- The Parable of Google Flu (PDF) — We explore two
issues that contributed to [Google Flu Trends]’s mistakes—big data hubris and algorithm dynamics—and offer lessons for moving forward in the big data age. Overtrained and underfed?
- Principles of Good Data Analysis (Greg Reda) — Once you’ve settled on your approach and data sources, you need to make sure you understand how the data was generated or captured, especially if you are using your own company’s data. Treble so if you are using data you snaffled off the net, riddled with collection bias and untold omissions. (via Stijn Debrouwere)
"machine learning" entries
More than algorithms, companies gain access to models that incorporate ideas generated by teams of data scientists
Data scientists were among the earliest and most enthusiastic users of crowdsourcing services. Lukas Biewald noted in a recent talk that one of the reasons he started CrowdFlower was that as a data scientist he got frustrated with having to create training sets for many of the problems he faced. More recently, companies have been experimenting with active learning (humans1 take care of uncertain cases, models handle the routine ones). Along those lines, Adam Marcus described in detail how Locu uses Crowdsourcing services to perform structured extraction (converting semi/unstructured data into structured data).
Another area where crowdsourcing is popping up is feature engineering and feature discovery. Experienced data scientists will attest that generating features is as (if not more) important than choice of algorithm. Startup CrowdAnalytix uses public/open data sets to help companies enhance their analytic models. The company has access to several thousand data scientists spread across 50 countries and counts a major social network among its customers. Its current focus is on providing “enterprise risk quantification services to Fortune 1000 companies”.
CrowdAnalytix breaks up projects in two phases: feature engineering and modeling. During the feature engineering phase, data scientists are presented with a problem (independent variable(s)) and are asked to propose features (predictors) and brief explanations for why they might prove useful. A panel of judges evaluate2 features based on the accompanying evidence and explanations. Typically 100+ teams enter this phase of the project, and 30+ teams propose reasonable features.
In order to make an effective decision, I need to understand key issues about the design, performance, and cost of cars, regardless of whether or not I actually know how to build one myself. The same is true for people deciding if machine learning is a good choice for their business goals or project. Will the payoff be worth the effort? What machine learning approach is most likely to produce valuable results for your particular situation? What size team with what expertise is necessary to be able to develop, deploy, and maintain your machine learning system?
Given the complex and previously esoteric nature of machine learning as a field – the sometimes daunting array of learning algorithms and the math needed to understand and employ them – many people feel the topic is one best left only to the few.
Hardcore Data Science speakers provided many practical suggestions and tips
One of the most popular offerings at Strata Santa Clara was Hardcore Data Science day. Over the next few weeks we hope to profile some of the speakers who presented, and make the video of the talks available as a bundle. In the meantime here are some notes and highlights from a day packed with great talks.
We’ve come to think of analytics as being comprised primarily of data and algorithms. Once data has been collected, “wrangled”, and stored, algorithms are unleashed to unlock its value. Longtime machine-learning researcher Alice Zheng of GraphLab, reminded attendees that data structures are critical to scaling machine-learning algorithms. Unfortunately there is a disconnect between machine-learning research and implementation (so much so, that some recent advances in large-scale ML are “rediscoveries” of known data structures):
While there are many data structures that arise in computer science, Alice devoted her talk to two data structures1 that are widely used in machine-learning:
Business users are starting to tackle problems that require machine-learning and statistics
I talk with many new companies who build tools for business analysts and other non-technical users. These new tools streamline and simplify important data tasks including interactive analysis (e.g., pivot tables and cohort analysis), interactive visual analysis (as popularized by Tableau and Qlikview), and more recently data preparation. Some of the newer tools scale to large data sets, while others explicitly target small to medium-sized data.
As I noted in a recent post, companies are beginning to build data analysis tools1 that target non-experts. Companies are betting that as business users start interacting with data, they will want to tackle some problems that require advanced analytics. With business analysts far outnumbering data scientists, it makes sense to offload some problems to non-experts2.
Moreover data seems to support the notion that business users are interested in more complex problems. I recently looked at data3 from 11 large Meetups (in NYC and the SF Bay Area) that target business analysts and business intelligence users. Altogether these Meetups had close to 5,000 active4 members. As you can see in the chart below, business users are interested in topics like machine learning (1 in 5), predictive analytics (1 in 4), and data mining (1 in 4):
An interview with Ash Damle of Lumiata on the role of data in healthcare.
Vinod Khosla has stirred up some controversy in the healthcare community over the last several years by suggesting that computers might be able to provide better care than doctors. This includes remarks he made at Strata Rx in 2012, including that, “We need to move from the practice of medicine to the science of medicine. And the science of medicine is way too complex for human beings to do.”
So when I saw the news that Khosla Ventures has just invested $4M in Series A funding into Lumiata (formerly MEDgle), a company that specializes in healthcare data analytics, I was very curious to hear more about that company’s vision. Ash Damle is the CEO at Lumiata. We recently spoke by phone to discuss how data can improve access to care and help level the playing field of care quality.
Tell me a little about Lumiata: what it is and what it does.
Ash Damle: We’re bringing together the best of medical science and graph analytics to provide the best prescriptive analysis to those providing care. We data-mine all the publicly available data sources, such as journals, de-identified records, etc. We analyze the data to make sure we’re learning the right things and, most importantly, what the relationships are among the data. We have fundamentally delved into looking at that whole graph, the way Google does to provide you with relevant search results. We curate those relationships to make sure they’re sensible, and take into account behavioral and social factors.