Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Ibm watson


Published on

Artificial Intelligence

Published in: Data & Analytics
  • Be the first to comment

  • Be the first to like this

Ibm watson

  1. 1. IBM Watson – One Pager
  2. 2.  2  IBM Watson - Pros & Cons Pros & Cons  Ease of use, sharing and collaboration  Descriptive, Diagnostic, Predictive and Prescriptive analysis  Pattern discovery technology  Remove Hypothesis based analysis  No need to write mathematical models and algorithm  Models can be exported as R Code  Models can be scheduled at a specified time day /week/month etc..  Analysis can be exported to word, powerpoint and HTML  Add commentary to the analysis: Explains the key insights, focuses the user's attention on the crucial details, and recommends additional graphs that the user should see to better understand related patterns and the overall context Pros Cons  It takes a dataset (max 12 columns) – May be trail version limitation  The number of rows 10 million (Whether is in a single table or split across multiple relational tables)  The number of columns 500  Does not understand relational databases  First we have to load the data to Watson  We have separate tools to load the data to Watson (For end user to load the data)  Does not do the analysis to the level that BTB does  Limited functions and features  Can’t use user defined functions as there are in R
  3. 3.  3  IBM Watson - Pros & Cons Pros & Cons  Access, blend, transform data from multiple data sources  Automated Self-learning Data Cleansing  Draw any graph you know you want to see (hypothesis driven)  Guided Analysis shows related graphs and explains hidden patterns  Automated Statistical Validation of each graph  Truly Dynamic Dashboards detect and explain the key changes today  Privacy By Default leveraging K- anonymity  Audit and monitoring of data / analysis / model / collaboration  Automated K-fold Model Validation and model simplification  It also has point-and-click data join, transformation, and cleaning features Pros Cons  It takes a dataset (max 12 columns)
  4. 4.  4  Datawatch and their Monarch solution Pros & Cons  Simple to use. Business users are able to prepare all types of data by working directly with it on premise, this is self service data prep solution  Automatic data extraction. Drag and drop, no scripting skills required.  Web page data extraction. Capture just the data without other page “noise”  Instant understanding. Visually navigate and filter data and metadata of any size  Multiple source connectivity. Out-of-the-box connectivity tools link to all major relational databases, Hadoop, NoSQL, and more.  Prep visibility. Automatically capture every change made for transparency.  Process automation. Save prep steps for reuse and reapplication  Powerful algorithms. Easily join disparate data without being a data scientist  Report consolidation. Append tables with a single mouse click  Watson-ready. Prepped data exports directly into IBM Watson Analytics, ready for analysis  BI integration: Connectors included to a variety of BI platforms including Oracle BI, Qlik and Tableau allowing data to be blended without worrying where the data resides.  On premise: No need to upload data to the cloud, DW Monarch allows data blending/shaping to stay on premise and then when ready a one time upload to IBM Watson Analytics can be initiated.  Low cost: A single user or DW Monarch is $500 per annum.
  5. 5.  5  How IBM Watson works Pros & Cons