Value of Data
In the modern world, data is a valuable resource. The
value of data though can be harder to quantify compared to other more tangible
resources such as equipment, real estate, or employees. Though these
assets can be more easily digested in terms of cost vs. value, the impact data
has had on the modern world in undeniable through cost reduction, revenue
increase, and income generation. With data being infinitely produced at an
ever-faster pace, the value of data is only increasing.
Though data is being constantly produces in the modern, the
type and quality of this data varies massively. Data alone doesn’t necessarily have
the value that business and organisations are looking for and that is why Big
Data technologies must be applied to extract value from the endless sea of data
and gets that data to the right people at the right time. Using Big Data to
extract value from data comes at a cost and how the end results balance out
this cost can be understood through the 1-10-100 rule. (1)
The 1-10-100 rule was introduced in 1992 with the basic
premise describing the impact of data costs:
1 – The cost of preventing bad data from entering the system.
Using data tools to manage data a business collects, and use incurs a cost though
a lower cost than later down the line.
10 – The cost of correcting bad data once it’s in the system.
This cost could be errors create by bad data, the cost of reprocessing data or manual
data correction.
100 – The cost of dealing with the consequences of bad data
that was not addresses thought earlier stages in the process. This Is the
highest cost and comes from the business or organisation making decisions based
on bad data, loss of consumer and shareholder confidence due to errors, or resources
spent rectifying errors.
This rule was created in 1992, and data is only more
essential to modern businesses, so the costs and scales of this rule is likely
even higher. The purpose of this rule is to show how it is essential and financially
beneficial to process data properly as early as possible to reduce costs and maximise
value from data. (2)
When the costs of data processing are understood, and data
is properly handled then the value data can bring is significant and various:
- Improving Efficiency – Streamlining processes, reducing waste and automation task, are all ways businesses can take advantage of data to bring value.
- Reaching New Customers – Using insight derived from data to reach to customers can increase business revenue.
- Improving Customer Satisfaction – With personalized data, businesses can target customers individually to deliver more tailored services and have happier customers.
- Improved Research and Development – Data is essential to new research and development ensuring focused efforts and reducing waste.
- Monetizing Data – Once data has been processed and refined the data then has value in itself with businesses such as data brokers packaging and selling data as a service.
The value of data can be approached in three ways:
Income approach – This approach looks at how much income increases
with using data vs. not using data and values the data based on income increase.
Market Approach – This approach is the value of selling data
on an active market. This is the simplest form of data as a pure value but is
more specific in its possible overall value.
Cost Approach – This approach compares the value of data to
the costs incurred creating the data and derives its value from the difference.
2. https://aicadata.com/the-power-of-clean-data-exploring-the-1-10-100-rule-with-aica/
3. https://www.pwc.co.uk/data-analytics/documents/putting-value-on-data.pdf
Thanks for your very rich explanation of the subject and for adding the references. Great expirience.
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