In the modern business world, data is often described as the new currency. The metaphor is quite fitting given that these days companies in all industries seem to be chasing data to streamline their operations, turning it into a highly coveted asset in the process. Just like money, data can serve as a means of exchange and a source of wealth for enterprises that know how to leverage it. But herein lies the problem. Most companies don’t really know how to use the data they collect to their advantage and instead expose themselves to all sorts of risks and challenges because their focus is not in the right place.
It’s clear that data is running the show, helping companies identify trends, gain valuable insights into their audiences and markets, and make more informed decisions, but it can also ruin it just as easily if the strategy around data collection and management is flawed. That’s exactly what happens when businesses place too much emphasis on amassing vast amounts of data while disregarding quality. What’s the use of standing on piles and piles of data if one can draw no benefit from it?
The truth is companies already have all the data they require and a lot more on top of it, so there’s no need for them to harvest more. In fact, a lot of them are overwhelmed and even paralyzed by the quantity of data that continues to flow in from various channels. Once a business reaches the point of saturation in this respect, data is no longer a commodity but becomes a liability and can have a deep negative impact on its productivity and overall performance, proving that bigger is not always better.
Therefore, it’s not more data that companies should be striving for but better data that can bring them real value and help them achieve their objectives.
The factors that determine data quality
The data that companies garner via different methods comes in many shapes, forms, and sizes, so its value is not easily quantifiable, as is the case with tangible business assets. Besides, thinking of data in strict terms of good and bad is not necessarily the most appropriate approach since its qualities are expressed on a spectrum.
Nonetheless, there are certain aspects and characteristics that can make it easier for companies to estimate the worth of different types of data and assess their utility, as follows:
- Accuracy – the information that the data provides must be correct and reflect objective reality
- Accessibility – one should be able to find, retrieve and use data with ease without scouring through data silos or hitting obstacles in the process
- Completeness – there should be no gaps or key information missing from a given data set to ensure accurate insights and usability
- Relevance – data must be suitable for the purpose that it’s intended and drive useful insights in line with the company’s specific needs and requirements
- Reliability – quality data should be trustworthy, error-free, and consistent across time and multiple sources
- Timeliness – in a fast-moving world, data must be up to date and mirror current realities
This demonstrated that data quality is an intricate and multidimensional concept that involves a combination of attributes, being dependent upon context.
The high price of prioritising quantity over quality
One may wonder what could possibly happen if a company insists on putting quantity first and leaving quality as an afterthought. A lot, apparently. A quantity-centric approach can lead to all sorts of unpleasant consequences, from damaged reputation to compliance issues or revenue loss.
For starters, when companies have a lot of data at their disposal, but its quality is poor, there’s a high risk of inaccurate analyses, biased results, and, by extension, misguided decisions and ineffective strategies. This is a particularly prevalent issue in market research, as many companies still rely on outdated data collection methods and strategies. At the same time, market research agencies in the Netherlands and all over the world recognize the need to prioritize data quality when conducting their analyses so they can harness the true power of data and move the needle in the right direction for their clients.
Treating all data equally and not making an effort to assess its quality can also result in a waste of time and resources, delayed decisions, and inefficiencies across all business levels. Blinded by the vast amount of data they have to handle, companies can lose focus of what’s truly important and stray away from the right path.
Additionally, collecting data that is not relevant or necessarily but can be exploited by malicious entities makes companies more vulnerable to cyberattack risks and the legal ramifications deriving from it. It can therefore be concluded that the costs of using low-quality data are rather high.
Addressing the need for better data
Since data quality plays a crucial role in making smart decisions and enhancing business processes, the question that arises is, what can companies do to ensure high data quality?
Best practices include a series of steps, such as:
- assessing business needs and objectives
- resorting to reliable data sources
- employing adequate channels and methods for data collection
- investing in cutting-edge data collection and management tools
- conducting a thorough evaluation of all collected data
- constantly monitoring data quality
Final thoughts
Nowadays, businesses have more access to data than ever before, but this accessibility can be a double-edged sword of sorts that can turn from an advantage into a burden and even a hazard for many companies. The difference between success and failure often lies in how companies make use of the data and the strategies they employ in this respect. In the end, all signs point in the same direction: quality trumps quantity in the data-driven business realm of today.