What are your users doing on your website or in your store? How do you turn the piles of data your organization generates into actionable information? Where do you get complementary data to make yours more comprehensive? What tech, and what techniques?
The Fifth Elephant is a two day conference on big data.
Early Geek tickets are available from fifthelephant.doattend.com.
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Each community member can vote for or against a talk. A vote from each member of the Editorial Panel is equivalent to two community votes. Both types of votes will be considered for final speaker selection.
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Describe how to use something that is available under a liberal open source license. Participants can use this without having to pay you anything.
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Buy a slot to pitch whatever commercial tool you are backing.
Speakers will get a free ticket to both days of the event. Proposers whose talks are not on the final schedule will be able to purchase tickets at the Early Geek price of Rs. 1800.
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The Fifth Elephant - known as one of the best data science and Machine Learning conference in Asia - has transitioned into a year-round forum for conversations about data and ML engineering; data science in production; data security and privacy practices. more
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Anannya Deb
@anannyadeb
From Data to Action (in Retail Banking)
Submitted May 24, 2012
Section:
Industry & Business
Technical level:
Intermediate
Session type:
Lecture
The promise of data (and all the technologies, systems, processes, etc. related to it) for many years has been a world where people are empowered to lead successful lives. Customers get the right products and services, organisations become more profitable, governments become more responsive to their citizens, markets become more precise and predictable and so on and on.
It may be too much of a generalization to say this promise has remained unfulfilled. There are many areas where it has definitely led to a transformation in society. The Information Age has opened up opportunities and possibilities for people. But has it really raised the quality of life of everyone? Has it made people wiser? Has it made people more confident and capable?
Or has it made people dumber? Has it given people a convenient scapegoat when things go wrong? Has it made people stop thinking?
Outline
Let’s take an example.
Consider a retail bank offering savings products to its customers. It collects all kinds of data – the customers, the transactions they do, the amounts of money they keep in their accounts. Let’s take a random piece of data.
Customer x001: ATM user: Yes
Customer x002: ATM user: No
Now what?
By itself this piece of data means nothing beyond what it is. To make anything from it, one needs something else – a purpose. I must want to do something for which this piece of data can do something.
Purpose is context dependent. If I am in charge of ATM usage at the bank, my purpose is to increase the number of people using ATMs. For this I need to make the ATM services more customer friendly. But what is “customer-friendly”? And how will this piece of data help me?
The point being made here is that generating the data report is not the end of the process. It is not even the beginning. It is a small input into a larger context. In the above example, the ATM Manager will, most probably, follow a chain of thinking that goes a bit like this:
Who are the customers who are using the ATM? What do they find attractive about it?
Who are the customers who are not using the ATM? What is holding them back?
From the random piece of data, the ATM manager needs to extract some relevant information and from that perceive a critical insight that can take him closer to a solution. For example:
Profile of ATM non-user:
77% are senior citizens
68% are rural customers
44% are women
23% are High Networth Individuals (average account balance > 500,000)
The above is information gleaned from churning the data. But what useful insight can one draw from it? For this the ATM Manager needs to go beyond the data and study the human beings. An example:
Women are scared of entering ATMs alone. They look at the ATM kiosk as a place where they may get robbed, attacked, etc. They prefer to go to a branch where there is safety in crowds.
Now, the ATM manager has something concrete that he can act upon:
Make ATMs safer for women – relook at lighting; location; behavior of security guards, etc.
Thus, we have a journey from a random piece of data to concrete action points. This journey is what fulfills the promise of data. And this journey happens not in the computer but in the mind of the user. There is a thinking process that works backwards from the purpose and asks the right questions, supplements the data with appropriate additional information and designs hypotheses for action.
How do we enable the user to make this journey each time, every time?
Requirements
It will be an interactive lecture. Participants should come with an open mind ready to challenge conventions
Speaker bio
I work as a Knowledge Practices Specialist in Illumine Knowledge Resources Pvt Ltd. We work on ways in making knowledge enabling to people in diverse contexts - government, corporate organisations (banking, financial services, oil retailing, software), education, health, social settings, etc. I have been working here for 6 years. Prior to that I was in advertising, Internet marketing, IT marketing and CRM spaces. I have done my post graduation from IIM Bangalore (1998) and graduation from St Xaviers Mumbai (Mumbai University)
Comments
Hosted by
The Fifth Elephant - known as one of the best data science and Machine Learning conference in Asia - has transitioned into a year-round forum for conversations about data and ML engineering; data science in production; data security and privacy practices. more
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