UX14 - Is Big Data Killing Customer Experience? (Arun Jawarlal)

In today's Big Data world, many technologies have been introduced to understand, analyze, slice and dice data. There also have been new genres of professionals supporting big data (Data Scientist). This raises some important questions - Are we (UX) doing enough to channelize Big data into Dashboard user experience? Are we helping Customers view data precisely? Is big data being converted into big visualizations and big dashboards rather than simple visualizations and Simple Dashboards? Are Users/Customers overwhelmed by the Big data?
There are so many questions lingering in the minds of people. We have a mammoth elephant in the living room and people are too scared to act. They choose to ignore the elephant rather than trying to deal with the problem.
We as User Experience professionals have always had a responsibility to raise questions on ROI (Return on Investment) of organizations. We have been paramount in saving millions of Dollars being squandered into IT where users have never been given key importance. We all have a new responsibility - Taming Big Data.
This concept proposes a 5 step process for Big Data UX and a new skill set for Big Data UX.

Transcript

1. Is Big Data Killing Customer Experience? Arun Jawarlal, Lead – Usability & UX Research
2. iNautix - A BNY Mellon Company iNautix Technologies India Private Limited is a group company of Bank of New York Mellon - a leading financial services provider. We provide technology development, business & technology operations and remote infrastructure management services for BNY Mellon and its subsidiaries. iNautix also develops and delivers comprehensive technology solutions and software development products for customers of BNY Mellon. Leveraging the resources based in Chennai and Pune, India, our parent organization BNY Mellon and other subsidiaries benefit from the proven track record of our more than 5000 consultants, analysts, and technologists.
3. The Big Dashboard story
4. This is Mr. John Doe, Product Manager of ABC Financial Corp., This travel with John Doe is going to show one of his Experiences with the Dashboards!
5. Mr. John Doe has a new Exercise : STEP 1 : To find out which is the Most Time Consuming Repetitive Activity in his Application STEP 2 : To introduce an Interaction Feature to make it more Productive
6. So his team gathered data to come up with a Data Analysis Dashboard of the Most Time Consuming Repetitive Actions on the Portal. Mr. John Doe wanted his team to analyze the Data and give him insights on the highest used feature/function. This would enable him to make informed decisions.
7. This was the Dashboard presented to Mr. John Doe Data Analysis Dashboard Home Page Transaction Account Setup Reports Insights Issue Management Alerts Login Navigation Activity 0 5 10 15 Mon Tue Wed Thu Fri Sat Sun Mon Tue Wed Thu Daily Transaction Chart Website Portals Vs. Usage Analytics Portal Name Time Spent # of Clicks Freq. Buttons Market Status 200 Mins 45 Today, Compare Stock Value 230 Mins 44 Present, Change Market Analysis 220 Mins 37 Compare Stock, News Stock Profile 100 Mins 32 Watch list, Buy/Sell Compare 220 Mins 28 Change, Buy/Sell News 107 Mins 21 Today, Weekly Rates 190 Mins 35 Buy/Sell
8. “ Can someone Walk me through this?“, asked Mr. John Doe Home Page Transaction Account Setup Reports Insights Issue Management Alerts Login
9. What is Big Data?
10. Evolution Timeline of Dashboards
11. What is BIG DATA? Big data is collection of Data sets so Large and Complex that it becomes difficult to Process using Traditional Processing Tools. Technologies D3.js Google Charts Visualizing.org Hadoop MapReduce
12. Some scary BIG DATA revelations • Gartner predicts that by 2015, we need 4.4 Million Data Scientists • IDC research states that more than 70% (900 Exabytes- 880Billion GB) of the data on the Internet will be User Generated in 2010 • Intel suggests that 44% of those who are not analyzing unstructured data expect to do so in the next 12 to 18 months • EMC Report states that Digital universe will reach 40 trillion gigabytes by 2020 • IDC also states that only 1% of the world Data is being analyzed currently Source : Multiple
13. FACTS ABOUT BIG DATA 2.7 Zeta bytes 235 Tera bytes Digital Universe U.S Library of Congress Walmart Database 2.5 Peta bytes 100 Tera bytes Data Daily Uploaded in Facebook 571 New Websites Created Every Minute of the Day Source : Wikibon
14. BIG DATA VISUALIZATION 2005 2006 2007 2008 2009 2020 2010 2011 Search Engines Social Media Producers Other websites 6.6 Zettabytes (Approx.) Social Big Data : Type of Data collected from Social Media is approximately 500GB per second
15. Is Big Data Visualization really confusing?
16. DATA VISUALIZATION METHODS
17. LETS KNOW A CHART What is the name of this Chart? Node Chart! What can it represent? Network Nodes / Connections Where can you use it? Relationship Mapping
18. LETS KNOW A CHART What is the name of this Chart? Tree map What can it represent? Proportions Where can you use it? Budget Planning, Financial Data
19. PUSHING BIG DATA INTO DASHBOARDS Technology has no provision to simplify big data. The Technology-inclined visualization methods are an 1-1 mapping of data
20. FROM BIG DATA TO DASHBOARDS www Database Social Networks Emails Big Data Big Data platforms Dashboards Data Analysts BI Analysts Data Sources Hadoop D3js Charts “Are we missing something?”
21. FROM BIG DATA TO DASHBOARDS www Database Social Networks Emails Big Data Data Visualizations Dashboards Data Analysts BI Analysts Data Sources Hadoop D3js Charts Visualization Expert Visualization Expert
22. Visualization Expert Data Visualization Methods Domain Knowledge User Experience Best Practices Big Data Technologies
23. BEST PRACTICES OF BIG DATA Gather Business Requirements before gathering Data! Use Agile and Iterative Approach to Implementation! Evaluate Data Requirements! Go for Big Data Visualization prototyping tools Associate Big Data with Enterprise Data! Design for Volume, Velocity, Value and Variety
24. BEST PRACTICES OF BIG DATA UX Gather Business Requirements before gathering Data ! Use Agile and Iterative Approach to Implementation! Evaluate Data Requirements! Go for Big Data Visualization prototyping tools Associate Big Data with Enterprise Data! Design for Volume, Velocity, Value and Variety Gather User requirements directly from Consumers and Product owners Pitch for Story Boards and User Narratives. Build good stories Transform Data Requirements into Taxonomy Let us try Simplicity first Associate Big Data with Enterprise Business Objectives Design for Scalability, Priority, minimalism and Usability
25. The Big Data UX Cycle
26. Perception Processing* Parallel Processing Pattern Perception Goal Processing Source : Colin Ware(2004) 1 2 3
27. BIG DATA UX Cycle Dream big Dissect elements Define Patterns Design for Decisions Dissent Solution
28. We have Ms. Jane Doe who is also a Product Manager of the Competitor company of ABC Corp., tries to Find the most time consuming and repetitive tasks in the Portal and compete in the Market with an Improved Interactivity.
29. Ms. Jane Doe advised her team to analyze the Customer Behavior in the Trading Portal and come up with a Dashboard containing the over all picture of the Analysis! The team analyzed the Data and along with the UX, came up with the Dashboard portraying the Analysis Information for Ms. Jane Doe
30. This was the Dashboard presented to Ms. Jane Doe 321 No. of Users 240 Mins/User Most Time Spent in a Page Most clicked Button/Icon 2035 Times Data Analysis Dashboard Top 5 Screens Trading Insights Issue Management Reports News 0 500 1000 1500 2000 2500 Buy/Sell Profile Trade News Insights Frequently Used Button TRANSACTIONS BUY/SELL Transactions
31. Likelihood of People Transactions Insights Issue Management Reports News Top 5 Screens “ I think we now know the Frequent Actions performed by Users in our Portal. Lets try to enhance it and make it more easy for the Users!“, says Ms. Jane Doe
32. What do we infer from their Experiences? Ms. Jane Doe Mr. John Doe Use of Big Data Lacks Insights Technology Centric Use of Key Data Decision Centric User Centric
33. With UX intervention, Big Data dashboards can reach a higher level of maturity!
34. Thanks Reach me @ arunjawar@gmail.com