Daniel's blog

I have recently recalled a conversation I had with my German boss in Grameen Creative Lab in Colombia. He gave me a very powerful insight, which he himself heard from his previous boss, about priorities in life, which I would like to share with you.

We are often juggling with many balls at the same time. Some of them, such as our professional life, are made of rubber and bounce back if we drop them. But other ones, such as our relationships and health, are made of glass and can shatter into many small pieces if we let them fall on the ground.

I would also add our own pride amongst the rubber ones.

I guess it is more easily said than done, but next time you are juggling with your priorities again, be careful not to drop the fragile glass balls.

I am now working in Moscow as a facilitator in an English speaking club which means I have lot of interesting conversations on a range of issues with local Russian people. Sometimes we continue these discussions even after club meetings. Couple days ago, I was talking to a physicist in his fifties who recently changed a job and now works as a sales manager for a Swiss engineering company. Being interested in my master’s degree which I will be starting soon, he said that he would like to share with me two big discoveries he made throughout his life. I was expecting something highly theoretical but instead I got two very practical advices, which can hardly be explained using mathematics and which on first glance might seem rather obvious but are actually very powerful and often not used.

He made the first one right after finishing his degree: If you are thinking about somebody, that person is usually thinking about you as well.

The second one occurred to him only three years ago, as an evolution of the first one: If you are thinking about somebody positively, the other person will think about you positively as well and vice versa.

On reflection, I realised that I have observed these phenomena many times before as well but I have never stopped to think about it and to formulate it. And they are so true!

Try recalling some situations as well. Think about when you approached somebody in a bad temper, feeling hostile towards that person. Chances are the person responded in a similar fashion, even if what you actually said was perfectly polite. Now think about a situation when you came to somebody thinking about that person really positively. A big difference, right?

I know one Canadian girl who is always happy, smiling and talks to people in a really positive way. Even if she is asking them to work harder or giving bad news, people react to her very well and generally tend to agree with whatever she says. I have always been admiring her for that but have never realised that the reason for such reception might be as simple as thinking about people in a positive way.

Try it, it works

Data on its own have only limited usefulness. It is only when we interpret them as information that we can understand what they actually mean. Consider a company knowing somebody’s date of birth. Unless we know also the date of this year and we are able to subtract those two to get the age of the customer, the data would be useless. Once translated into information (age), the company can tailor its proposition by e.g. offering a youth discount. That was easy, but how to make sense of huge amounts of seemingly unrelated data organizations are amassing these days?

The Economist published in May a very interesting article on big data and new businesses focusing on this market. Especially big banks are making use big data to detect fraudulent transactions. They collect lots of data about each transaction such as the amount, date, location or name of the sender. If everything was ok, the data would have certain patterns and would not deviate from them in the short term in a specific way. People would be depositing certain amounts of money in city A and sending e.g. 3% to city B in another country. Sometimes there are exceptions, but those happen on random basis. When analysing big data, companies therefore focus on deviations from standard patterns and focus on new emerging ones. If there are suddenly lot of transactions from the one city to another one, it raises a red flag to be investigated.

It’s all about patterns

I was reading this article when I was still living in China and thought how else can this concept be used. And I came up with one example. I lived in a city called Suzhou and every Friday evening took a train ride to Shanghai and returned back late on Sunday. When buying tickets I had to show my passport and they noted its number. If I was working in China on an incorrect visa, I would be in trouble. As an application of big data analysis, the Chinese authorities can for example track passport numbers of train passengers and look for patterns where there should be none. One would not expect somebody with a tourist visa to be making regular trips over two months – such person should be travelling randomly across the country. Unsure of how sophisticated they are I started using my driving license which looks similar to Chinese id card instead of my passport every now and then, just to be on the safe side.

Can you think of some other situations where you can find patters and how this information can be used?