It’s Not Just the Data that is collected that Counts, but, the Quality of the Data as well

In God We Trust, Rest Have to Bring Data: The Importance of Data Collection

All entities collect data for various reasons. Whether it is the government collecting data on economic growth, employment rate, budgetary purposes, or for welfare schemes, or it is the private firms collecting data on revenues and productivity, there is a need for data at all levels and for all entities.

Indeed, collecting data is the lifeblood of all entities since they need data to justify or reject the many options and strategic choices available to them.

No wonder the icon of the Indian Industry, NR Narayana Murthy, was fond of saying that “In God we Trust, Rest all have to Bring Data”.

This is the reason why many governments have dedicated departments for collecting data and on the other hand, the private firms often employ external agencies to collect data for them.

Moreover, technology has revolutionized the way in which data is collected by making it easier to centralize data collection and at the same time, ensuring that the data that is collected is very fine and granular.

Thus, data collection does rank as a high priority item for both governmental and private entities.

It’s Just Not the Data Collected that Counts, but, the Quality of the Data as well

Having said that, it is also not the case that merely collecting data is enough, by itself. Indeed, the importance of collecting quality data that is reliable, accurate, and genuine cannot be overemphasized more.

This is because poor quality data distorts decision making and leads to bad decisions. for instance, if the government reports that unemployment has gone down and therefore, puts in place policies that do not take joblessness as a priority item, the whole edifice of the nation’s approach to welfare schemes and policymaking goes for a toss.

Indeed, this is what is happening in India right now wherein the employment statistics and other associated economic growth figures are being questioned by experts and doubted whether they are accurate.

In this context, it is worth noting that the governmental officers in charge of statistics and data collection have resigned due to the government’s reluctance to reveal the data about the true picture of the Indian economy.

Further, many respected economists worldwide have weighed in on the issue and have pointed to how the methodology for computing the GDP or the Gross Domestic Product is suspect since it inflates the growth rates through negative deflators whereas in reality, macroeconomic indicators point otherwise.

The Perils of Reporting Faulty or Inaccurate, and Unreliable Data

There are many disadvantages and downsides to collecting poor quality data or data that is questionable.

For instance, Greece is an example of a nation that fudged its numbers to gain entry into the Eurozone and once the financial crisis struck, it was forced to admit that it used questionable growth figures and had to be bailed out multiple times.

Apart from this, if we go back a decade or two, we find that the Asian Financial Crisis of 1997 which brought Thailand, Indonesia, and Malaysia to their knees was because of fudging and manipulating data about the Real Estate Sector as well as other key sectors in the economy.

Even in the corporate world, there is sometimes a tendency to do what is euphemistically called “Window Dressing” of data that presents the data that is collected in a favourable light and brushes under the carpet that data that reveals the true picture.

Tools such as the HR Scorecard need accurate and reliable data to perform efficiently and effectively, and hence, it is in nobody’s interest if faulty data is collected and used to justify decisions that can backfire in the longer term.

One Cannot Fool All People All the Time Forever

Talking about the longer term, as the saying goes, one cannot fool all people all the time, and hence, sooner or later, there would be payback for all the faulty data that has been collected and reported and this can lead to serious repercussions for all stakeholders.

We have given examples of countries that have suffered on account of faulty data. In the history of the Indian Industry, the erstwhile Satyam Computers is an example of how fudging data can lead to the demise of entire companies as well.

This corporate and its founders and the senior execs in cahoots with the auditors presented a rosy picture of the finances of the firm until it was no longer possible to hide such irregularities.

In recent years, there has been more regulatory pressure on corporates to report accurate data and in this regard, it needs to be mentioned that the good work done so far is now again under threat in the last few months.

In other words, there are serious issue related to excessive debt in the Indian Banks and for some time, it looked as though they were being forced to report their numbers accurately.

However, with the change of guard at the RBI or the Reserve Bank of India, it seems as though we are back to square one.

Concluding Thoughts

Lastly, just as households cannot sustain themselves without being genuine about their savings and expenses, corporates and governments too cannot go on forever by reporting faulty data.

Indeed, this problem is not restricted to a few firms or countries and is now an international phenomenon where reporting poor quality data has become the norm rather than the exception.

To conclude, data is integral to any entity and hence, steps must be taken to ensure that unreliable and inaccurate data does not vitiate our future prospects.


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The article is Written By “Prachi Juneja” and Reviewed By Management Study Guide Content Team. MSG Content Team comprises experienced Faculty Member, Professionals and Subject Matter Experts. We are a ISO 2001:2015 Certified Education Provider. To Know more, click on About Us. The use of this material is free for learning and education purpose. Please reference authorship of content used, including link(s) to ManagementStudyGuide.com and the content page url.