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Tony Searl

Datawocky: More data usually beats better algorithms - 0 views

anand.typepad.com/...more-data-usual.html

algorithms datamining analysis algorithm datawocky data mining lak11

shared by Tony Searl on 17 Jan 11 - Cached
  • This simple change made Google's ad marketplace much more efficient than Overture's. Notice that the algorithm itself is quite simple; it is the addition of the new data that made the difference.
  • Of course, you have to be judicious in your choice of the data to add to your data set.
  • Tony Searl on 17 Jan 11
     
    But the bigger point is, adding more, independent data usually beats out designing ever-better algorithms to analyze an existing data set. I'm often suprised that many people in the business, and even in academia, don't realize this.
hansdezwart

YouTube - Why Data Matters: IBM Leads Data Analysis in the Decade of Smart - 0 views

www.youtube.com/watch

data video ibm analytics week2 lak11

shared by hansdezwart on 23 Jan 11 - No Cached
  • hansdezwart on 23 Jan 11
     
    Data is present in all the systems and processes in the world. IBM helps analyze this multitude of information to make intelligent decisions, while enabling business efficiency and adding value to many industries.
Vanessa Vaile

LAK11: Big Data Small Data « Viplav Baxi's Meanderings - 0 views

learnos.wordpress.com/...lak11-big-data-small-data

data lak11 analytics week2 tools connectivism #lak11 #evomlit education

shared by Vanessa Vaile on 23 Jan 11 - No Cached
  • which data is more appropriate - BIG or small
  • most discussion about big data centres on quantity
  • other elements you mention – implication, new models, new decision making approaches – all flow from this abundance of data.
  • ...15 more annotations...
  • Increased data quantity requires new approaches
  • Is small beautiful? Look at the following links. Big Data, Small Data New Age of Innovation (Prahalad) So you like Big Data
  • reading on Insurers and the work done by Levitt and Dubner on Freakonomics tells us clearly that data not earlier thought relevant or causal can be an efficient predictor.
  • Secondly, strategies designed on BIG data
  • may overpower small data strategies
  • Thirdly, BIG data also has BIG impacting factors.
  • Fourthly, actions taken on BIG data will have big consequences,
  • Lastly, if everybody, big or small, started using BIG analytics, to make decisions
  • companies would anyway lose the competitive differentiator that analytics brings to them.
  • Corresponding to the question, how big does BIG need to be, the question I have is - how small really is small.
  • defining patterns that emerge from very small pieces of data (e.g. synchronicity)
  • how tools for SNA and analysis of BIG data can apply to Learning and Knowledge Analytics
  • at the other end it embraces how small changes can cause long term variations
  • not easy to analyze the small data
  • data that is small enough not to be generalizable
Media Lab

http://www.solidq.com/sqj/Documents/2010_July_Issue/SQJ_001_pag._40-45.pdf - 1 views

www.solidq.com/...SQJ_001_pag._40-45.pdf

database tools analysis lak11 analytics connectivism learning

shared by Media Lab on 07 Feb 11 - No Cached
  • Media Lab on 07 Feb 11
     
    Por qué usar Data mining?
Tony Searl

Unstructured data is worth the effort when you've got the right tools - O'Reilly Radar - 2 views

radar.oreilly.com/...tured-data-analysis-tools.html

data #LAK12 analytics learning

shared by Tony Searl on 07 Feb 12 - No Cached
  • With regard to the challenges, enterprise data is very messy, inconsistent, and spread out across multiple internal systems and applications. APIs like the ones we're working on can bring consistency and structure to a company's legacy data.
  • entity relation extraction is an important trend.
  • Entity relation extraction helps detect new knowledge in big data.
  • ...1 more annotation...
  • Other trends include detecting sentiment in social data, integrating multiple languages, and applying text analytics to audio and video transcripts.
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