CIO OPINION
The scale of the task may appear daunting for CIOs and CDOs . metric , would be well worth working towards , and could also have value as a digital performance indicator . However , it may be too early to measure , while so much dark data remains invisible .
Let ’ s talk useful to them in the future once they have acquired better analytic and business intelligence technology to process the information .
New tools and standards
There is good news here . The scale of the task may appear daunting for CIOs and CDOs , but AI and Machine Learning have now advanced to the point that they can help automate the data structuring process . Only a tiny percentage of dark data needs to be reviewed at the outset by humans to kickstart the process . This can then be followed up with a reinforcement learning model to assess the relevance of remaining data and prioritise it . From then on , a virtuous cycle of tagging and analysis makes the process easier to manage .
Whatever dark data means to you or your business , it is an ‘ elephant in the room ’ for data centres , and the more we talk about it , the likelier we are to come up with incremental improvements . For individual data users there are things we can do to reduce single-use data . For organisations it ’ s a bit more complicated but approaches and tools are emerging . These should be discussed and shared .
As with energy efficiency , identifying and eliminating waste at source is the most obvious opportunity . According to IBM 60 % of data loses its value within milliseconds of being acquired , and any scheme to use data more effectively must first address the issue of collecting useless data . A robust approach to data gathering is the key here ; assessing how data can be used , or if it is usable .
Measurement would also help to benchmark progress ; considering the scale of the problem , there may be a case for setting standards for effective data use . Perhaps there is a case for a Data Usage Effectiveness ( DUE ) metric to sit alongside CUE ( Carbon ) WUE ( Water ) and PUE ( Power ), where 1 = 100 % elimination of non-essential single-use data . This , or some similar
The next step is structuring the data we keep . Structured data is not only more valuable , but easier to track and , if necessary , delete . By making data more visible , it should be possible to reduce the environmental and financial burden of storage at the same time as using our valuable data to empower our organisations and serve our customers better . p
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