The Internet of Things (IoT) and digital business will produce an unprecedented amount of location-referenced data, particularly as 25 billion devices become connected by 2020, according to Gartner estimates. Organizations that implement geospacial and location intelligence (GLI) capabilities will benefit from opportunities to analyze the spatial dimension across their strategic, tactical and operational analytics.
This type of insight can produce real business results, such as reduction in assessment time for new franchise salons; analysis of driving patterns to optimize fleet performance; or optimal placement of wind turbines by compiling 10 times the breadth and history of weather data including temperature, precipitation, wind direction and velocity. However, a mere 23% of organizations use GLI capabilities within their business intelligence (BI) and analytic platforms, according to Gartner research.
It’s time to investigate how GLI capabilities are being used, identify existing capabilities, and consider the infrastructure necessary to support new use cases.
Moving forward, analytic and information leaders must put the “where” question to all new BI and analytic projects.
Given that nearly all vendors of BI and analytic platforms deliver at least basic GLI capabilities out of the box, organization’s relatively low utilization rate indicates that the spatial dimension of businesses (such as where are things happening? or where are my customers?) is apparently not yet a key component of a BI and analytics strategy, according to Thomas Oestreich, research director at Gartner. Moving forward, analytic and information leaders must put the “where” question to all new BI and analytic projects.
Identify your current GLI capabilities
As a first step in transforming the organization’s readiness to leverage GLI capabilities, identify and document your current GLI capabilities and how they are used. Build a task force and check areas including:
- Use cases and users in the different departments or business units using geospatial and location data.
- GLI capabilities already present in the current BI portfolio, data discovery and analytics tools.
- Spatial storage and processing capabilities of the organization’s databases and data integration platforms.
- External data (open or syndicated sources) available for enriching current enterprise data to provide greater GLI opportunities.
This first step should give analytic and information leaders a good overview on how GLI is currently leveraged across the organizations and which — potentially unused — capabilities already exist. Identifying unmet requirements can lead to an immediate action plan to enhance the use of GLI to expand the knowledge and expertise within the organization.
How will you use GLI?
Next, analyze the identified unmet requirements and build an action plan to implement those delivering the highest and fastest return. Here, it’s necessary to brainstorm use cases and collect new ideas to identify further opportunities to leverage location data in analytics.
Dynamic use cases require a significantly different technology that is able to handle the spatial processing and analytics in (near) real time.
It is important to make a distinction between static and dynamic use cases. In static use cases, all the necessary data is collected beforehand — whereas in dynamic use cases the data is collected in (near) real time. Dynamic use cases require a significantly different technology that is able to handle the spatial processing and analytics in (near) real time.
Secondly, a distinction should be made between indoor and outdoor use cases. Each requires different map data in different formats. The data transmission technologies are also different, depending on where the data is collected, for example, within a building or on the road.
Finally, with the advent of the Internet of Things comes a great opportunity to identify and track people, whether employees, customers or consumers — but this also introduces potential risk. Inappropriate application of GLI can lead to regulatory, ethical and even legal issues. This means that any new use-case idea should be evaluated in line with regulatory requirements, regional privacy and data protection laws. It must be part of the governance practice to identify appropriate usage of location data.