matching. Music data is an ideal candidate for discovering patterns. Identifying a pattern in the melody, harmony, rhythm, bass, etc. that a particular user finds appealing enables song recommendation software to recommend similar music that the user may not be aware of.
Identifying patterns can provide other benefits. A big problem that the music industry faces is piracy of recorded material. Analyze has written software that helps identify copyright violations on web-posted recordings. For example, YouTube, Dailymotion, Vimeo, and Vevo have many recordings posted online that can be accessed for free by anyone. Are the artists and publishers being fairly compensated for their work? Do they even know how much of their work is being made available for free? Analyze has applied big data pattern-matching techniques against open-source music repositories to identify online musical recordings. When the user indicates the musical recording he wants evaluated, the resulting report provides concrete evidence of musical usage of that recording. Pattern matching at its finest. See this link to view the product called AnalyzeStreamTrack. So, big data can contribute to the music industry just as it does too so many other industries.
A good article for a further read on how another company called Pandora analyzes music to offer customized recommendations is found at this link.
Facebook has built its highly profitable social network off its users, selling advertisements based on their ages, interests and other details. But the scrutiny over the company’s vast trove of personal data — following a report that a political consulting firm had improperly obtained information of 50 million users — is taking direct aim at that lucrative formula. 4
[Facebook CEO Mark] Zuckerberg … sought to defend his company's data collection practices, claiming that "the vast majority" of data that users share is "data you chose to share." 1
Facebook revealed that Cambridge Analytica, a data firm with ties to President Trump's campaign, may have had data on 87 million people. 1
In a response …, Cambridge Analytica asserted that it had licensed data from no more than 30 million people via research company GSR. The firm also asserted that it did not use any GSR data in its work in the 2016 presidential election and insisted that it had immediately expunged all GSR data from its systems after Facebook alerted it to the breach. 2
In 2013, Mr. Aleksandr Kogan, a Cambridge researcher, created a personality quiz app that about 300,000 people installed, Mr. Zuckerberg wrote. Because Facebook was an open platform, Mr. Kogan was able to collect data on tens of millions of friends of those users who had installed the personality quiz app. 3
Mr. Kogan, a professor at Cambridge University, paid users small sums to take a personality quiz and download an app, which collected private information from their profiles and from those of their friends. Facebook allowed that sort of data collection at the time. 4
By 2015, Mr. Kogan had shared his data and findings with Cambridge Analytica, which later used the material to single out American voters. Mr. Zuckerberg said Facebook had banned Mr. Kogan’s app and demanded that the researcher and Cambridge Analytica formally certify that the data had been deleted. 3
[In 2014], Mr. Zuckerberg said, Facebook changed its policy to limit how much data third-party apps could access. “These actions would prevent any app like Kogan’s from being able to access so much data today [April 2018],” he wrote. 3
The social networking giant is also facing an investigation by the Federal Trade Commission, which is looking into whether Facebook violated an agreement with the agency… The F.T.C. investigation is connected to a settlement the agency reached with Facebook in 2011 after finding that the company had told users that third-party apps on the social media site, like games, would not be allowed to access their data. But the apps, the agency found, were able to obtain almost all personal information about a user. 4
In a blog post Wednesday [Apr 4, 2018], Facebook's chief technology officer announced limits to developers' access and new restrictions regarding an account recovery feature that could have enabled bad actors to scrape user data. 1
[Mr. Zuckerberg] traced the information-sharing issue to 2007, when Facebook decided to become an open platform — enabling people to use Facebook to log into other apps and share detailed personal information about themselves and their friends. 3
Facebook also announced [on April 4, 2018] the details of the most extensive revamp of its privacy settings in several years. The changes tighten controls on a range of activities. 1
If you are looking for an alternative to Facebook’s customer data, Analyze Corporation can help. We have experience, the analytics, and data to help with consumer research. Give us a call at 703-273-1900 if that is something you need.
Links below were valid as of April 5, 2018:
With the abundance of consumer data available, it has become easy to understand your customers' backgrounds and interests. Using this information can improve the customer's satisfaction with your product, thus increasing sales and retention. The linked article mentions how the magazine industry could tailor the images on the front and back covers to suit the tastes of their subscribers. Thus one version of "People" or "Forbes" could depict a race car on the inside cover (for those interested in cars and motor-sports) and another version of the same magazine could depict a beach setting to capture the attention of different subscribers.
This level of customization is not the dreams of the future. It is the reality of today.
See article here.
Great news! You have just received a new job opportunity. But, it is not that close to home. The commute could be awful. Maybe you should relocate. But, where should you go? How long will it take you to fit into a new area? Will you really be a fit?
Analyze Corporation provides insights that can help with all kinds of decisions. We have a vast collection of information on consumer demographics and consumer behaviors that we tap for solving problems. We have recently posted a web-based tool that allows a user to evaluate various characteristics of other communities. This could help narrow down the search when considering re-locating. By feeding the site a desired zip code and answering several questions, the program will show you how you compare to people in your potentially new neighborhood.
Below is an example of the web-screen’s questionnaire that is filled-in with some sample data:
In this case, the user’s annual income level ranges from $60-74K. The following graph shows how the user’s income level compares with others who live in the 22003 zip code.
The income ranges for the people in the 22003 zip code are shown with the green bars and are separated into 8 income ranges. The user’s income range is designated with the orange bar. By hovering the mouse over any bar in the chart, a pop-up will appear that indicates the income range for that bar as well as the percentage of that range. From viewing the chart above, the user can see that his income level would place him slightly lower than most of his future neighbors. In fact, he would be in the lower 35%.
Other comparison charts showing community demographics are available as well. The chart below shows how he stacks up in the area of education:
In terms of education, there are four categories. The green bars show the education levels of the 22003 zip code. The yellow bar shows the education level of the user. From viewing the education details in the graph above, the percentage of those who have completed just high school is similar to those who have completed college. Both of these categories have a slightly greater percentage than those people who have completed graduate level work.
If this kind of data and analysis is intriguing, keep in mind that Analyze Corporation has all kinds of data and experience in analytics that can help you and your business gain valuable insights. Give us a call at 703-273-1900 or fill out our contact webform and we will reach out to you via email.
To actually run this web application, go to this link.
I've said it before and I'll say it again, direct marketing is not dead (cringe). Don't worry, I'm not diving into the details, I'm only stating a fact. And let's face it, facts are cool. They validate statements and opinions, and quiet all the objectors. So... to validate my statement that direct marketing is not dead, here are ten facts about direct marketing and mail. A word to the objectors, a wise man named Confucius once said, "real knowledge is to know the extent of one's ignorance." For those who agree, grab your blanket and a warm cup of hot chocolate. Here are ten satisfying facts about direct marketing/mail.
1. 52% of over-performers say their organizations leveraged data and analytics to improve marketing effectiveness compared to just 35 percent of under-performers.
2. Direct Marketing produced $2.05 trillion in sales in 2012 - representing roughly 8.7% of US GDP.
3. "Traditional offline marketing," which includes direct mailers, was a $93.6 billion industry in 2012.
4. 79% of consumers will act on direct mail immediately compared to only 45%, who say they deal with email straightaway. This research is from the DMA.
5. Direct Mail Triggers an online response: 44% visit the brand’s website, 34% search online, and 26% keep the piece for future reference.
6. In just over 1200 surveys, 74% said personalization of mail was important.
7. 66% of consumers keep their mail, 17% regularly keep an item of interest, and 48% do so occasionally.
8. 56% of people think that print mail is the most trustworthy of all communication channels.
9. A recent Direct Mail Information Service report highlights that over 75% of direct mail is opened by the recipients and 63% read the contents.
10. According to a USPS poll, 64% of customers said they valued the mail they received, yet only 36% of business owners believed customers valued their mailings.
I hope you found these facts new and enlightening, and can make use of them in your marketing strategy moving forward in the year.
Winter ended late this year in Boston and people are still trying to figure out how to dress. The Bruins are in the playoffs, which means we're in one of those weird years here where Hockey and Baseball seasons overlap. Should we be wearing Red and Blue, as summer fashion suggests, or put on one more weeks’ worth of black and yellow to show the B's some spirit? (I'm wearing Red Sox under my college sweatshirt; statisticians have always been partial to Baseball.)
In this way, our own Dr. Nolker and I could not be more different. I coach special needs baseball; he plays amateur hockey. Sometimes it seems like baseball and hockey people are from different worlds. Ever wonder what data science has to say about it? I looked at sociometric data from 100 thousand US households to try to come up with an answer. Here's what I found:
(1) Hockey fans are twice as likely (20% vs 11%) to ski recreationally. No surprises there.
(2) Hockey fans are more likely to gamble at a casino (24% vs 19%). Baseball folks don't gamble, unless it's in the locker room. (Remember Peter Rose?)
(3) Hockey fans are more likely to smoke cigars or premium tobacco (17.5% vs 13%). Chewing tobacco was not factored.
(4) Baseball players are significantly more religious. 38% read religious or inspirational books and magazines, vs. 29% of hockey fans. (We had 93 seasons without a series win to get some religion.)
(5) Hockey fans are better off, financially at least. They are more likely to have an "upscale lifestyle" (53% vs 48%) and a credit card from a premium department store (57% vs 50%). They'll need one to buy all those sweaters and ski equipment.
At least there was one thing we could both agree on. Only two baseball fans and absolutely no hockey fans (0% vs 0%), reported that they watch professional soccer. Guess we're not so different after all.
Do you have an interesting topic you'd like us to research and write about? Send us your ideas for future topics.
InsideAnalysis.com published an article this morning describing the big data work that Analyze has been doing to help combat illegal fishing. Check out the article.
Recognizing that if you're reading our blog or our social media posts, it’s unlikely that you're attending Dr. Nolker’s presentation at the Sentiment Analysis Symposium, we’ve posted a paper on our site that you can download for free entitled, “Social Computing and Weighting to Identify Member Roles in Online Communities.” This paper was the genesis of what has become a groundbreaking approach for pulling meaning out of social networks.
For those that would rather get the meat without sifting through the paper, here’s a summary of the paper and how it's useful when doing social network analysis:
A. Not everything that everyone says in an online social network is worth analyzing. We've all met that guy or gal that post things that don't matter.
B. Structure mining provides a means for finding and weighting which individuals are most worth analyzing and which individuals we should ignore in an automated fashion.
C. Not surprising to those that work in teams, the most important people to analyze are Influencers and Motivators (defined more specifically in the paper).
D. You can detect roles (like Influencers and Motivators)
in online communities (like Facebook, LinkedIN, Twitter, or other more specialized forums like those for Hackers) and sift out individuals that detract from a community by measuring things like:
- The number of one and two way conversations,
- Whether those conversations or posts are directed at individual persons,
- The number of different people users converse with, and
- How close (first, second, or third level connections) a user has in a given social network, among others.
C. This type of analysis can help businesses target to whom they market, social networks measure how healthy their communities are, and data scientists choose whom to target for more in-depth sentiment, natural language, or link analysis.
Download the paper for more detail.
Dr. Robert Nolker, Analyze’s Vice President of Research and Development, will be presenting at the Sentiment Analysis Symposium in New York this week, March 5, during the Technology and Innovation workshops. Dr. Nolker will be presenting his groundbreaking research in identifying user roles within social networks using structure mining approaches. Dr. Nolker’s approach provides two primary benefits. First, a user’s role provides insight into how much weight their opinions or comments should be given in text and sentiment analysis. Second, role identification can be used to reduce the size of your dataset, an important step to reducing processing costs when doing text analysis. Dr. Nolker will demonstrate these structure mining techniques on cybersecurity networks, more specifically software vulnerability research forums, in order to demonstrate how to choose the most important targets for additional sentiment and text analysis.
Analyze successfully uses advanced analytics to improve marketing return on investment, reduce operational labor costs, and improve cybersecurity by providing businesses next generation analytics using machine learning, graph theory, and structure mining techniques.
Read more about Dr. Nolker at http://analyzecorp.com/executiveteam
Illegal fishing is a significant economic and environmental challenge for countries around the world. Up to 40% of fishing catch in certain parts of the world is unlawful or unregulated, resulting in approximately $10B to $20B in economic losses and significantly depleting international food stocks.
Using geospatial position information, data scientists at Analyze provided a reliable method for characterizing fishing behaviors among ships on the high seas. These methods have the potential to significantly improve interdiction of illegal fishing on the high seas.
Using data transmitted from the Automated Identification System, Analyze studied nearly 500,000,000 data points for 110,000 vessels. They analyzed time-codes, vessel identity and motion data including: navigational status, rate of turn, speed over ground, lat/long, true heading, true bearing and more. The hypothesis was that unique motion behaviors could be associated with fishing activity using motion analytics. For example circular and duplicative motions could indicate fishing behavior.
Analyze research consisted in identifying and characterizing this unique motion behavior. To accomplish this, we employed a basic “big data” analytics strategy consisting of data acquisition, data extraction, transformation and loading, data analysis using statistical and machine leaning approaches, predictive analytics and visualization.
Once the data set was identified for a specific geo-fenced area in the, Analyze utilized a number of analytics from the Mercury Motion Analytics Module that would aid in the discovery of motion behaviors including position information, boundaries & geocoding, distance, velocity, acceleration, motion primitives, shape conformance and consistency of motion. Measures were derived from this data.
We noticed that frequent and significant changes in the vessel's compass heading (erratic heading) and erratic changes in velocity were strong predictors of fishing activity. The vessels themselves use a navigational status of 7 to self-report fishing activity but this was under and over reported throughout the data set. Analyze was able to derive a fishing prediction function using candidate analytics to positively identify fishing behavior on the open seas.
Data Scientists working on this analysis would be willing to discuss the process and methods used in this analysis. If you happen to be attending Strata 2014 in Santa Clara, visit Analyze in booth 928 in the Innovators Pavilion.
Data Science describes the processes, techniques, and tools used to extract deeper, non-obvious meaning from data of all kinds. Whether an organization is attempting to understand it's customers, operations, competition, or market, data science draws from best practices in computer science and statistics to find more meaning in the world.
While most organizations already make basic observations about their data by tracking sales, operations, productivity, and customer satisfaction; these organizations don't realize how much data science can improve decision making.
For example, several years ago we were asked to analyze a company attempting to address staffing problems. After gathering data, we were impressed with this organization's breadth of understanding of their sales cycle and staffing--the sales department knew exactly who their customers were, how frequently they purchased, and when they purchased. It knew how many hours it paid employees and for which projects it paid them. After we applied data fusion and trend analysis techniques, this data produced even deeper insights such as identifying trigger events in the sales cycle that could be used to plan staffing and supply chain events.
You can read many more examples of how data science produces deeper insights into data on our Case Studies page.
Big data is code for difficult data. More precisely, it is any data set where traditional techniques (databases and software) are inadequate -- whether trying to to store, query, manipulate, analyze, or otherwise use the data.
Because (by definition) Big Data is difficult, an industry is springing up, with various database, software products and analytical techniques to address the most common problems with traditional techniques. These are often described using the three (3) V's: Volume, Variety, and Velocity. Essentially, data sets that become too big, contain incongruous data types (such as video files, images, documents, and text and numerical values) and require real time storage (such as click behavior online or sensor outputs from satellites, cell phones, and vehicles).
The Big Data industry as a whole, in an effort to solve the 3Vs is still evolving as to how it will provide additional, non-obvious meaning difficult data giving rise to Data Science and Big Data Analytics.
In response to the requests we've received, we've created a simple video demonstration of our virtual Cybersecurity Training curriculum. You can read a sample list of our training curriculum here. Having developed more than 400 hours of the most advanced training curriculum and interactive cyber exercises for customers such as the Department of State and Department of Defense, Analyze's instructors offer both a mentored approach to teaching advanced cyber to practitioners as well as specific cyber demonstrations to offer management and executives looking to stay abreast of cyber threats.
On June 19, Analyze hosted the first Illegal, Unregulated, and Unreported (IUU) Fishing Roundtable. With attendees from the United States, United Kingdom, and Israel, the Roundtable brought together the most influential players in the campaign against IUU fishing, including the National Oceanographic and Atmospheric Agency, Pew Charitable Trusts, Google, SpaceQuest, Greenline Systems, IHS Fairfplay, Windward Maritime Solutions, OrbComm, and SkyTruth. Read more about the meeting by downloading Analyze's report.
Innovation is a way of looking at the world. Analyze takes pride in being innovative outside the bounds of any industry, product, or service. Some of our most innovative ideas come from the shower, the gym, nap time, family time, and anywhere but work. We are passionate about our ideas, love to share them, and are thrilled to see them come to reality. Whether they make us a $M or $0.99, we love our ideas.
The mobile app for iPhone and Android designed to ensure that your phone never embarrasses you again. Open the app, click NOT HERE, and your phone will go on silence. Whenever you return to that location again, your phone remembers and will ensure you're not interrupted. Important meetings, Movies, Theaters, Parent Teacher Conferences, Church... you choose ONCE and you'll never be interrupted again.