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Garbage in, garbage out

Garbage in, garbage out—so goes the computer science mantra. But what if you have no trustworthy, reliable data to input into your calculations and analyses? How do you navigate challenges, mitigate risks and spot opportunities if you can’t confidently make predictions from past trends?

I recently wrote about predictive analytics for a UK accounting publication where I considered its value in this world of atypical data produced by the COVID-19 pandemic. Predictive analytics is the ability for organisations to unlock the value of big data to predict the future using data mining and various mathematical processes. It can be used to spot trends, identify the relationship between behaviours, and forecast events and market conditions. Crucially, it is set to be a huge differentiator for organisations in the future.

But thanks to the health crisis, we have two to three years of atypical data. Further, when we do come out on the other side of the pandemic, the world in 2022 or 2023 is unlikely to pick up smoothly where 2019 left off. Instead, we’ll have to develop a new baseline of data on which to build our predictions and forecasts. So for the moment in any case, I see the power of predictive analytics as being reduced, because we don’t have the trustworthy data with which to feed it.

This got me thinking, as the Zondo Commission of Inquiry starts wrapping up, that in South Africa atypical data goes beyond the pandemic. The output from the Commission definitely suggests that due to state capture, fraud and corruption in the public sector, most data therein is immediately tainted and potentially untrustworthy. How can we say with any confidence that a trend, in spending say, is based on reality and not warped by corruption?

The power of predictive analytics is not just beneficial for companies, it has huge potential in the public sector to improve service delivery for citizens, drive efficiencies and cut costs. But instead, the public sector is potentially laden with untrustworthy data, marred by years of corruption and fraud, and now further blurred by the pandemic.

So what hope for municipal, provincial and national governments who want to tap into the power of predictive analytics as part of their digitalisation strategies? They have to navigate the double blow of fraud and corruption-skewed data, as well as the pandemic’s atypical trends. How do they, or any organisation for that matter, plan today?

Accountants have a model for this already: zero-based budgeting. Taking a zero-based approach to budgeting and forecasting using financial modelling, organisations can work around a dependency on historical data. Instead, we can look at our own predictions, coloured by our own experiences and expectations, relevant to our unique set of circumstances.

Zero-based forecasting

Another benefit of a zero-based approach to forecasting is that your financial model is now rooted in reality, your numbers can be tested against a myriad of flexible assumptions, at very granular levels, and across your entire organisation. These best estimates may paint a picture of what your tomorrow may look like in a still somewhat uncertain future.


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