mindtalks analytics: COVID-19 analytics: the lesson for those involved in policy management – Finextra – picked by mindtalks

Contemporary federal policy interventions are increasingly described by data, indicators, and metrics. Furthermore, Data-driven ‘radical incrementalism’ techniques devised via the now independent typically the UK’s Federal government

Behavioural Observations Team
(BIT) –  which is now independent in the UK government – are generally successfully used worldwide.

BIT’s usual process is to help create several policy variants, test  them, and then selecting typically the best option. This is a good luxury that we can’t locate in a crisis like your COVID-19 pandemic, however.

Yet what we can do is  compare the progress together with outcomes of different policies bought y choices by across the exact world different countries inside the wish that of learning to quickly discover and,   if required, intervening, if required, in order toe early enough to be qualified to improve the outcomes.  

The following extract from
UK government coronavirus briefing slides
  is attempting to do merely that.  

But can you see what’s inappropriate with this picture on your slide 8? A close evaluation reveals that it has a selection of major flaws.

In the first place,   a consistent definition regarding ‘“COVID death’” across countries and also throughout the analysis timeline is without question needed if we want in order to make a detailed comparison in words and phrases of death rates. This chart features some countries  which have enclosed non-hospital deaths, as well just as others that have not. Additionally, some countries only counted deaths of those who had tested positive for the virus, while others included ‘suspected’ cases in addition. The timeline is also skewed since it sometimes counts deaths that occurred up to two weeks before.  

Moreover, the figures typically are not adjusted for population size. This, coupled with the absence of any reference points of death rates from previous years, is very misleading.

It’s also worth pointing out that, in its quest for simplicity, the chart only features deaths related to the herpes virus. It ignores other second-order effects such as the impact on life expectancy due to  postponed treatments, mental health challenges, and economic hardship.    

All of this begs the question: is the chart informative at all, given its data and its visualisation issues?

The importance of good data 

This case clearly demonstrates the importance of thinking about the that composition of a dataset, data measurement  and comparison processes, underlying assumptions, criteria, and definitions of success before any data analysis begins. It’s a good lesson for those needed for internal company policy management.

So,   the first thing an organization policy manager needs to do is  choose the right data points, right? Unfortunately, it’s not that easy! First and foremost,   a manager you needs to be in a position to manage to choose
what to measure. They need access to a wide range of metrics. Without this, they will  just finally end up measuring what they can, not what they  need to. So, the place to start is ensuring that all data generated by the firm is captured accurately.  

This is particularly true for companies that operate in the financial services industry. Banks together with other financial institutions generate an extraordinary amount of data and it’s crucial that they capture all this data. For these firms, data is definitely a valuable asset that can help create a competitive advantage. With access to the right data, financial organisations can increase efficiency, reduce costs, and improve customer experiences. It all starts with the data capture process, though. Data-reliant processes are only as effective as the data that is supplied to them. Having access to the right data is vital to gaining insights and empowering decision makers.  

If a bank’s policy management processes are manual and rely on multiple disconnected systems and generic tools, you could end up

spending 200 employee hours
merely prepare a single management report. Manually combing through stockpiles of documents, spreadsheets, and emails is not scalable and it’s certainly not agile.

When thinking about analytics in your own firm, consider your  policy management processes. Are they as automated because they could  be?

The very first thing you can be sure of is that change is constant. Regulations, technology, environments, mergers and acquisitions, business relationships… ‘stuff happens. ’  In  today’s dynamic, interconnected, disrupted business environment actually need to be ready for change.   Modern data-enabled systems and processes might possibly be the key to staying competitive. Regulatory technology, which has advanced tremendously over the past number of years, is the way forward.   It’s time for an upgrade!

We’ve built a platform which could save time and slash compliance costs by 30%. Analytics provide you with visibility into your ability to adapt to change, trends, relationships, frequencies, and volumes. You can empower you with a high-resolution, big- picture view of your GRC.

Source: finextra. com

 

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