tag:blogger.com,1999:blog-39818639649457841282024-03-13T09:35:35.547-07:00Technosoft Corporation's BlogTechnosoft Corporationhttp://www.blogger.com/profile/12361615608425186450noreply@blogger.comBlogger6125tag:blogger.com,1999:blog-3981863964945784128.post-42343246509859987232013-12-05T22:56:00.004-08:002013-12-05T22:56:45.016-08:00The Art of Conversation and the Science of Service<div dir="ltr" style="text-align: left;" trbidi="on">
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<br />
<div class="MsoNormal">
<span style="font-family: "Arial","sans-serif";">The frequent
launch of a variety of products across emerging customer segments has led to
the proliferation of a number of devices and equipment. With increasing customer
expectations and the ease in which complaints can be lodged and enquiries sought
online, there is a surge in synchronous and asynchronous conversation.
Consequently, we see product manufacturers and OEMs competing to provide higher
quality of customer service. The first step in addressing customer needs is to
converse, engage and address. This is imperative to retain or gain market
share. </span></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<span style="font-family: "Arial","sans-serif";">This competition
in “service lifecycle management” has resulted in the rapid rise in data mining
activities on customer complaints, inquiries and the like, as recorded in
contact center logs. These activities are aimed at identifying the root causes
of specific issues .The proactive resolution through alerts is often through
the deployment and usage of smart algorithms. </span></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<span style="font-family: "Arial","sans-serif";">Recently our
team interacted with OEMs in the telecom, manufacturing and healthcare industry
segments. We discovered the root cause analysis and the subsequent proactive
resolution of customer issues, to be the significant element in their overall
customer acquisition and retention strategy. Not surprisingly, the spurt of
such activities has been catalyzed by the evolution of marketing strategies,
leveraging customers’ sentiments about different product or service offerings,
expressed through social networks. The arcane world of Natural Language
Processing has now come to the forefront as an application to aid in addressing
these novel business challenges. </span></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<span style="font-family: "Arial","sans-serif";">Above all,
research has added a host of new algorithms in this area, such as, dialog act
tagging, topic modeling, sentence boundary detection, text tiling and so on,
enabling to bring in solutions to hitherto unsolvable problems in this field.</span></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<span style="font-family: "Arial","sans-serif";">However, when
I delved a bit deeper into the problem, I found that there are distinctly three
kinds of issues which come from the data sources in this field: </span></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoListParagraph" style="margin-left: .75in; mso-list: l0 level1 lfo1; text-indent: -.5in;">
<span style="font-family: "Arial","sans-serif"; mso-fareast-font-family: Arial;"><span style="mso-list: Ignore;">(i)<span style="font: 7.0pt "Times New Roman";">
</span></span></span><span style="font-family: "Arial","sans-serif";">mining
event logs from devices</span></div>
<div class="MsoNormal" style="margin-left: .25in;">
<br /></div>
<div class="MsoListParagraph" style="margin-left: .75in; mso-list: l0 level1 lfo1; text-indent: -.5in;">
<span style="font-family: "Arial","sans-serif"; mso-fareast-font-family: Arial;"><span style="mso-list: Ignore;">(ii)<span style="font: 7.0pt "Times New Roman";">
</span></span></span><span style="font-family: "Arial","sans-serif";">analyzing
tweets or comments from social networks</span></div>
<div class="MsoListParagraph">
<br /></div>
<div class="MsoListParagraph" style="margin-left: .75in; mso-list: l0 level1 lfo1; text-indent: -.5in;">
<span style="font-family: "Arial","sans-serif"; mso-fareast-font-family: Arial;"><span style="mso-list: Ignore;">(iii)<span style="font: 7.0pt "Times New Roman";">
</span></span></span><span style="font-family: "Arial","sans-serif";">identifying
root cause of problem from call notes.</span></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<span style="font-family: "Arial","sans-serif";">Initially we
thought they are very similar as they have a few common characteristics: they
are unstructured text, are event-driven and have an underlying taxonomy of
products, events or sentiments. </span></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<span style="font-family: "Arial","sans-serif";">Interestingly,
however, they are significantly different in some key aspects, necessitating
the need for applying different solution strategies. While device logs are
easier to deal with due to the standardized response pre-configured in systems,
such as, SMART logs, sentiment analysis is more complex due to the wide
diversity of the ways in which people react to similar scenarios and express
their sentiments. </span></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<span style="font-family: "Arial","sans-serif";">The human
element in social network feeds introduces a lot more noise, such as, acronyms
and polysemy. We still have scope to model discussion topics based on a <i>apriori
</i>defined taxonomy or a taxonomy built on-the fly based on known language
constructs, such as, synonyms, phrases, adjectives, and so on. So the problem
of extracting actionable insights from device logs, can be considered, as a
subset of the problem of developing targeted marketing strategies based on the
analysis tweets or other forms for social network feeds.</span></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<span style="font-family: "Arial","sans-serif";">Identifying
root cause of problems from contact center or technical support logs, sometimes
known as conversation mining or interaction mining, is an even more complex problem,
due to several reasons,for example:</span></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoListParagraph" style="margin-left: 39.0pt; mso-list: l1 level1 lfo2; text-indent: -.5in;">
<span style="font-family: "Arial","sans-serif"; mso-fareast-font-family: Arial;"><span style="mso-list: Ignore;">(i)<span style="font: 7.0pt "Times New Roman";">
</span></span></span><span style="font-family: "Arial","sans-serif";">difference
in terminology of the expert call center agent and the novice customer, </span></div>
<div class="MsoListParagraph" style="margin-left: 39.0pt; mso-list: l1 level1 lfo2; text-indent: -.5in;">
<span style="font-family: "Arial","sans-serif"; mso-fareast-font-family: Arial;"><span style="mso-list: Ignore;">(ii)<span style="font: 7.0pt "Times New Roman";">
</span></span></span><span style="font-family: "Arial","sans-serif";"> iterative
and asynchronous nature of messages, </span></div>
<div class="MsoListParagraph" style="margin-left: 39.0pt; mso-list: l1 level1 lfo2; text-indent: -.5in;">
<span style="font-family: "Arial","sans-serif"; mso-fareast-font-family: Arial;"><span style="mso-list: Ignore;">(iii)<span style="font: 7.0pt "Times New Roman";">
</span></span></span><span style="font-family: "Arial","sans-serif";">huge
boundary of the problem scope, and so on. Several approaches have been
developed in recent times, but most of them seem to elude the real solution to
this problem. </span></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<span style="font-family: "Arial","sans-serif";">Our attempts
to solve the problem by integrating topic modeling, hyperclique pattern
discovery and sequence mining to detect anomalies and thereby identifying root
causes underlying customer complaints is in well underway. The stated
evaluation metrics as reference points for conversation mining will be key to
defining the success.</span></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<span style="font-family: "Arial","sans-serif";">Somjit Amrit is the Chief Business Officer of Technosoft Corporation</span></div>
<div class="MsoNormal">
<span style="font-family: "Arial","sans-serif";">He can be reached at somjit.amrit@technosoftcorp.com </span></div>
</div>
Technosoft Corporationhttp://www.blogger.com/profile/12361615608425186450noreply@blogger.com0tag:blogger.com,1999:blog-3981863964945784128.post-10968870533994448472013-10-06T20:23:00.003-07:002013-10-06T20:23:59.149-07:00The trickiness of forecasting rainfall<div dir="ltr" style="text-align: left;" trbidi="on">
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<b style="mso-bidi-font-weight: normal;"><span style="font-family: "Arial Unicode MS","sans-serif"; font-size: 12.0pt; line-height: 115%;">Believing<span style="mso-spacerun: yes;"> </span>in the weather guy <span style="mso-spacerun: yes;"> </span>to keep the golf course dry ? – Forecasting
rainfall could be tricky ..</span></b></div>
<div class="MsoNormal" style="text-align: justify;">
<br /></div>
<div class="MsoNormal" style="text-align: justify;">
<span style="font-family: "Helvetica Neue",Arial,Helvetica,sans-serif;"><span style="font-size: 12pt; line-height: 115%;">Forecasting rainfall,
and at a broader level, forecasting the weather is a problem fraught with
challenges. Around the world, various initiatives, such as, <i style="mso-bidi-font-style: normal;">Deep Thunder</i> by IBM and <i style="mso-bidi-font-style: normal;">Named Peril</i> by Climate Corporation, have
been undertaken to address this challenge. In India, the Indian Meteorology
Department and the Centre for Mathematical Modeling and Computer Simulation
have developed several solutions to address this problem.</span></span></div>
<span style="font-family: "Helvetica Neue",Arial,Helvetica,sans-serif;">
</span><div class="Default" style="text-align: justify;">
<span style="font-family: "Helvetica Neue",Arial,Helvetica,sans-serif;">The
Government of India recently announced a country wide competition, among
software companies to forecast rainfall among other similar topics by sharing
the historical rainfall and temperature time series data across the different
sub-divisions of the Indian subcontinent. </span></div>
<span style="font-family: "Helvetica Neue",Arial,Helvetica,sans-serif;">
</span><div class="Default" style="text-align: justify;">
<br /></div>
<span style="font-family: "Helvetica Neue",Arial,Helvetica,sans-serif;">
</span><div class="Default" style="text-align: justify;">
<span style="font-family: "Helvetica Neue",Arial,Helvetica,sans-serif;">The
opportunity to bring in Big Data and the related techniques to help forecast
rainfall is immense. Rainfall prediction helps in agriculture, irrigation,
disaster management during floods/droughts, and so on. Several attempts have
been made, primarily using statistical models, to predict rainfall. </span></div>
<span style="font-family: "Helvetica Neue",Arial,Helvetica,sans-serif;">
</span><div class="Default" style="text-align: justify;">
<br /></div>
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</span><div class="MsoNormal" style="text-align: justify;">
<span style="font-family: "Helvetica Neue",Arial,Helvetica,sans-serif;"><span style="font-size: 12pt; line-height: 115%;">Natural systems,
like atmosphere system, exhibit complex dynamical behavior and their data is
considered to exhibit nonlinear dynamical, even chaotic behavior. To add to
this rainfall data is more sensitive to temporal and spatial variations than
other climatic variables. The rainfall time series data, as was provided for
the said competition was used to train the neural networks capable of emulating
similar behaviors, including deterministic chaos.</span></span></div>
<span style="font-family: "Helvetica Neue",Arial,Helvetica,sans-serif;">
</span><div class="Default" style="text-align: justify;">
<span style="font-family: "Helvetica Neue",Arial,Helvetica,sans-serif;">So
what are the challenges in rainfall forecasting and why these are unique? Chaotic
dynamics is associated with extreme sensitivity to initial conditions,
exponential divergence of proximal trajectories and have very low
predictability horizons. Moreover, most of the variables in the atmospheric
state-space are not even measurable, giving rise to computationally intractable
behaviors. Hence it is difficult to model and predict atmospheric systems using
even higher-order multi-parameter statistical models. </span></div>
<span style="font-family: "Helvetica Neue",Arial,Helvetica,sans-serif;">
</span><div class="Default" style="text-align: justify;">
<br /></div>
<span style="font-family: "Helvetica Neue",Arial,Helvetica,sans-serif;">
</span><div class="Default" style="text-align: justify;">
<span style="font-family: "Helvetica Neue",Arial,Helvetica,sans-serif;">Therefore,
machine learning algorithms, specifically recurrent neural networks, capable of
emulating the nonlinear dynamical, including chaotic, behaviors have become
increasingly popular in the domain of rainfall prediction. </span></div>
<span style="font-family: "Helvetica Neue",Arial,Helvetica,sans-serif;">
</span><div class="Default" style="text-align: justify;">
<br /></div>
<span style="font-family: "Helvetica Neue",Arial,Helvetica,sans-serif;">
</span><div class="Default" style="text-align: justify;">
<span style="font-family: "Helvetica Neue",Arial,Helvetica,sans-serif;">Given
the advantages of recurrent neural networks in explaining the nonlinear
behavior between the inputs and output, the same were explored to forecast the
rainfall of 36 meteorological sub-divisions of India. The model used the past
years’ of rainfall data only, to forecast the monsoon rainfall of coming year. The
application presented would be providing the forecast for a rolling 18 months
period.</span></div>
<span style="font-family: "Helvetica Neue",Arial,Helvetica,sans-serif;">
</span><div class="Default" style="text-align: justify;">
<br /></div>
<span style="font-family: "Helvetica Neue",Arial,Helvetica,sans-serif;">
</span><div class="MsoNormal" style="text-align: justify;">
<span style="font-family: "Helvetica Neue",Arial,Helvetica,sans-serif;"><span style="color: black; font-size: 12pt; line-height: 115%;">A
number of algorithms, Elman neural network, Jordan neural network, Radial Basis
Function neural network, were applied in a competitive way to obtain the best
possible prediction accuracy. The performance of the algorithms was tested across
the different regions, categorized by aridity, as the underlying dynamics were expected
to change, influenced by geographical location, ocean currents, and so on.</span></span></div>
<span style="font-family: "Helvetica Neue",Arial,Helvetica,sans-serif;">
</span><div class="MsoNormal" style="text-align: justify;">
<span style="font-family: "Helvetica Neue",Arial,Helvetica,sans-serif;"><span style="color: black; font-size: 12pt; line-height: 115%;">I
firmly believe that given the strong emergence of the usage of machine learning
algorithms, this challenge of forecasting rainfall with increased degree of
accuracy would be a reality in the coming years, and we could believe in the
weather guy to keep the golf course dry.</span></span></div>
<div class="MsoNormal" style="text-align: justify;">
<br /></div>
<div class="MsoNormal" style="text-align: justify;">
<span style="font-family: "Helvetica Neue",Arial,Helvetica,sans-serif;"><span style="color: black; font-size: 12pt; line-height: 115%;">Somjit Amrit is the Chief Business Officer, Technosoft Corporation </span></span></div>
<div class="MsoNormal" style="text-align: justify;">
<span style="font-family: "Helvetica Neue",Arial,Helvetica,sans-serif;"><span style="color: black; font-size: 12pt; line-height: 115%;">He can be reached at somjit.amrit@technosoftcorp.com</span></span></div>
</div>
Technosoft Corporationhttp://www.blogger.com/profile/12361615608425186450noreply@blogger.com0tag:blogger.com,1999:blog-3981863964945784128.post-12315867068879025752013-09-23T00:55:00.001-07:002013-09-23T00:55:29.219-07:00Do the maverick mavens need managers?<div dir="ltr" style="text-align: left;" trbidi="on">
<strong><span class="font-size-5">Do the maverick mavens need managers?</span> </strong><br />
The
McKinsey and company report “Big data: The next frontier for
innovation, competition and productivity” (May 2011) is a well
publicized and circulated one on the internet .<br />
<br />
The report
projected that the demand for deep analytical positions in a big data
world in the United States could exceed the supply based on the trends
seen ( in 2011) , by 140,000 to 190,000 positions. While this was widely
referred to , in various reports, what intrigued me was the additional
information, tucked in there , which has been often ignored .<br />
<br />
The report
further suggested that there would be a projection of a need for 1.5
million additional managers and analysts in the United States who can
ask the right questions and consume the results of analysis of big data
effectively. All these add up to a number which will make anyone to sit
up and take notice. <br />
<br />
This brings up the moot question- does an
ensemble of Data Scientists need managers and that too in a proportion
as being mentioned. Who could be those managers, what would be the skill
and expertise and where would one get them from ? <br />
<br />
Let us take a minute to discuss how a Data Science project is different from a typical application development project. <br />
<br />
<strong>Data Science projects are different !</strong> <br />
A
Data Science project could be more akin to a typical R&D project
which would be more of a process of discovery. The discovery as an
output / outcome is expected to be more abstract and less definite . <br />
<br />
For
example, I was engaged in a Data Science project related to the area of
weather forecasting .This is an area where predictive analytics is
deployed , the deployment of the appropriate algorithms is the key to
yield the results with the right accuracy .The skill would lie on the
identification , selection and the deployment of the right approach, to
deliver the highest possible accuracy of prediction .<br />
<br />
The right approach
is a combination of the appropriate method of enriching the input data,
competing a set of most applicable algorithm(s), tuning the parameters
of the algorithm(s), ensuring that no over fitting is done and using the
right metric to test the performance of the algorithm(s).<br />
<br />
The
team structure in a Data Science team is necessarily a flat structure
.There is a reason. The roles are distinct and the level of peer
collaboration is intense. Designated Data Scientists would take up
defined role as per the “triangle of intelligence” namely aggregated
content ( the raw data ) , algorithm ( the thinking ) and reference
structures ( the domain knowledge base , ontology ) .<br />
<br />
An
application development project, more often than not, has a defined
output based on the requirements gathered and the business objectives
which have to be derived. Execution of such projects require a
hierarchical team structure with different types of work packets being
defined and allocated across functions (requirement gathering , design ,
development , testing , release management , change management
..warranty support) . The output here is often defined , stable and
possibly repetitive .<br />
<br />
So while a manager is the need in a hierarchical
setup, as in the latter case does one need one in a flat structure,as in
the former ? <br />
<br />
Going back to the moot question , “ the industry
would require “1.5 million of additional managers and analysts…. “ . Who
would constitute these managers? <br />
<br />
Let us analyze the work
elements that constitute a Data Science project and the team members for
a typical project. They would be : <br />
• Data Access ( immediate and unfettered access to data ) – Project sponsor mostly from the business side <br />
• Data Investigation ( defining the business problem and objectives ) – Business Analyst <br />
•
Data Preparation (constitutes parsing , cleaning , de/re-normalizing ,
linking , indexing , interpreting of data ) – Data Quality engineer <br />
•
Data Interpretation ( appreciation of the business process and the need
to work with subject matter expert for contextual understanding ) –
Subject Matter expert <br />
• Data Engineering ( connecting the dots between data sources , construction of quality algorithms , coding ) – Programmer <br />
•
Data Analysis ( ability to do statistical sense checking , statistical
validity of results ) – Data Scientist<br />
• Data Presentation (
communication of the nuanced message as discovered and help bridge the
gap between results derived and actions taken ) – Sr Data Scientist/
Manager <br />
<br />
So a typical project would have a core team of Data
Scientists with a surround team as mentioned. This could well attempt to
address the McKinsey’s estimate .<br />
<br />
<strong>True Blue Data Scientists are outliers and they stick to their ilk :</strong> <br />
<br />
Top
notch Data Scientists are hard to come by . These mavens, most of the
time are non conformists , individualists and are uncommonly precise .
Can we say they are mavericks ? However, having closely observed them , I
can safely conclude that they like to be around others of their ilk and
this is more out of a necessity .<br />
<br />
The subject is vast , deep and
to many arcane . It cuts across domains , statistics and computational
science . The inherent complexity brings with it, the need to use the
collective mind of the “commune” of data scientists. This could best
address the business problems by devising a repertoire of solution
approaches through a degree of intense and needful collaboration . <br />
<br />
In
such a situation where does then a manager fit in? How will the manager
provide value? As we have seen the projects are different from normal
application development projects and these projects are best run in a
nonhierarchical and flat structure. <br />
<br />
This field is evolving, the
eco-system is becoming more enabling as Data Scientists would be reached
out to solve complex business problems across industries .. <br />
<br />
If a manager has to play a valuable role, some areas that need to be considered are : <br />
1.
Given the “Triangle of Intelligence” , the manager should truly
understand the domain and the needs of the business and be the glue to
help interact between the business users and the data scientist plus
provide the wherewithal to the team to get the access to the input data,
the quality of which would be critical to the success of the project . <br />
2.
Have the ability to toggle between business jargon and the language of
data scientists. Understand the complexity of implementing a solution
and optimizing its performance; hence can estimate the time required to
implement the solution. <br />
3. Have the experience to disaggregate
the complex activities in Data Science projects and help integrate the
multiple models ( eg . combination of algorithms ) . <br />
4. Capable of providing the right inputs for innovation that is frequently required in this field.<br />
<br />
Then
the question is – would Data Science managers grow from within the
“commune” of Data Scientists , or could they migrate from the business
domain ? We will get the answer as we see more mainstream activities in
this area in the coming months . <br />
<br />
Managing is about coping with
complexity, but leadership is about coping with change ? Given that Data
Scientists are already groomed to manage complexity ( and hence could
manage themselves ) , do we need then Data Science leaders? May be the
need of the hour is to lead rather than manage .<br />
<br />
Somjit Amrit Chief Business Officer, Technosoft Corporation <br />
Somjit.amrit@technosoftcorp.com </div>
Technosoft Corporationhttp://www.blogger.com/profile/12361615608425186450noreply@blogger.com0tag:blogger.com,1999:blog-3981863964945784128.post-71447845578728668512013-09-16T18:48:00.001-07:002013-09-16T18:48:12.378-07:00Do not stifle the questions visualized data raises!<div dir="ltr" style="text-align: left;" trbidi="on">
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<br />
<div class="MsoNormal">
<b style="mso-bidi-font-weight: normal;"><span lang="EN-IN" style="font-size: 12.0pt; line-height: 115%; mso-bidi-font-size: 11.0pt;">Do not
stifle the questions visualized data raises!</span></b></div>
<div class="MsoNormal">
<span lang="EN-IN">Extracting meaningful insights from data to
address business needs has benefited immensely from the availability of data
visualization <span style="mso-spacerun: yes;"> </span>tools that have <span style="mso-spacerun: yes;"> </span>data more approachable. Today the
proliferation of off-the-shelf tools, which are easy to learn and are web
enabled, have democratized the way data is presented and consumed. Tools like Spotfire,
Tableau, Qikview have helped breathe life into data. They provide a
professional look and feel and give an inherent feel of fidelity of the data
that is being visualized, more than when data is simply presented as text .</span></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<span lang="EN-IN">Well designed and deployed Data
visualization many a time could lead the user to a question which could spark
the need for deeper insights and which possibly cannot be answered by the
visualization software. But before we delve into this problem let us understand
the primary goals of data visualization</span></div>
<span lang="EN-IN">Essentially <b style="mso-bidi-font-weight: normal;">good</b> Data Visualization is expected to<span style="mso-spacerun: yes;"> </span></span><br />
<br />
<ul style="text-align: left;">
<li>Provide the interpretation of the data <span style="mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;"><span style="mso-list: Ignore;"><span style="font: 7.0pt "Times New Roman";"></span></span></span> </li>
<li>Bring in relevance and context to the data <span style="mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;"><span style="mso-list: Ignore;"><span style="font: 7.0pt "Times New Roman";"></span></span></span> </li>
<li>Reveal elusive insights to spark deeper analysis</li>
<li><span style="mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;"><span style="mso-list: Ignore;"><span style="font: 7.0pt "Times New Roman";"></span></span></span>Drive<span style="mso-spacerun: yes;"> </span>management<span style="mso-spacerun: yes;"> </span>by exception <span lang="EN-IN"> </span></li>
<li><span lang="EN-IN">Embed intelligence in the reports </span></li>
</ul>
<br />
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<span lang="EN-IN">Recently we
had an opportunity to work on a project with the objective to understand and
analyze the failures in a telecom network, and the related quality issues which
could have direct bearing on customer churn apart from the repair and service
costs. The goal was to enhance the quality of service (QoS) of the telecom
network provider using predictive analytics. The added expectation was to
enable the service manager to take proactive decisions on repairs using machine
learning models. </span></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<span lang="EN-IN">We developed dashboards for the equipment
maintenance manager, using a well regarded <span style="mso-spacerun: yes;"> </span>commercial off-the-shelf visualization tool.
We also developed the advanced failure prediction model using the input data as
the standard telecom equipment log files and used techniques such as <span style="mso-spacerun: yes;"> </span>pattern mining and event sequence analysis to
predict equipment failure. <span style="mso-spacerun: yes;"> </span><span style="mso-spacerun: yes;"> </span>R open source programming language was used to
create this model. </span></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<span lang="EN-IN">We had <span style="mso-spacerun: yes;"> </span>two options to present this data. In one
scenario the maintenance manager used the visualization tool to drill into the
failures to locate the regions or equipment models with high failure. This
information was then shared with the engineering team for root cause analysis who
used our R based model to predict failure. </span></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<span lang="EN-IN">The visualized data provided
information to act on, but was it intelligent enough to bring in preventive maintenance?</span></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<span lang="EN-IN">The manager had a number of questions <span style="mso-spacerun: yes;"> </span>during such slice and dice analysis, namely - Why
is a region doing better than others? Why are some failures more common in one
model and a particular region and not the others? Unfortunately, simple data visualization
cannot provide answers to these questions and they get stifled. Consequently
the service manager hopes that his engineering team will come up with the right
answers. Many a time, the well represented and slickly visualised data is
“counter-productive” by making the user numb to the questions which could get
triggered.</span></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<span lang="EN-IN">Our approach was to integrate the relevant
prebuilt sequence mining models in R and integrate it with the off-the shelf visualization
tool.<span style="mso-spacerun: yes;"> </span>This approach immediately gave the
manager the freedom to ask even deeper questions about the state of equipment
he managed.</span></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<span lang="EN-IN">In the new approach the manager did not
send the information to his engineering team for analysis but felt empowered to
do the same . <span style="mso-spacerun: yes;"> </span></span></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<span lang="EN-IN">Once the region associated in the problem got
identified, next , running the advanced sequence mining algorithms, identification
of the frequently occurring patterns in the repair history were <span style="mso-spacerun: yes;"> </span>carried<span style="mso-spacerun: yes;">
</span>out.. The patterns in data showed that two components were failing in tandem.
While the short term measures would be generally to replace the defective parts,
but more importantly the findings were passed on to product engineering team to
redesign the part which would be more fault tolerant.</span></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<span lang="EN-IN">Here is an example of using the power of R aided
by the inherent strengths of good visualization tool to build intelligent and <span style="mso-spacerun: yes;"> </span>actionable data visualization.The service
manager did not have to leave his data visualization<span style="mso-spacerun: yes;"> </span>environment nor wait for the engineering team
to do background analysis. </span></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<span lang="EN-IN">I would like to conclude that in data visualization,
there is more to the choice of representation of data only. Data visualization
should not make the user numb with its slick representation, but should help<span style="mso-spacerun: yes;"> </span>in revealing those elusive insights by
goading the user to ask questions which he would have <span style="mso-spacerun: yes;"> </span>never asked.<span style="mso-spacerun: yes;">
</span><span style="mso-spacerun: yes;"> </span></span></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<span lang="EN-IN"><span style="mso-spacerun: yes;">by Somjit Amrit</span></span></div>
<div class="MsoNormal">
<span lang="EN-IN"><span style="mso-spacerun: yes;">Somjit is the Chief Business Officer of Technosoft Corporation, an IT Outsourcing Services Provider</span></span></div>
<div class="MsoNormal">
<span lang="EN-IN"><span style="mso-spacerun: yes;">He can be reached at somjit.amrit@technosoftcorp.com </span></span></div>
</div>
Technosoft Corporationhttp://www.blogger.com/profile/12361615608425186450noreply@blogger.com0tag:blogger.com,1999:blog-3981863964945784128.post-33193744741120558132013-09-10T02:35:00.005-07:002013-09-16T18:49:07.476-07:00“The Purple People” - The conundrum of finding business expertise among Data Scientists<div dir="ltr" style="text-align: left;" trbidi="on">
<b>“The Purple People” - The conundrum of finding business expertise among Data Scientists.</b><br />
<br />
Over the last several months, as I looked at addressing the business needs across various industries as someone leading a team of Data Scientists, the question of domain expertise invariably cropped up.<br />
<br />
Attending one meeting with a Pharmaceutical company, I was posed with the question of, "Have you done work in the areas of Rare Signal detection?" In a similar vein, while preparing for a meeting with an Auto finance major, the question was in the area of using Auto telemetry data and deploying it to work on fraud detection in auto-insurance claims.<br />
<br />
Multiply the business problems with the numerous industries and the enormity of the challenge becomes apparent. More so since it may not be possible to be a domain expert in every possible industry. Which begs the question, is there a line between domain knowledge and domain expertise? <br />
<br />
While domain knowledge would refer to the appreciation of the industry, its business processes and challenges, domain expertise is expected to be much deeper. It would be addressing meaningfully, through the effective use of technology, a problem or two which a particular industry is grappling with.<br />
<br />
Is the expectation then from the prospective customer 'You need to understand my industry language (i.e. domain knowledge)' or is it 'You need the expertise to solve an industry specific problem (i.e. domain expertise)'?<br />
<br />
How then does one manage the expectations here? A typical Data Scientist needs to be adept in understanding the business problem, have a good handle of the data on hand, and have a grasp of the algorithms which would aid him/her in the journey of discovery, design, deployment and ultimately delivering the results .<br />
<br />
These would come in various shades across Data Scientists. As the area becomes mainstream at a furious pace, primarily driven by storage and accessibility costs, the need to balance out the three, otherwise known as the “Triangle of Intelligence", namely business (knowledge or expertise), data (content) and algorithm (thinking) will possibly decide the difference between resounding success and abysmal failure, while addressing a business problem.<br />
<br />
I came across an interesting note by Caitlin Garrett in the <a href="http://www.blogger.com/blog%20http://rapidinsight.blogspot.in/2013/06/data-scientists-next-generation.html" target="_blank">blog http://rapidinsight.blogspot.in/2013/06/data-scientists-next-generation.html</a><br />
<br />
Caitlin rightly mentions that as a practitioner of Data Science, it is mandatory to have analytical thinking, mathematical/statistical ability, a knack for communicating results to non-data people, and creativity. Her blog however does not make a reference to the business (knowledge or expertise).<br />
<br />
Can we surmise then, that the ability to articulate and appreciate the business problem and marrying the expected ways to get this addressed through analytical skills could be a good starting point to provide that confidence to the business user that the problem at hand can be addressed? <br />
<br />
The combination of business acumen and technical skill isn’t easy to come by.<br />
<br />
David Logan in his interesting blog:<br />
<a href="http://www.itweb.co.za/index.php?option=com_content&view=article&id=63781" target="_blank">http://www.itweb.co.za/index.php?option=com_content&view=article&id=63781 </a><br />
<br />
has mentioned about the "Purple People", folks who are blessed with the business acumen and the analytical abilities. Purple is the blend of Red (Business acumen) and Blue (Analytical abilities) and we would by now know that this would be a very hard to get profile. <br />
<br />
However as this area matures and moves beyond the hype cycle, we may have the luxury of seeing experts who cover both areas well.<br />
<br />
The question still remains. The profile as described is rare but the expectations of the prospective customers most of the time is "Can you solve the point problem which I have been grappling with for so long?" for which domain expertise and NOT knowledge would be required.<br />
<br />
The only way to address this is to borrow the domain expertise from the prospective customer and build that from the base of domain knowledge one is expected to have and move forward. <br />
<br />
But as one is trying to demonstrate credibility to the prospect , this may be easier said than done.<br />
<br />
<br />
<div class="MsoNormal">
<span lang="EN-IN"><span style="mso-spacerun: yes;">by Somjit Amrit</span></span></div>
<div class="MsoNormal">
<span lang="EN-IN"><span style="mso-spacerun: yes;">Somjit is the Chief Business Officer of Technosoft Corporation, an IT Outsourcing Services Provider</span></span></div>
<div class="MsoNormal">
<span lang="EN-IN"><span style="mso-spacerun: yes;">He can be reached at somjit.amrit@technosoftcorp.com </span></span></div>
<br />
<br /></div>
Technosoft Corporationhttp://www.blogger.com/profile/12361615608425186450noreply@blogger.com1tag:blogger.com,1999:blog-3981863964945784128.post-61255442763564003882013-09-09T23:58:00.002-07:002013-09-09T23:58:48.122-07:00<div dir="ltr" style="text-align: left;" trbidi="on">
<b>Welcome to Technosoft Corporation's blog</b><br />
<br />
On these pages, you will find the thoughts, the musings and the ideas of our employees - who come from varied backgrounds but driven by a mission to solve our client's challenging technology problems.<br />
<br />
So welcome and we hope to see you often.</div>
Technosoft Corporationhttp://www.blogger.com/profile/12361615608425186450noreply@blogger.com0