"data analysis" entries
Edge contributors say it's time to retire the search for one-size-fits-all answers.
The 2014 Edge Annual Question (EAQ) is out. This year, the question posed to the contributors is: What scientific idea is ready for retirement?
As usual with the EAQ, it provokes thought and promotes discussion. I have only read through a fraction of the responses so far, but I think it is important to highlight a few Edge contributors who answered with a common, and in my opinion a very important and timely, theme. The responses that initially caught my attention came from Laurence Smith (UCLA), Gavin Schmidt (NASA), Guilio Boccaletti (The Nature Conservancy) and Danny Hillis (Applied Minds). If I were to have been asked this question, my contribution for idea retirement would likely align most closely with these four responses: Smith and Boccaletti want to see same idea disappear — stationarity; Schmidt’s response focused on the abolition of simple answers; and Hillis wants to do away with cause-and-effect.
Big Data and analytics are the foundation of personalized medicine
Despite considerable progress in prevention and treatment, cancer remains the second leading cause of death in the United States. Even with the $50 billion pharmaceutical companies spend on research and development every year, any given cancer drug is ineffective in 75% of the patients receiving it. Typically, oncologists start patients on the cheapest likely chemotherapy (or the one their formulary suggests first) and in the 75% likelihood of non-response, iterate with increasingly expensive drugs until they find one that works, or until the patient dies. This process is inefficient and expensive, and subjects patients to unnecessary side effects, as well as causing them to lose precious time in their fight against a progressive disease. The vision is to enable oncologists to prescribe the right chemical the first time–one that will kill the target cancer cells with the least collateral damage to the patient.
How data can improve cancer treatment
Big data is enabling a new understanding of the molecular biology of cancer. The focus has changed over the last 20 years from the location of the tumor in the body (e.g., breast, colon or blood), to the effect of the individual’s genetics, especially the genetics of that individual’s cancer cells, on her response to treatment and sensitivity to side effects. For example, researchers have to date identified four distinct cell genotypes of breast cancer; identifying the cancer genotype allows the oncologist to prescribe the most effective available drug first.
Herceptin, the first drug developed to target a particular cancer genotype (HER2), rapidly demonstrated both the promise and the limitations of this approach. (Among the limitations, HER2 is only one of four known and many unknown breast cancer genotypes, and treatment selects for populations of resistant cancer cells, so the cancer can return in a more virulent form.)
Increasingly available data spurs organizations to make analysis easier
Genomics is making headlines in both academia and the celebrity world. With intense media coverage of Angelina Jolie’s recent double mastectomy after genetic tests revealed that she was predisposed to breast cancer, genetic testing and genomics have been propelled to the front of many more minds.
In this new data field, companies are approaching the collection, analysis, and turning of data into usable information from a variety of angles.
What business leaders need to know about data and data analysis to drive their businesses forward.
Foster and Tom have a long history of applying data to practical business problems. Their book, which evolved into Data Science for Business, was different from all the other data science books I’ve seen. It wasn’t about tools: Hadoop and R are scarcely mentioned, if at all. It wasn’t about coding: business students don’t need to learn how to implement machine learning algorithms in Python. It is about business: specifically, it’s about the data analytic thinking that business people need to work with data effectively.
Data analytic thinking means knowing what questions to ask, how to ask those questions, and whether the answers you get make sense. Business leaders don’t (and shouldn’t) do the data analysis themselves. But in this data-driven age, it’s critically important for business leaders to understand how to work with the data scientists on their teams. Read more…
As society becomes increasingly data driven, it's critical to remember big data isn't a magical tool for predicting the future.
If you eat ice cream, you’re more likely to drown.
That’s not true, of course. It’s just that both ice cream and swimming happen in the summer. The two are correlated — and ice cream consumption is a good predictor of drowning fatalities — but ice cream hardly causes drowning.
These kinds of correlations are all around us, and big data makes them easy to find. We can correlate childhood trauma with obesity, nutrition with crime rates, and how toddlers play with future political affiliations.
Just as we wouldn’t ban ice cream in the hopes of preventing drowning, we wouldn’t preemptively arrest someone because their diet wasn’t healthy. But a quantified society, awash in data, might be tempted to do so because overwhelming correlation looks a lot like causality. And overwhelming correlation is what big data does best.
It’s getting easier than ever to find correlations. Parallel computing, advances in algorithms, and the inexorable crawl of Moore’s Law have dramatically reduced how much it costs to analyze a data set. Consider an activity we do dozens of times a day, without thinking: a Google search. The search is farmed out to thousands of machines, and often returns hundreds of answers in less than a second. Big data might seem esoteric, but it’s already here. Read more…
Strata Community Profile on Jon Higbie, Managing Partner and Chief Scientist of Revenue Analytics
In his role as chief scientist at Atlanta-based consulting firm Revenue Analytics, Jon Higbie helps clients make sound pricing decisions for everything from hotel rooms, to movie theater popcorn, to that carton of OJ in the fridge.
And in the ever-growing field of data science where start-ups dominate much of the conversation, the 7-year-old company has a longevity that few others can claim just yet. They’ve been around the block a few times, and count behemoth companies like Coca-Cola and IHG among their clients.
We spoke recently about how revenue and pricing strategies have changed in recent years in response to the greater transparency of the internet, and the complex data algorithms that go into creating a simple glass of orange juice.
Using data science to predict the Oscars
Sophisticated algorithms are not going to write the perfect script or crawl YouTube to find the next Justin Beiber (that last one I think we can all be thankful for!). But a model can predict the probability of a nominee winning the Oscar, and recently our model has Argo overtaking Lincoln as the likely winner of Best Picture. Every day on FarsiteForecast.com we’ve been describing applications of data science for the media and entertainment industry, illustrating how our models work, and updating the likely winners based on the outcomes of the Awards Season leading up to the Oscars. Just as predictive analytics provides valuable decision-making tools in sectors from retail to healthcare to advocacy, data science can also empower smarter decisions for entertainment executives, which led us to launch the Oscar forecasting project. While the potential for data science to impact any organization is as unique as each company itself, we thought we’d offer a few use cases that have wide application for media and entertainment organizations.
A deconstructed web analytics report shows what the dashboard missed.
We can all agree that in 2013 web analytics is still a nightmare, right?
The last few years have brought about an enormous expansion in the top of the web analytics information overload funnel, and today I can discover just about any aspect of my web traffic that piques my curiosity.
I know how much traffic I’m getting, who told them to come here, how they got here, how long they’re staying, what they’re looking at, what they’re using to look at it, where they’re from, and just about anything else I want to know about them. If I don’t like what I’m looking at, I can customize everything from my dashboard to reports to parameters within those reports.
What none of this tells me is how I can be more successful at turning the words I put on the Internet into dollars in my pocket.
Now, I know what you’re thinking: “It’s all there! More information than you could ever figure out what to do with.”
The problem with that is that it’s all there. It’s more information than I could ever figure out what to do with. Read more…
In-memory data storage, SQL, data preparation and asking the right questions all emerged as key trends at Strata + Hadoop World.
At our successful Strata + Hadoop World conference (including successfully avoiding Sandy), a few themes emerged that resonated with my interests and experience as a hands-on data analyst and as a researcher who tracks technology adoption trends. Keep in mind that these themes reflect my personal biases. Others will have a different take on their own key takeaways from the conference.
1. In-memory data storage for faster queries and visualization
Interactive or real-time query for large datasets is seen as a key to analyst productivity (real-time as in query times fast enough to keep the user in the flow of analysis, from sub-second to less than a few minutes). The existing large-scale data management schemes aren’t fast enough and reduce analytical effectiveness when users can’t explore the data by quickly iterating through various query schemes. We see companies with large data stores building out their own in-memory tools, e.g., Dremel at Google, Druid at Metamarkets, and Sting at Netflix, and new tools, like Cloudera’s Impala announcement at the conference, UC Berkeley’s AMPLab’s Spark, SAP Hana, and Platfora.
We saw this coming a few years ago when analysts we pay attention to started building their own in-memory data store sandboxes, often in key/value data management tools like Redis, when trying to make sense of new, large-scale data stores. I know from my own work that there’s no better way to explore a new or unstructured data set than to be able to quickly run off a series of iterative queries, each informed by the last. Read more…