In 2020, there will be 20.4 billion connected “things” in use, up from 8.4 billion in 2017. From smartphones to sensors to appliances to stoplights, the exponential growth of connected devices (dubbed the Internet of Things) is creating an overwhelming amount of data about what we do, how we do it, and where it happens.
CARTO partnered with Hanover Research, a respected market research firm, to bring you The State of Location Intelligence 2018.
We surveyed over 200 business executives, data analysts, and business analysts and our data shows that businesses executives and data practitioners see Location Intelligence as critical to their organization’s success.
Our goals for The State of Location Intelligence 2018 were to understand:
Nearly all medium and large organizations collect and store location data. Business executives know that Location Intelligence (turning that location data into business outcomes) will be crucial to their success.
Location Data is nearly ubiquitous at companies with over 500 people. 94% of C-level respondents said that their organization collects and/or stores location data.
94%
6%
There is a gap in the understanding of location data’s value, the practical applications, and challenges to adoption (among other things) between executive management and data practitioners.
For example, C-level respondents were more aware of methods for collecting location data and interestingly, also more aware of the challenges than analysts.
57%
40%
54%
44%
51%
38%
47%
50%
45%
23%
45%
31%
45%
25%
43%
21%
43%
23%
41%
23%
41%
6%
28%
13%
24%
17%
24%
25%
0%
2%
0%
8%
Businesses must focus on implementing LI best practices and methods across their organizations.
Most businesses use geographic boundaries at a state or zip-code level, but few visualize or analyze their spatial data with more granular boundaries. Only 27% said they use any kind of custom geography and 17% use block groups (the most granular level that Census data provides). To understand location data, businesses must begin visualizing and analyzing at a deeper geographic level.
76%
State
72%
Region
69%
City
68%
Zip Code
64%
Country
31%
Physical Store
27%
Lat / Long
26%
Custom Geo
26%
Virtual Location
17%
Block Groups
0%
Other
0%
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