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Six realistic ways to tackle data-related challenges

12 minute read
We have never been at a more opportune moment than now to talk about the means by which government employees can achieve real benefit using the data they have.
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We have never been at a more opportune moment than now to talk about the means by which government employees can achieve real benefit using the data they have. In contexts where challenges are at the centre, Identifying specific problems and resolving them in a clear way is instrumental in improving governments' communication with the communities they serve.

But the mere talk of data gives government employees a sense of confusion among a flood of specialized technical terms. We often hear talk about employing data, but it doesn't answer the question: How can employees who are not tech-savvy achieve real success when they face challenges in performing their daily tasks? The good news is that data analytics is not a specialized technical term at all and government employees can apply it on the ground. Data is the key to transforming policies into practice and is a tool that helps everyone work smarter without extra effort. The most important point here is to link the data to a change in services to achieve tangible results.

Let us reinforce this on the ground in practical applications of any different nature, Leaders need to find opportunities to make a difference in services. Let's take truffle supply services for example, First the pigs curse truffles, extract them from the ground and quickly acquire significant market value. We can liken the opportunities that data analytics offer truffles to government. Leaders who find these truffles and put them on the market will become heroes in our eyes.

Here are six practical ways to activate the opportunities presented by data analytics.

1. Pelletizing algorithms to predict scenarios

It can sometimes be difficult to determine the identity or location of the recipient of the services. But we can use data analytics to predict which locations services should target so that government agencies can accurately direct their resources away from randomness.

In New Orleans, for example, An unfortunate fire broke out in a house that killed five people, only to discover that the house was not equipped with a fire alarm, despite the existence of a program introduced by the Fire Department that enables individuals to obtain fire alarm systems that benefited very few. Desiring from the Chief of the Fire Section to prevent unfortunate incidents of this kind, He issued orders to send firefighters to neighborhoods to inquire about homes that needed alarms.

Using the data to determine which locations to go to first, Firefighters were able to target the right locations without wasting their time. The municipality used free and government-wide housing data along with its fire incident data to develop an algorithm to predict which neighborhoods are likely to need the devices. Seven months after the fire alarms were provided, The data revealed that 11 people survived a house fire located in the targeted neighborhoods thanks to the installation of the new alarm.

2. Prioritize action that achieves impact

The accumulation of operational tasks is common in government agencies that used to deal with them on a first-come, first-served basis. Data analytics helps entities sort their backlogs so that services reach those who need them most. Data analytics is also important for entities that have always faced staff shortages to be able to achieve the highest value for money spent.

In New York City, A team of building inspectors faced a large backlog of work that included buildings that had been illegally diverted to make them uninhabitable. When the inspectors visited these buildings on the basis of complaints, 21% were in very poor conditions to receive an eviction warning. Analyses of the data showed that late payment of building taxes is a good indicator of unsafe housing. By reclassifying the backlog to show buildings that are in arrears in paying taxes first, The infection rate reached 71%.

3. Use early warning tools

Government services are far from proactive and available after a situation highlights the need. And the opportunity here presents itself, If predictive analytics can anticipate problems before they occur, Governments can adjust their services to adopt a proactive approach.

In Los Angeles, Predictive analysis has enabled the municipality to provide services aimed at better preventing people from homelessness. Researchers from the University of California, Los Angeles, used data from seven government agencies to create a model to predict populations most at risk of homelessness. Accordingly, The municipality has prepared an action plan for the intervention and established a multidisciplinary homelessness prevention unit composed of representatives from all government agencies to deal with the risk lists provided by the model and the direct link between people and services.

4. Make better and faster decisions

Some government tasks involve making repetitive decisions made on a case-by-case basis. Using data to look into such situations before taking action can provide us with information on how to make better decisions in the future. New Orleans leaders have turned to data to address the challenge of pest spread by analyzing patterns inherent in pest eradication decisions. Upon conviction during the hearing, The municipality chooses to either demolish the building or foreclose the mortgage and sell the company.

Despite the expansion of pest control operations, The procedures related to it remained the same, resulting in a large backlog of tasks. Whether demolishing or selling a house is a repeated decision, It was taken manually every time. The decision study provided a glimpse into the patterns used to create an algorithm to sort cases into "potential sale" or "potential demolition" cases, which contributed to tasks that typically take 18 months in just 90 days.

5. Optimal allocation of resources

Sometimes, Governments allocate assets based on intuition or the traditional way of working. But in this case, the data can be used to make decisions about resource allocation based on real need rather than gossip.

The city of Chicago, Illinois, has reduced the number of complaints about rats by studying the factors that lead to calling 311 to complain about rats. Rattraps have previously been distributed randomly. But looking from another angle at the indicators behind the complaints enabled the city to redistribute traps to anticipate the spread of rats. The algorithm predicted that complaints of rats come after broken main water pipes, illegal dumping, etc., and accordingly distributed rat traps, reducing complaints by 15%.

6. Experience what really works

Governments use mass communication messages at all times to ask residents to perform various tasks, from paying bills to enrolling in a program. Studies have shown that some clever adjustments in the wording of letters or messages have a significant impact on their effectiveness. Sometimes, Letters are sent to everyone without being tested. Therefore, employing the A/B test and comparing the results illustrates effective messages to adjust future discourses accordingly.

In New Orleans, Only 50% of the eligible population benefited from the new medical assistance program that provides primary health care. To encourage individuals to participate, The municipality conducted a thesis test that showed the difference between the formulations, resulting in a significant behavioral change that led to more appointments with doctors.

By identifying opportunities in which data analysis succeeds, Governments have been able to save lives, increase productivity, avoid crises, and reduce backlogs. Tangible results that directly help voters are likely to have more value than ever before.

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