These days big-data seems to be so ‘big’ that it is
everywhere. I have read with some interest
‘Big data’ by Mayer-Schonberger and Cukier.
I was looking to form some views about it reflecting my perspective of
risk management in financial services and ‘value enhancement’. These
are three of the key points I took from the book.
1. Big-data is not about size but about the
ability to work with full data sets.
This
means that the constraints that might arise from sampling are avoided. Interestingly, there will be cases where adopting
a big-data approach means handling a relatively small data set.
2. The shift from causation (small data) to
correlation (big-data).
The
ability to create additional data at low cost and join up data sets means that
we are likely to increase our ability spot correlations. This would help us understand the ‘what’ even
if we don’t fully understand the ‘why’ or the causation.
3. All data has value and a company’s
ability to extract the value depends on the business model and skills.
The
value of data arises from secondary uses which are difficult to predict when
the data is collected. Companies can extract
the value by hoarding the data, analysing it and identifying opportunities for
big-data.
This led me to three observations about big-data and risk
management:
1. Risk managers need to identify the aspects
of risk management that can be enhanced by understanding correlations (‘what’)
and the aspects that can be enhanced by causation (‘why’).
While
the message of big-data is that correlation is becoming cheaper to identify and
offers more value in a shorter period of time, there isn’t a one fits all! For example, insurers’ ability to spot
financial crime, cases of fraud and price insurance risks would be enhanced by the
ability to identify the correlation between key variables. On the other hand, understanding correlations
between risk drivers may need some plausible stories to make them actionable.
2. Existing risk management and regulatory
concepts would need to be revisited.
One of the features of
big-data is that when different data sets are combined the resulting data is
‘messy’ with many empty cells. How do
you apply existing criteria for data quality governance, in particular
‘completeness’?
How do you validate models? The authors bring in an interesting example where a simple model performs more effectively than any of the alternatives when a significant amount of data is fed into the model.
3.
Extracting
value from data would need careful thinking.
One
of the fundamental technological changes is that data is generated in many
un-suspected places and situations, e.g. internet searches. Spotting those opportunities requires a
big-data mind set. Capitalising them
requires the ability to capture the data and / or use it. One implication is that the value of data is
something that would need to be factored into commercial outsourcing with third
parties.
Overall, this could lead to a significant transformation
of how risk is managed and become a new ‘normal’. However, between now and then companies
would need to tread carefully to avoid chasing ‘big-data’ opportunities of
limited value.
If
you work in financial services, I would be keen to hear your thoughts about
big-data and risk management. If you
don’t, I would be keen to know if these lessons resonate with your experience.
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