Measuring Reality is much easier than Reconstructing it

When asked about the accuracy of RTD's data mining algorithms I often find myself explaining the reasons behind my belief that as a system RTD is much more accurate than any offline data mining system in most cases. One of the reasons for the enhanced accuracy is the capability of directly measuring reality rather than trying to reconstruct it from disconnected data sources.

For example, assume that you are studying the acceptance of offers in a call center. One of the inputs that may be interesting is the length of the queue at the time of the call. In an offline exercise you would have to obtain the logs from the telephony queue, hope that they are kept at enough accuracy, hope that the clock in the systems is synchronized and then query the log using a time based query for sorting the log records. The same thing in RTD is accomplished by simply querying the telephony queue for its current length, at the time of the call. There is no need to hope for data being collected properly, at the right granularity and with synchronized clocks. As we are dealing with reality as-it-happens, we do not care if the clocks are all wrong.

The end result of the difficulty in reconstructing reality is that typical offline data mining studies have much narrower inputs than those typically seen in RTD implementations. The difference in data availability in many cases more than makes up for possible accuracy improvements gained from a manually crafted data mining model.

Just to complete the picture I have to point out that I said "many cases" or "most cases" but not "all cases". The reason for that is that there are many good reasons to perform off-line data mining and it is worth investing in getting the data and complex queries right. Examples include retention, life-time value and in some cases product affinity models. There are also many areas for which RTD algorithms are not applicable, like data exploration, visualization and clustering.

Nevertheless, for predictive data mining applied to process improvement it is hard to beat the real time data collection capabilities or real time analytics systems.
Comments:

Yes, interrogating real time data from various data sources, and RTD producing more accurate prediction is undoubtedly true. However, when we consider "Real Time", what will be the practical considerations if:- 1. Customer contexts are from many channels, iphone, call center, booth/ATM/check in counter, web 2. Customer Master records are maintained separately in the silo apps. 3. An enterprise is using an ESB What will be suggested approach to handle this before we can actually enjoying the benefits of real time data ?

Posted by Eddie Tsui on March 21, 2010 at 06:31 PM PDT #

When dealing with multiple channels and siloed applications it is true that there may be inconsistencies in the data held by the many applications. Nevertheless, as long as these inconsistencies do not occur in an overwhelming number of cases, it is OK to live with them for the purpose of Real-Time process optimization, as long as the lift is there. For example, if two channels disagree on the exact address of a person, this may be a big problem from the transactional systems point of view for obvious reasons, like having to decide which address to use for sending letters. Nevertheless, from the point of view of optimization, for example in marketing, all that may happen is that the recommended offer will be sub-optimal by some degree. So while RTD can benefit from a unified, 360 degree, clean view of customer data, it is not a pre-requisite to have a process be optimied by RTD.

Posted by michel.adar@oracle.com on March 22, 2010 at 04:11 AM PDT #

A display of excellence. This was precise as well as to the point.

Posted by pig dog supplies on April 27, 2011 at 02:44 AM PDT #

Post a Comment:
  • HTML Syntax: NOT allowed
About

Issues related to Oracle Real-Time Decisions (RTD). Entries include implementation tips, technology descriptions and items of general interest to the RTD community.

Search

Categories
Archives
« April 2014
SunMonTueWedThuFriSat
  
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
   
       
Today