Boon from big data or loss of privacy?
Today’s post is going to be different.
There is no technical subject matter I am going to talk about. But the article is far more thought provoking than any of the article I have written till date.
[stextbox id = “section”] A real life incident:[/stextbox]
Let me start with a real life example to get your thinking process started:
About 6 months back, I bought a top end Android smartphone. After using it for a month or so, I accidently started Google Now on the phone. The interface looked very simple on first look (nothing more than a search bar and weather update). So I moved back from the application and started living my usual life.
I would have almost forgot this instance like multiple other applications which come with the phone and I don’t use. However, Google had something else in store. A week after I opened the application for first time, I got a notification on my home screen, suggesting that I am 15 minutes away from Home and the traffic on route is normal!
The notification took me by surprise. I never told my phone where my home is! Over the next few days, the application identified my Office, commute place, friends place, the websites I visit frequently. It now integrates my searches across devices. So if I search a restaurant on my laptop, my phone shows me the route to same restaurant!
The incident above is like a dream come true for a lot of analysts and a scary incident leading to loss of privacy to a lot of customers.
As an analyst and some one who specializes in predictive modeling, I am usually a proponent of big data and the changes it is bringing to our day to day life. However, I have to admit that Google took me by surprise and has made me think and reflect a lot more on how life is changing. It has ensued a debate between 2 sides of my personality.
[stextbox id = “section”]Two sides of debate:[/stextbox]
My first personality is that of a common man. I want my privacy, specially during some personal moments. These moments could be the time I spend with my family or when I am reading or may be talking to a friend. I don’t want interruptions or suggestions from any third party during this period. I want to relish the moment as it is. After going through the experience mentioned above and many more like that, I am not sure whether these moments will remain as pristine and unadulterated as I would want them. Would my reading experience be marred by suggestions about different things I might like? Would the phones pop up notifications about my friend when I am talking to them? or may be when I am talking about them to my wife? The possibilities are limitless!
The other side of my personality is a big proponent of technology and Analytics. I remain excited about how technology can be used to solve day to day problems. I come out with innovative ways of using data to create value (for customers as well as Organizations). I continuously think how behavioural modeling can help customers in breezing through day to day chores? How can I predict something before it actually happens.
[stextbox id = “section”]How do we resolve this?[/stextbox]
After a lot of thought and internal debate, I have come to a conclusion that we are standing at that juncture of time when both these personalities need to change. The first personality needs to be aware about the data trails I am creating and actively cut them out if I don’t want them. So, I have stopped carrying my smartphone when I play around with my daughter. I need to be aware that nothing gets erased from this new world we are living in, be it a comment on Facebook, a status update, my geo-location map (through the mobile device I am carrying). It is only a matter of next version of Android roll out before my calls get searched for what I am looking out for and I get app suggestions based on that.
The second personality needs to be cognizant about the presence of first personality and take actions which are in sync with values of first personality. Here are some rules I have come out with, which every analyst needs to keep in mind while designing a product or working on his next big data project:
[stextbox id = “section”]1. Transparency:[/stextbox]
This is the biggest takeaway. The bare minimum an analyst needs to make sure is that the customer is aware about what data is being collected and how can this be used. This needs to come out clearly. This is similar to apps (on smartphones) asking permissions before installing them. If you are collecting data with out asking customer explicitly, you are headed for disaster.
So, instead of using data through a pre-selected tick box (buried somewhere is my phone settings), I would have appreciated if the app reminded me of the data it will use, when I started it for the first time.
[stextbox id = “section”]2. Develop a character of your Organization by keeping customer at the heart:[/stextbox]
Let me try and explain. Years before Google started collecting information about usage from Android phones, Microsoft started this for MS Office. They asked me whether I would want to share my usage patterns with Microsoft, which will help them improve user experience further. I almost always declined. When Google asks me same thing, I am more open to sharing information.
It might be a personal choice. However, the reality is that I am more open to sharing data with Google because I can relate to the benefits they have provided me by using this information. I have benefited by sharing some of this information with Google.
The message is that if you don’t provide the benefit of this information back to the consumer, they will stop sharing this information.
[stextbox id = “section”]3. Make change in subtle manner:[/stextbox]
Big changes in user interface or the way new product gets rolled out can take customer by surprise. You have to build in these changes in subtle manner. In a way such that the customer still feels as much at home as possible. I think Google does a nice job at it. Here are some best practices:
- Provide an option to user to switch back to old proposition, if it is not working for him
- Roll out changes slowly and in steps. Recently Google organized Gmail in different tabs, a move which benefited me by making sure I spend maximum time on relevant mails. But they also started to show ads in one of the tabs. I thought it was a smart way to test. If customers feel distracted by these ads, they will pull them out. If customers get used to them, they can roll them out to different tabs.
- Try and keep as much user interface unchanged as possible.
[stextbox id = “section”]4. Test and roll-out:[/stextbox]
Irrespective of how good an idea is, you should avoid making complete roll-outs without testing. There are multiple benefits from this:
- You actually act based on how customer feels about the product
- You can size the benefit / loss you have seen by moving to a new product.
I think until and unless Organizations and analysts adhere to these rules, it might only be a question of time before they face a bunch of disgruntled customers.
What do you think about these rules? Are there any other rules, you think Organizations should follow? Or which you follow while rolling out your big data projects. Do let me know your thoughts through comments below.
Image 1 credit: tnooz.com
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