@Jamescon - thanks Jim...I'll look for Beth's article. Unfortunately lots of the big data applications in education are at the post-secondary level; not so much for K-12. [Maybe that's a niche I need to carve out. :) ]
@Lyndon - after SF uses the model with parking for some time, they should have data that allows predictions. Also, there are toll roads in Atlanta and Dallas that use traffic dependent pricing. Seems like another opportunity for predictive modeling.
@Marklr. There were some initiatives along those lines -- predicting student outcomes -- discussed here on the site last summer. Beth Schultz wrote about them, although they may have been at the college level.
== I believe that the city of SF uses a machine learning model to set demand driven prices for public parking. They are striving to have 2 spots available all the time for each sector and the prices adjust based on demand.==
Not travel demand forecasting exactly, but getting closer and closer...
Jared or anyone in the audience: Can you point me to some examples of where big data and analytics are being used in the K-12 Education sector, especially anything related to predictive analytics for student outcomes and instructional practices? TIA.
@Lyndon_Henry I believe that the city of SF uses a machine learning model to set demand driven prices for public parking. They are striving to have 2 spots available all the time for each sector and the prices adjust based on demand.
Yes, Jameson - like Information is applied data and knowledge is applied information (with experience thrown in.) But I guess I find the terms thrown around ore as buzz words by many - so it's not always clear where the real value is under what becomes hype i guess. And i guess that gets back to my other questions about - actually finding real business value.
@GaryCokins Good point! If the executives you work with don't create an enviornment where analtyics and questions are debated (at least some of the time) then I guess it is good there is a shortage and you can look for another organization to help with your talent
I commented earlier about the tremendous utilization of analytics/big data in the public transit industry. In particular, travel demand forecasting and ridership forecasting seem classical examples of predictive analytics.
Any idea if machine learning development is impacting this implementation of predictive analytics?
@EAS4000. Good point about data, information and knowledge. Isn't knowledge just another form of data, or maybe an aggregation of data. Consider the knowledge management/transfer systems that today are capturing the knowledge of experienced field workers like repair people before they retire. Their "data" is what they know about how to fix a particular device, not just troubleshooting it, but the best way to access something like a circuit board.
Jared ... Where do the C-suite executives fit in? My observation is in the past the best leaders had the best answers. But today the best leader will have best questions. They can not rely on their past experiences and intuition that got them promoted to the executive jobs. But not all executives create a culture for questions.
Louis: dealing with peoples' preconceived notions, especially from senior management involves "big politics". It seems no matter what the data actually shows, their minds are made up. The *presentation* of your data analysis becomes most important if you want to changes opinions.
@MarkLR Data and information are different things and are often treated intercahangably. Even when I'm trying not too. Information driven management is probably more correct but the risk is that information is not derived from data but from a single experience.
I don't think big information really makes sense because it is that big data that makes information and I might find a single very valuable insight from lots of data.
Thanks for the interesting conversation. Seems like finding the value inthe data is a big challenge. Do companies usually have to experiment a lot to find it - or is the applied value, or monitizabel value apparant? I see pitfalls in trying to monetize data - that usually you need to know things beyond the data you currently have - so end up trying to colect even more data before youc an monetiz...
Jared--You mentioned data-driven decision making (a.k.a. "data driven management"). Do you make any distinctions between "data-driven" and "information-driven"? I know lots of folks use the terms "data" and "information" interchangably but they are different animals. Should "big data" really be "big information" ?
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@Rodney, i think that's true on the surface--but organizations are wanting more and more sophisticated information and extrapolation. The end result is that the job used to be arithmatic (basic queries) but now it's turning into calculus!
As a scientist by training, I continue to be naively amazed at the degree to which business and even medicine have NOT been "data-driven"; e.g., "evidence-based medicine". What do you call the opposite of "data-driven"?
Too true Hailey. I wonder how much or little real world experience comes in to play in dta anayltics. To my untrained eye it seems like once you learn how to structure query, the rest is just plugging in parameters.
I mean there are a lot of published articles on some topics like "Big Data" for instance, but you need to find a trend on a controversial issue, How about taking that kind of data? specially since Jared spoke about the limitations of some models or THE MODEL
@Sergio, my obstacle is along that same point of having accurate and none edited data. Our companies data is very subjectively entered (or not entered) so the end results can be manipulated - either intentionally or because of a lack of training/knowledge. We can not wait until the data is perfect to run the analytics, so we have to make a lot of assumptions which brings that human factor right back into the analytics. The challenge is not only to get the leadership of the company to see this as a necessisity, but also for those involved with collecting/entering the data (if not automated) to believe that accuracy and totality is important and essential to their success too.
I can say that the machines at my bank have done that already. Within 4 hours of one transaction in New Hampshire, my credit card somehow bought some things at a convenience store in South Korea. No, I can't teleport.
My question: What do you see as the biggest challenges around using technology to create an analytical environment for data mining, machine learning, and working with big data? Where are the biggest stumbling blocks?
Via machine learning, couldn't predictive analytics be enriched to, e.g., learn lessons from customer behavior, competitor activities, etc., even current events, and recommend alternative or new strategies for the organization?
The book mentions that "no amount of data would have helped" predict the financial collapse in 2008. I understand - recent data was useless. But if we had longer term data of a different types, i think the collapse could have been predicted.