An Introduction to Data Driven Decisions for Emergency Response Organizations
Not everyone needs to become a quant, data scientist or an expert in data management. However, it is worth brushing up on the basics of quantitative analysis, so as to understand and improve the use of data in your organization. We’ve created a brief overview of data management to get you started.
Not a week goes by here in the [D4H] Lighthouse HQ that we’re not thinking about the value of data for emergency response. Big data, small data, internal, external, experimental, observational, everywhere we look, information is being captured and we want to make sure this is quantified and used well.
YOU CAN'T MANAGE WHAT YOU DON'T MEASURE.
There’s much wisdom in that saying, which has been attributed to both W. Edwards Deming and Peter Drucker, and it explains why the recent explosion of digital data is so important. Simply put, because of big data, managers involved in emergency response can measure, and hence know, radically more about their organization, and directly translate that knowledge into improved decision making and performance. To begin, we'll define a few of the terms;
- Measure: The verb means "to ascertain the measurements of"
- Measurement: The figure, extent, or amount obtained by measuring"
- Metric: "A standard of measurement"
- Benchmark: "A standard by which others may be measured"
For many years spreadsheets have been used as an aid for compiling emergency response activities. However, the evolution of technology has allowed public and private sector organizations to capture, store, and analyze their data in a structured way, adding real value to compiled information.
However, given growing budget constraints and needs for accountability, their is a growing importance on understanding the data organizations collect. There is a need for systematic data for budget appraisals, mitigation activities and prevention planning is an increasing concern for many organizations. We've outlined 5 key areas where having a structured reporting solution, has improved the operations of organizations we've worked with and added real value:
The Value of Your Response Team Data:
Focusing on the Bigger Picture Statistical analysis of multiple responses, from a number of response teams, across a broad time frame, is a fast way of using statistics to measure the response trends for an organization. This data can afford leadership an unbiased outlook of the risks faced. Allowing them to mitigate and put preparation in place that is not based on uncorroborated presuppositions.
Backing Judgements Statistical data can back up assertions. Leaders can find themselves backed into a corner when persuading people to move in a direction or take a risk based on unsubstantiated opinions. Data can provide objective goals, with stand-alone metrics, as well as hard evidence to substantiate positions
Ensuring Quality of Emergency Response Equipment Anyone who has looked into continuous improvement or quality assurance programs, understands the necessity for data. Statistics provide the means to benchmark and control the quality of equipment. This saves money by ensuring substandard equipment is identified and removed from service.
Proving Response Team Budget Needs Response organizations face a variety of fiscal issues. Sound data can often be the key to unlocking the budget you need. Achieving the maximum possible budget allocation can often only be achieved by proving the worth in your operational activities. The best way to prepare for this budget conversation is to know what’s going on in your organization today and to have the data to prove it.
Connecting Risks to Training Needs Data can point out relationships. A careful review of data can reveal links between two variables, such as specific training and risk of incident (e.g Hazmat training and crude oil on rail incident). Using software solutions to delve into data further, can provide more specific theories about the connections to test. This will lead to more control over ensuring that teams are prepared for the most likely scenarios.