Jer Thorp's work focuses on adding meaning and narrative to large amounts of data to help people understand and control the information around them. There are six ways to make data more human: use human insight to frame problems, remember that more data is not always better and can find false correlations, account for human biases and self-deception in data, understand that context is important, embrace that data can help abandon stereotypes, and realize that stories told by robots lack human emotion. These insights are relevant for managers in India because collecting and storing vast amounts of personal data overseas risks privacy violations and data access by foreign governments or companies that could affect a nation's policies.

1. Make data more human
Debashish Jana

2. Jer Thorp’s work focuses on adding meaning and narrative to huge
amounts of data as a way to help people take control of the information
that surrounds them.

3. Six Ways to Make Your Data
More Human
There is no doubt that data has fundamentally changed the nature of
marketing. Not only are we able to target audiences with increasing
precision, but the shift to data-driven methods has transformed our
ability to understand consumers. We can deliver more, and are expected
to deliver more, making work in this industry both exciting and daunting
at the same time.

4. Sadly, this data revolution has been so hyped that we risk thinking it is
the answer to everything. Data has been hailed as a way to predict
terrorist strikes, cure cancer, or even solve the age-old problem of
knowing which half of your marketing budget is wasted. But data is
neither omnipotent nor infallible.

5. For a start, it takes people to design the systems that collect and
organize data. It takes people to understand the limitations and biases
of these systems; and it takes people to focus data on the right
questions that can lead to meaningful and actionable insight.

6. Use human insight to frame
the problem.
Data doesn't ask questions. In many ways, the first few steps of any
inquiry are the most challenging. The wrong choice of variables, poor
instrumentation and measurement, or an imprecise question come with
a high cost. No amount of automation can correct these missteps.

7. Remember that bigger is not
always better.
Massive amounts of data defy the limits of human analysis, which is
why machines are essential to understanding large amounts of
information. But increasing the volume of data is only useful if it
serves to improve the ratio of signal to noise. More data also means a
greater risk of finding false correlation, or conclusions that aren't
relevant or actionable. A machine can find any number of answers,
but it takes a human to discern treasure from trash.

8. Know that everyone is lying.
To put it more gently, people are masters of self-deception.
Unlike weather patterns or traffic data, information that
people volunteer is always biased in some way. People distort
the truth about all kinds of things -- sometimes directionally,
as in how much they earn, and sometimes in unpredictable
ways, such as how they feel about a product they know
others like. This is yet another problem a machine can't solve,
but experience and human judgment can. It is also why
passive observation is often the best way to gather data.

9. Understand that context is
everything.
The events that are captured and recorded in our data are
almost impossible to understand without knowing the
context in which they were collected. The same action, even
by the same person, can mean wildly different things.
Purchase of a children's toy at a supermarket or drugstore
often indicates a child is present -- unless it is December,
when the holidays play havoc with shopping patterns. The
same product purchased online is usually bought by an adult
without children. And if the toy is purchased in a store
outside a consumer's home area, there is likely to be a guilty
parent traveling alone at the register.

10. Embrace the idea that data
forces us to abandon
stereotypes.
This one almost works backwards. Robots struggle to recognize patterns,
while the human brain revels in the process. That's not always a good
thing. Our minds adapt to poor or incomplete data by filling in the
blanks with shortcuts and assumptions. With better data, the machines
are practically begging us to abandon stereotypes like "soccer mom"
and respect that each person has a unique cross-section of interests and
characteristics.

11. Realize that a robot never
told a great story.
In reducing people to what data can measure, we leave out that most human
of attributes -- emotion. Emotions are marketing's primary currency. People
literally make decisions from the emotional center of their brain, which is
why smart marketers use narrative, context and feelings to tell stories that
resonate. A story created by a robot is a story devoid of human emotion,
which is one more reason why effective marketing, even in the data-driven
era, will always need the human touch.

12. Why and how are these insights
relevant to a manager in India?
India needs to negotiate the world of big data
technology with adequate safeguards

13. One major problem with collecting and storing such vast
amounts of data overseas is the ability of owners of such data
stores to violate the privacy of people. Even if the primary
collectors of data may not engage in this behaviour, foreign
governments or rogue multinationals could clandestinely
access these vast pools of personal data in order to affect
policies of a nation. Such knowledge could prove toxic and
detrimental in the hands of unscrupulous elements or hostile
foreign governments. The alleged Russian interference in the
U.S. election tells us that these possibilities are not simply
science fiction fantasies.

14. THANK YOU