Five Tips for Implementing Geospatial Analytics

Applied Tech Review | Wednesday, September 08, 2021

There are some ways to implement geospatial data to optimize business performance and manage the movement of assets.

Fremont, CA: Geospatial analytics gives the geographical information of an entity. It's not only about particular points when it comes to location; it's also about movement. Because there are millions of moving assets to monitor and optimize at all times, including delivery fleets, vehicles, users, and on-the-ground drivers. Robust location analytics tools provide an organization with relevant real-time hyperlocal data that can revolutionize your day-to-day operations, promote greater asset utilization, minimize attrition, and improve the overall performance of organizations that deal with moving assets.

Here are five tips for implementing geospatial analytics:

Orchestrating

After acquiring data from various sources to gain insights, the data must be cleansed and verified for any anomalies. An organization can go on to create meaningful metrics once the data is ready to be used. Currently, it is done using R and Python to write complicated queries and perform data transformations. PostgreSQL and PostGIS are two other well-known software packages for powerful data processing and geographical operations. While these technologies can process the company's data, developing sophisticated measures like idle time, unfulfilled demand, or variances takes time and requires a thorough understanding of the situation.

Debugging

This crucial stage aids in the discovery of patterns and correlations among the moving assets. It ultimately boils down to identifying the issue areas and gaining actionable insights. For example, An actionable insight could be, that there is a strong demand near an office at 5 p.m. on weekdays.

Decision Making

There must be a way to make decisions in real-time when reacting to situations or more strategically when analyzing patterns and trends. Multiple teams must come together to decide and act on the data to implement the strategy. This is a time-consuming procedure that necessitates exceptional cooperation. It's more difficult when a decision needs to be made quickly.

Collaborating

Analytical work cannot be done in isolation. It must ensure that individuals from various teams may collaborate to debug and implement choices. Collaboration can be done using Slack, Jira, and other tools, but keep in mind that they do not have a knowledge base, and clearing a Jira issue can take longer than planned. When multiple stakeholders must make decisions, the context is frequently obscure, and results could be delayed.

Measuring impact

A company can measure the result of its actions and modify them once it has made its decisions. It is the only way to determine whether or not the company's solution is effective. Due to a lack of appropriate infrastructure, this stage is now absent from today's analytics process. This not only makes it difficult to experiment, but it also makes it difficult to evaluate the strategy in different places and at different times. For example, a company may experiment with the western part of the city to determine if delays are reduced on Mondays by providing additional members of the delivery fleet.

Read Also

follow on linkedin Copyright © 2021 www.appliedtechnologyreview.com All Rights Reserved | About us
Top