Every industrial business—indeed, every conceivable kind of organization—runs on data. Or to be more precise, they run on the insights generated from the enterprise’s data, making key decisions based on those insights and their value in the appropriate context.
Yet everyone seems to agree that most of what is in our databases is of questionable value, no matter who owns the data or what it’s used for.
So, here’s the “running-on-data” problem. On the one hand, data is essential in running any business. On the other hand, much of our data may be worthless or inaccessible to those who need it. How can these two apparently incongruent statements be reconciled?
I believe the answer lies first in placing each discrete bucket of data in context, such as where it was created and by whom (i.e., person and/or system) and where and by whom it can be accessed. Decision-makers can then extract value from the data and the depth of insights needed for sound decisions. Even with this, uncertainties will persist.
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