DATA STRATEGY 101:
BECOMING A DATA DRIVEN ORGANISATION
They say Mission Critical Mobile Broadband networks will revolutionize the public safety industry.
Riding high on the hype-curve of expectations are broadband enabled technologies such as AI, AR, and IoT. Development of sensor technologies, e.g. biometric wearable sensors, hazmat detectors, wearable cameras and video analytics technologies combined with ubiquitous connectivity (ultra-reliable, high bandwidth, low latency) are expected to drive public safety digitalisation, delivering unparalleled value to the efficiency and security of the first responder’s daily lives.
In the field of public safety (like in other fields), the recent message has been clear: It is good to start doing something now to leverage the benefits of digitalisation and be prepared for the future.
Motivation to change
The public safety sector has been traditionally considered conservative to adopt new technologies. And it is understandable that having your life at stake makes you pragmatic towards vendor promises. However, applying new technologies to public safety can bring significant return on investment in areas like:
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Efficiency (resource efficiency, cost efficiency and speed of operation)
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Safety and Security (Control, Resilience, Risk Reduction)
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Quality (Effectiveness of operations, compliance with regulations and expectations).
Digitalisation and utilizing data driven tools opens the path to achieving all of these. Data allows us to see through the organisation’s processes accurately and often even in real-time. This allows us to share and validate information, measure, record, analyse and optimize our processes.
“it is understandable that having your life at stake
makes you pragmatic towards vendor promises.”
Be aware of the pitfalls
This all sounds lucrative. However, there is a risk that digitalization projects fail to deliver the benefits if basic assumptions and expected benefits are not understood and reviewed by users up-front.
New tools have a cost: they need to be trained to users, maintained and further developed. The lifetime costs do not justify the investment, work is moved from one place to another and frustrated users fail to benefit from the new tools.
Being “Productive” may not always lead to the desired results. It can be used to justify keeping resources at the minimum. Public safety organisations should prefer “efficacy” – the goal being maximizing. the value of people’s work and allowing people to focus on the type of work that only people can do.
Also, we need to always be aware of the alternative that data becomes unavailable. The users should not lose their capability to act independently and work based on their experience.
“Public safety organisations should focus on efficiency
instead of productivity – the goal being maximizing the value
of people’s work instead of keeping the resources minimal.”
Transform data into meaningful information
A data driven organization is one where decisions are based on data, e.g. facts. The levels of sophistication vary but essentially, organisation should be able to get accurate data to base their decisions on. Having access to reliable operational data as basis for planning and analysing the operations serves as a good baseline.
Where public safety is concerned, the need for ACCURATE real-time information is relevant. Often the lack of agreements about the meaning of data, terms and data dictionaries can make it difficult to collect reliable and understandable information. This can prevent data from being actionable, which in the other hand is better than acting based on false information. This is not a technical issue but something all organisations need to tackle on a process level – agreement on data. Once the meaning of data has been defined, organisations can start extracting information from it.
Rule based automation is one way to harness data without overwhelming the users. Teach systems rules (typically based on what people already know) that filter, enrich, distribute and prioritise the incoming data. This allows bringing new information to the user in a controlled way. Automation can also help in creating data pipelines that remove the need for manual human work. It can help in moving data between systems saving time and avoiding costly system replacements.
Implementing learning systems (AI, machine learning) is the next step. Algorithms are used to understand patterns in data and predict and automate decision making. The potential is immense, but often the technology needs to be applied to solve specific problems and requires a lot of work and effort to reach a reliable level. The time when an algorithm is – although not perfect and error free – on the average more accurate in making dispatching decisions than a human being is much closer than we may realise.
Make information actionable
The human brain is by nature constantly observing and processing the environment – classifying and prioritizing – to be able to decide if something is a threat or an opportunity, requiring immediate action. This is our survival instinct. We’re great at detecting anomalies in our environment. However, when overloaded with too much information, we slowly loose our capability to classify and prioritise. We become blind to the normal and it becomes increasingly difficult to decide what to focus on
When utilizing large amounts of data, organisations need to consider how to filter the information to support the human brain’s attempt to prioritise and classify the information.
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Information filtered for the situation at hand
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Provide enough details for decision making (ability to drill into more details if needed)
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Provide accurate and concise presentation of the information
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Provide reliable data that the user can trust