Internet of Things
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It comes as no surprise that an important part of data analytics is the data itself. In fact, the appeal of the internet of things (IoT) largely relates to the role of connected devices in gathering this valuable resource.
With hyperbole rampant about the new “oil,” “soil,” “coal,” or even “gold,” are we becoming inured to data’s promise? And even if we aren’t, how effective is IoT at delivering on the promise of these data riches?
Recently, Stephanie Jernigan, David Kiron, and I researched the effect that IoT is having on organizations. A combination of interview and survey responses from 1,480 managers resulted in a summary report of this research, “Data Sharing and Analytics Drive Success With IoT”.
One aspect of our findings that we were unable to cover in the summary report was the relationship between organizational experience with IoT projects and organizational data. Our understanding of this relationship flows from responses to two questions: First, we asked organizations how much experience they had with IoT projects, ranging from none (27%) to actively using IoT for more than 2 years (13%). Second, we asked organizations to assess the data they’ve collected along four dimensions: its timeliness, accuracy, detail, and reliability. Figure 1 summarizes these results.
What we found was that increased experience with IoT projects is associated with improvements in the timeliness, detail, accuracy, and reliability of data. This is certainly comforting to those investing the time and resources in deploying these devices. A greater volume of data from IoT devices seems inevitable. But beyond that, organizations improve over time in their ability to get better quality data, not just greater quantities.
Of these data quality measures, timeliness exhibits the largest difference. About 40% of respondents whose organizations aren’t active with IoT reported that their data has “mostly” or “completely” sufficient timeliness; in contrast, 76% of respondents who have 2 years or more of IoT experience said their data was sufficiently timely. As systems monitor and transmit data closer to the source, delays associated with data gathering decrease. Accuracy of data takes longer to improve but eventually reaches the same level of sufficiency as the timeliness dimension.