What sets Locale apart from the other geospatial tools?
BI tools, as the name suggests, are general-purpose analysis tools best suited for statistical charts and lack geospatial capabilities. There are 5 steps in operational decision making:
- Creating metrics
- Debugging the problem
- Taing actions and decisions
- Measuring the impact, thus closing the decision loop
Doing all of this on a BI tool is a complex process and takes massive engineering and analyst bandwidth. This is how the process currently works:
- 1.Creating location-specific metrics like user churn, delivery costs, delays, etc. requires complex SQL or Python that happens outside of Tableau or Looker.
- 2.Debugging the problem again takes multiple iterations and creating several reports as these tools are not built for getting hyperlocal insights.
- 3.These tools don’t support collaboration, sharing and teams end up using Emails, Slack, or Jira. As a result, the insights are not democratized to the entire company.
- 4.Decisioning tools are completely different from BI tools in most companies. Marketing, operations, and product teams use different tools for implementing decisions and the data needs to get passed onto them.
- 5.With all of this, teams don’t measure the impact of their decisions because the current infrastructure doesn’t allow for quick experimentation and iteration.
To conclude, all of this takes approx. 60 days to implement a decision and still have no measure of impact.
We have built Locale custom for operations teams and have spent a lot of time with them to understand their needs and use cases.
In any decision-making process, there are 5 steps involved - creating metrics, debugging the problem, collaborating on top of it, making the decision, and measuring its impact. Kepler only allows operations teams to create a metric and debug the problem. However, with Locale, teams can also collaborate, take decisions and measure impact. Another reason why Kepler is not suited for high growth companies is that Kepler is not scalable. It needs the teams to manually upload data every time they want to look at something, while with Locale, there’s one-time integration only. Once a flow is set of the data, operations teams can come and use the product however they like.
Locale.ai Vs Kepler.gl
Engineering teams build and maintain internal dashboards which is a huge cost to a company. Moreover, the dependency on the engineering team to create new metrics for analysis makes the entire process much slower. Typically, companies take 60-75 days to create a single metric and this involves 5 different teams including data, operations, and engineering teams. Even after this, internal dashboards only solve for access to metrics while collaboration, taking the decision, and measuring impact is missing.
Locale.ai vs Internal solutions