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Why You Have to Treat That Real-Time Economic Data Cautiously

COVID-19 pandemic has spread at a disturbing speed, infecting and affecting millions while taking economic activities to a near-standstill as countries imposed tight restrictions on movement to stop the spread of the deadly virus. As the health and human toll grows, the economic damage is already evident and represents the largest economic shudder the world has experienced in decades.

Time to Appreciate Data

The massive turnover brought about by the COVID-19 pandemic is up until now probably the most quantified and reported news on record. Economists, firms and statisticians seeking to gauge the depth of the collapse in economic activity and the pace of the recovery have stumbled upon using real-time data to measure economic trends and in future forecasting, a method that was previously underestimated.

World Business Leaders Listening to a Presentation made by Predictive Analytics Lab Founder and CEO Timothy During  the Ismail Economic Forum  held in Dubai

Economic data are known to describe a past or present actual economy, typically found in time-series form, that is, covering more than one time period or in cross-sectional data in one time period. The real-time data set consists of vintages, or snapshots, of time series of major economic variables. The data set may be used by economic researchers to verify practical results, analyse policy, or to forecast.

Prior to the COVID-19 pandemic, investors and policymakers alike relied on official, so-called hard data, to report on the progress on issues such as inflation, employment or output measures, which tended to be released with a lag of several weeks, or even months. With the current trend however, these policymakers lean largely on real-time data that offers a far greater advantage of timeliness.  Not without mentioning has to be Big Data Redefining  Human Resource Functions as one of the ways to keep up with the ongoing demand for a relevant skillset.

Investors eagerly await the release of mobility/ transportation statistics from tech companies such as Apple or Google, in a manner once reserved for official inflation and unemployment estimates – data they now use to make well-informed decisions on what direction to take. Central bankers and investment bank analysts in like manner season their monthly reports with statistical indicators showing the variant levels of consumer spending; where to invest and why, depending on data collected using different algorithmic expressions.Read: Applying Algorithmic artificial intelligence in business solutions

Remaining ahead of the curve

Predictive Analytics Lab Coach,  on a One on One Coaching Session With Company Departmental Heads and Senior Decision Makers

The main attraction of real-time data to policymakers and investors alike is timeliness. Companies that would prefer to wait for the official hard data are regarded as being like fans of an already shut down Internet Explorer, ignoring the efficient opportunities presented by other search tools such as Google Chrome, Bing and the recently launched Microsoft Azure. They remain stuck behind the curve, while the rest of the world has moved on, embracing better technology such as Business Intelligent App

The adverse effects brought about by covid-19 have put a premium tag on swift intelligence. Hard data has always been efficient for their quality but is not sustainable during this pandemic. Filling in statisticians’ forms for instance has probably, since COVID-19, fallen to the bottom of firms’ to-do lists, reducing the accuracy of official output measures. It has also been an uphill task compiling labour-market figures as the difficulty in accounting for downsizings increases with never-ending high levels of uncertainty.

What the numbers tell

Predictive Analytics Lab Instructor, Training Employees from the Agriculture Sector on Data Science and Visualization at University of Nairobi

While real-time data is an indicator of the moment’s consumer trend, it should be treated as that, and not over interpreted to represent a global economic recess. Case scenario would be the mobility data from Apple and Google. The tech firms should be commended for making these figures available within a short span of time, and at a level of specificity that allows for a detailed look at travel patterns. How big data can help boost tourism numbers. But the numbers need to be treated as what they are—a measure of mobility—and not a whiteboard for overall economic activity.

They may for instance reveal that more people are returning to workplaces as the weeks go by, but not whether they were previously working from home or were out of the labour force altogether. Set up a profile at Available Data Science Jobs to receive notifications on emerging opportunities.

Neither can they show whether commuters had saved up money for their children’s school fees or were using up their travel vacation money. Should there be a need to unpack probable reasons leading to why the trends show what they show, it would be a job handed over to economic analysts and specialists to unload the information.

A different example however would be the increase in usage of mobile money and e-transactions. Supermarkets and stalls alike have since the pandemic, registered an increase in customers using credit cards and mobile money platforms such as M-Pesa when paying for goods. A good reason for this would be reducing the risk of COVID – contamination presented by hard cash. A viable adjustment to make would thus be increasing the bandwidth by the service providers to allow these e-transactions to run smoothly, void of network issues. Talk about swift intelligence!

 Read: The importance of using data in Financial reporting

Long Term Decisions

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Making these adjustments dependent on real-time indicators is a decision that lies with the key stakeholders having a better understanding through Data Science master classes, to be able to   make informed changes that still maintain the company’s overall objective.

The value of real-time measures will be tested once the swings in economic activity approach a more normal magnitude. Mobility/ transportation figures for March and April 2020 predicted, as did other sectors, the scale of the collapse in GDP – a definite result of the pandemic. Investors in this industry thus were able to make proper decisions, dependent on what the figures showed.

What Next?

So, rather than relying on presumptuous data on what the trends show, why not take the initiative to learn what the numbers say and what to do about them! Much like how Internet explorer shut down and Microsoft Azure comes in, Learn how to drill sense out of Data and get to know the trends from the numbers and remain ahead of the curve.

Real time economic data enables us to quickly understand the main drivers of a wide range of economies, and makes an invaluable contribution to our macroeconomic and real estate forecasting work. To get to learn more on Big Data Enroll for our Data Science courses at


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